Business, Sauder School of
Strategy and Business Economics, Division of
DSpace
UBCV
Baik, Min Kyeong
Doctor of Philosophy - PhD
University of British Columbia
This thesis is a collection of three empirical studies on competition and innovation. The essays study how firms respond to competitive shocks in different contexts and understand their strategic motives. The first essay examines the impact of mergers on innovation outcomes in the context of the pharmaceutical industry. By exploiting a novel dataset at the drug-disease level, the essay finds that mergers harm the innovation outcomes of the drug projects acquired in the deals. The existence of overlap in drug portfolios does not drive the negative impact of mergers. The negative effects of mergers are driven by their negative impact on the scientific process of drug development, not on commercialization activities. The results suggest that mergers harm innovation because of the organizational challenges faced by firms during the integration process rather than because of a lessening of competition. The second essay analyzes and quantifies the impact of weakened intellectual property rights on innovation using the pharmaceutical industry data. Starting in 1997, countries like South Africa and Brazil attempted to procure low-cost HIV drugs through compulsory licensing, triggering a controversial dispute between these countries and pharmaceutical companies. The essay uses this shock to the strength of patent rights as the basis of the identification strategy and applies the synthetic control method. The essay finds a large negative effect on the development of HIV drug therapies, suggesting a dynamic trade-off in using compulsory licensing to deal with public health crises. The third essay examines the impact of competition on price dispersion. The essay exploits the expansion of Southwest Airlines to capture potential and actual competition. When incumbent carriers face the risk of entry by Southwest Airlines, price dispersion declines in that route. The effect is driven by a sharp decrease in the 90th percentile of the price distribution, suggesting incumbents' motive to make entry less attractive. Incumbents cut down the 10th percentile of the price distribution after actual entry by Southwest Airlines, increasing price dispersion to its pre-entry threat levels. It is likely a strategic response of incumbents to avoid the loss of price-sensitive consumers.
Essays on Innovation and Industrial EconomicsbyMin Kyeong BaikB.A., Sungkyunkwan University, 2013M.A., Sungkyunkwan University, 2015A THESIS SUBMITTED IN PARTIAL FULFILLMENT OFTHE REQUIREMENTS FOR THE DEGREE OFDOCTOR OF PHILOSOPHYinThe Faculty of Graduate and Postdoctoral Studies(Business Administration - Strategy and Business Economics)THE UNIVERSITY OF BRITISH COLUMBIA(Vancouver)July 2023© Min Kyeong Baik, 2023The following individuals certify that they have read, and recommend to the Faculty ofGraduate and Postdoctoral Studies for acceptance, the thesis entitled:Essays on Innovation and Industrial Economicssubmitted by Min Kyeong Baik in partial fulfillment of the requirements for the degreeof DOCTOR OF PHILOSOPHY in Business Administration - Strategy and BusinessEconomics.Examining Committee:Guillermo Marshall, Associate Professor, Sauder School of Business, UBCSupervisorSanghoon Lee, Associate Professor, Sauder School of Business, UBCSupervisory Committee MemberEkaterina Khmelnitskaya, Assistant Professor, Sauder School of Business, UBCSupervisory Committee MemberThomas Davidoff, Associate Professor, Sauder School of Business, UBCUniversity ExaminerMargaret E. Slade, Professor Emeritus, Vancouver School of Economics, UBCUniversity ExaminerMo Xiao, Professor, Department of Economics, University of ArizonaExternal ExaminerAdditional Supervisory Committee Members:Jorge Lemus, Associate Professor, Department of Economics, University of Illinois atUrbana-ChampaignSupervisory Committee MemberiiAbstractThis thesis is a collection of three empirical studies on competition and innovation. Theessays study how firms respond to competitive shocks in different contexts and understandtheir strategic motives.The first essay examines the impact of mergers on innovation outcomes in the contextof the pharmaceutical industry. By exploiting a novel dataset at the drug-disease level, theessay finds that mergers harm the innovation outcomes of the drug projects acquired inthe deals. The existence of overlap in drug portfolios does not drive the negative impactof mergers. The negative effects of mergers are driven by their negative impact on thescientific process of drug development, not on commercialization activities. The resultssuggest that mergers harm innovation because of the organizational challenges faced byfirms during the integration process rather than because of a lessening of competition.The second essay analyzes and quantifies the impact of weakened intellectual propertyrights on innovation using the pharmaceutical industry data. Starting in 1997, countrieslike South Africa and Brazil attempted to procure low-cost HIV drugs through compulsorylicensing, triggering a controversial dispute between these countries and pharmaceuticalcompanies. The essay uses this shock to the strength of patent rights as the basis of theidentification strategy and applies the synthetic control method. The essay finds a largeiiinegative effect on the development of HIV drug therapies, suggesting a dynamic trade-offin using compulsory licensing to deal with public health crises.The third essay examines the impact of competition on price dispersion. The essayexploits the expansion of Southwest Airlines to capture potential and actual competition.When incumbent carriers face the risk of entry by Southwest Airlines, price dispersiondeclines in that route. The effect is driven by a sharp decrease in the 90th percentile ofthe price distribution, suggesting incumbents’ motive to make entry less attractive. Incum-bents cut down the 10th percentile of the price distribution after actual entry by SouthwestAirlines, increasing price dispersion to its pre-entry threat levels. It is likely a strategicresponse of incumbents to avoid the loss of price-sensitive consumers.ivLay SummaryPredicting the outcomes of mergers and the effect of changes in patent rights is crucialfor the pharmaceutical sector, where new medicines can save lives and improve life quality.Understanding how firms adjust their ticket prices is essential for the airline industry, wherethe variation in prices across passengers is substantial.The first essay concerns the development of drug projects acquired in the pharmaceuti-cal M&A deals. The second essay concerns the importance of intellectual property rights inthe innovative industry. The third essay studies the pricing decisions of the airlines facingthe risk of entry by low-cost rivals.This thesis contributes empirical evidence to the previous studies on competition andinnovation. It is informative for policymakers, as it is relevant for merger review, competi-tion policy, and patent protection.vPrefaceChapters two and four of this dissertation are original, unpublished, independent works bymyself. Chapter three is joint work with Professor Guillermo Marshall of the UBC SauderSchool of Business and Professor Jorge Lemus of the UIUC Department of Economics.viTable of ContentsAbstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iiiLay Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vPreface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiAcknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xivDedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Mergers, Research Synergies, and Innovation: Evidence from the Pharma-ceutical Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.2 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11vii2.3 Industry Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.4.1 Drug Development Data . . . . . . . . . . . . . . . . . . . . . . . 152.4.2 Drug Acquisition Data . . . . . . . . . . . . . . . . . . . . . . . . 192.4.3 Drug Originator Data . . . . . . . . . . . . . . . . . . . . . . . . . 192.5 Empirical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202.5.1 Effects of Acquisitions on Innovation Outcomes . . . . . . . . . . . 202.5.2 Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 242.5.3 Effects of Overlap on Innovation Outcomes . . . . . . . . . . . . . 242.5.4 Scientific versus Commercialization Activities . . . . . . . . . . . . 272.5.5 Strategic versus Nonstrategic Scientific Activities . . . . . . . . . . 292.5.6 Development Patterns Before Acquisitions . . . . . . . . . . . . . . 302.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323 The Impact of Weakened Patent Rights on Innovation: Evidence from HIV’sDrug Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 343.2 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383.3 Industry Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403.4 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 423.5 Empirical Framework and Results . . . . . . . . . . . . . . . . . . . . . . 443.5.1 Empirical Framework . . . . . . . . . . . . . . . . . . . . . . . . . 443.5.2 Constructing a Synthetic HIV Drug Therapy Industry . . . . . . . . 473.5.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493.6 Placebo Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513.6.1 In-Space Placebo Study . . . . . . . . . . . . . . . . . . . . . . . . 51viii3.6.2 In-Time Placebo Study . . . . . . . . . . . . . . . . . . . . . . . . 533.6.3 Placebo Study using Breast Cancer Therapy Industry . . . . . . . . 553.7 Leave-One-Out Robustness Test . . . . . . . . . . . . . . . . . . . . . . . 563.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584 Price Dispersion and Competition: Evidence from the Expansion of South-west Airlines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604.2 Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 634.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 654.4 Empirical Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 704.5 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724.5.1 Impact of Competition on Average Fares . . . . . . . . . . . . . . . 744.5.2 Impact of Competition on Price Dispersion . . . . . . . . . . . . . 754.5.3 Impact of Competition on 10th and 90th Percentile Fares . . . . . . 754.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 775 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81A Chapter 2 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87A.1 Drug Development Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 87A.1.1 BioMedTracker . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87A.1.2 Categorization of Event Types . . . . . . . . . . . . . . . . . . . . 89A.1.3 Drug Characteristics and Overlap Measures . . . . . . . . . . . . . 95A.1.4 Mergers and Acquisitions . . . . . . . . . . . . . . . . . . . . . . . 97A.1.5 Standardization of Company Names . . . . . . . . . . . . . . . . . 98ixA.2 Drug Acquisition Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98A.2.1 Refinitiv SDC Platinum . . . . . . . . . . . . . . . . . . . . . . . . 98A.2.2 S&P Capital IQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100A.3 Drug Originator Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100A.3.1 Springer Nature AdisInsight . . . . . . . . . . . . . . . . . . . . . 100A.4 Robustness Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101A.4.1 Alternative Overlap Indicators . . . . . . . . . . . . . . . . . . . . 101A.4.2 Alternative Data Frequencies . . . . . . . . . . . . . . . . . . . . . 103A.4.3 Inclusion of Time Period Fixed Effects . . . . . . . . . . . . . . . . 106A.4.4 Logistic Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 108A.5 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109A.5.1 Difference-in-Differences Event Study based on Yearly Data . . . . 109B Chapter 3 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111B.1 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111B.2 Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112B.2.1 In-Time Placebo Study with Alternative Placebo Treatment Periods 112C Chapter 4 Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113C.1 Robustness Check . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113C.1.1 Use of Alternative Price Dispersion Measures . . . . . . . . . . . . 113C.1.2 Inclusion of Control Variables . . . . . . . . . . . . . . . . . . . . 114C.2 Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121C.2.1 U.S. Airline Bankruptcies . . . . . . . . . . . . . . . . . . . . . . . 121C.2.2 Southwest Airlines Arrival History . . . . . . . . . . . . . . . . . . 122xList of TablesTable 2.1 Acquisitions and Drug Project Development . . . . . . . . . . . . . . . 22Table 2.2 Acquisitions and Drug Project Discontinuation . . . . . . . . . . . . . . 22Table 2.3 Project Overlap and Innovation Outcomes . . . . . . . . . . . . . . . . 27Table 2.4 Scientific versus Commercialization Activities . . . . . . . . . . . . . . 29Table 2.5 Strategic versus Nonstrategic Scientific Activities . . . . . . . . . . . . 30Table 2.6 Innovation Outcomes Before Acquisitions . . . . . . . . . . . . . . . . 31Table 3.1 Industry Weights in the Synthetic HIV Drug Therapy Industry . . . . . . 48Table 3.2 Predictor Means before Weakening of Patent Rights . . . . . . . . . . . 48Table 4.1 Expansion of Southwest Airlines . . . . . . . . . . . . . . . . . . . . . 69Table 4.2 Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Table 4.3 Estimation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73Table A.1 Categorization of Event Types . . . . . . . . . . . . . . . . . . . . . . . 91Table A.2 Strategic Motives in Scientific Activities . . . . . . . . . . . . . . . . . 95Table A.3 Reasons for Drug Project Discontinuation . . . . . . . . . . . . . . . . . 96Table A.4 SDC Platinum Industry Codes in All Biotechnology within High TechIndustry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99Table A.5 Drug Project Development: Alterative Overlap Measures . . . . . . . . 102xiTable A.6 Drug Project Discontinuation: Alterative Overlap Measures . . . . . . . 102Table A.7 Drug Project Development: Yearly Data . . . . . . . . . . . . . . . . . 104Table A.8 Drug Project Development: Monthly Data . . . . . . . . . . . . . . . . 105Table A.9 Drug Project Discontinuation: Yearly Data . . . . . . . . . . . . . . . . 106Table A.10 Drug Project Discontinuation: Monthly Data . . . . . . . . . . . . . . . 106Table A.11 Project Overlap and Innovation Outcomes: with Time Period Fixed Effects107Table A.12 Scientific versus Commercialization Activities: with Time Period FixedEffects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Table A.13 Strategic versus Nonstrategic Scientific Activities: with Time PeriodFixed Effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108Table A.14 Project Overlap and Innovation Outcomes: Logistic Regression . . . . . 109Table A.15 Scientific versus Commercialization Activities: Logistic Regression . . . 109Table A.16 Strategic versus Nonstrategic Scientific Activities: Logistic Regression . 110Table B.1 Adults and Children Living with HIV/AIDS in 1997 . . . . . . . . . . . 111Table C.1 Alternative Price Dispersion Measures . . . . . . . . . . . . . . . . . . 115Table C.2 Competition and Average Fare: Inclusion of Control Variables . . . . . . 116Table C.3 Competition and Log-Odds Ratio of Gini Coefficient: Inclusion of Con-trol Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Table C.4 Competition and Log of Gini Coefficient: Inclusion of Control Variables 118Table C.5 Competition and 10th Percentile Fare: Inclusion of Control Variables . . 119Table C.6 Competition and 90th Percentile Fare: Inclusion of Control Variables . . 120Table C.7 U.S. Airline Bankruptcies . . . . . . . . . . . . . . . . . . . . . . . . . 121Table C.8 Southwest Airlines Arrival History . . . . . . . . . . . . . . . . . . . . 122xiiList of FiguresFigure 2.1 Dynamics of the impact of acquisitions on innovation outcomes of ac-quired drugs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Figure 3.1 Trends in drug development: actual HIV drug therapy industry versussynthetic HIV drug therapy industry . . . . . . . . . . . . . . . . . . . 51Figure 3.2 In-space placebo study: estimated effect for the HIV drug therapy in-dustry and placebo effects for 88 industries in the donor pool . . . . . . 52Figure 3.3 In-time placebo study: estimated effects for placebo weakened intel-lectual property rights in April 1994 . . . . . . . . . . . . . . . . . . . 54Figure 3.4 Trends in drug development: actual breast cancer therapy industry ver-sus synthetic breast cancer therapy industry . . . . . . . . . . . . . . . 56Figure 3.5 Leave-one-out distribution of the synthetic HIV drug industry . . . . . 58Figure A.1 Dynamics of the impact of acquisitions on innovation outcomes of ac-quired drugs using yearly data . . . . . . . . . . . . . . . . . . . . . . 110Figure B.1 In-time placebo study with alternative placebo treatment periods . . . . 112xiiiAcknowledgmentsFirst and foremost, I would like to express my deepest gratitude to my advisor, ProfessorGuillermo Marshall, for the continuous support of my Ph.D. study and research, for hispatience, motivation, and immense knowledge. His guidance helped me in all the time ofresearch and writing of this thesis. I could not have imagined having a better mentor formy Ph.D. study. This endeavor would not have been possible without my thesis committee,who generously provided knowledge and expertise. I am indebted to Professor SanghoonLee for his feedback and insightful comments on my thesis and presentation. I am gratefulto Professor Ekaterina Khmelnitskaya for her encouragement and sharing of knowledgeabout research interests in common. I would like to extend my sincere thanks to ProfessorJorge Lemus for his support and practical advice.To continue, I would like to thank Professors Werner Antweiler, James Brander, ThomasDavidoff, Limin Fang, Keith Head, Isaac Holloway, Ken Kikkawa, Scott Orr, A´lvaro Parra,John Ries, Tom Ross, and all the other faculty members in the Strategy and Business Eco-nomics division who have provided generous support during my doctoral study. SpecialThanks to Elaine Cho for her responsiveness and help. I thank my friends and colleaguesat the Sauder School of Business: Rui, Minjia, Sepide, Nargess, Jaecheol, Xiaoke, Jun-dong, Zining, Siying, Saattvic, Nicolas, Nan, for walking along the journey and for all thexivfun we have had.Finally, my sincere thanks should also go to my parents, Sungbai Baik and MyeongbuLee, for their everlasting love, support, and encouragement throughout my life. Thanks tomy dearest friends: Heekyung, Jiyoung, and Milae, for being by my side. Their belief inme has kept my spirits and motivation high.xvDedicationI dedicate my dissertation to my loving parents, Sungbai Baik and Myeongbu Lee.xviChapter 1IntroductionThis thesis is a collection of three empirical studies on competition, innovation, intellectualproperty, and pricing strategies. The essays in this thesis aim to examine how firms respondto competitive shocks in different contexts and understand their strategic motives.The first essay studies the impact of mergers on innovation outcomes in the contextof the pharmaceutical industry. By exploiting a novel dataset with detailed informationabout drug development and merger activity, this essay finds that mergers harm innova-tion because of the organizational challenges faced by firms during the integration processrather than because of a lessening of competition. The second essay studies the impact ofweakened intellectual property rights on innovation, with a focus on the HIV drug therapyindustry. Using the issuance of compulsory licenses in 1997 as the basis of our identifi-cation strategy, this essay finds a significant negative impact of weakened patent rights onthe development of HIV drugs. The third essay studies the effect of competitive threatson incumbents’ price dispersion in the context of the airline industry. By exploiting theexpansion of Southwest Airlines, this essay finds that competition can both increase and1decrease price dispersion depending on the timing and strategic motive. Three essays con-tribute empirical evidence to the literature and are informative to policymakers.The first two essays study pharmaceutical drug development, a crucial innovation ac-tivity that benefits society by improving public health and quality of life. The two essaysaddress the questions relevant to two crucial aspects of the innovative industry, competitionand patent protection. Developing and launching a new drug is a costly and lengthy processwith a low probability of success. Pharmaceutical companies spend billions of dollars onresearch and development of new medicines—ranging from $314 million to $2.8 billion,according to recent estimates. It takes several years—about 10 to 15 years—for a new drugto reach the market. Mergers and acquisitions have been increasing in the pharmaceuticalindustry, varying the consolidating firms from small-sized biotech companies to middle-sized or big companies. Pharmaceutical mergers have played a prominent role in the waveof market concentration, accounting for some of the largest mergers of the last decade.Predicting the outcomes of mergers and examining the role of patents are important forthe pharmaceutical sector, where new and affordable medicines can even save the lives ofpeople.What is the impact of mergers on innovation outcomes? The first essay addresses thisquestion by exploiting a novel dataset on pharmaceutical drug development. I make use ofmultiple databases to construct drug–disease level panel data, which tracks the developmentprocess and ownership changes of drug projects for the 1985 to 2020 period. Using theinformation about the occurrence and timing of the series of regimented procedures a drugcandidate goes through ultimately to reach the public (which include preclinical research,clinical trials, and commercialization activities), innovation is measured as the occurrenceof development (discontinuation) events in a given quarter.2By estimating a difference-in-differences model, I find that mergers negatively impactthe innovation outcomes of drug projects acquired in the deals. I estimate an event studymodel to evaluate the existence of pre-trends before the treatment periods. I revisit the ideaof Cunningham et al. (2021) by estimating a triple differences model, including an overlapindicator that captures the scientific closeness of drug portfolios of the merging firms. Ifind that the existence of overlap does not drive the negative impact of mergers, suggestingthat a lessening of competition within a narrowly defined market is not driving the results. Iunpack the result by studying whether the negative impact of mergers is heterogeneous forthe scientific and commercialization process of drug development. I find that the negativeeffects of mergers are driven by their negative impact on the scientific process of drugdevelopment, not on commercialization activities. The results suggest that mergers harminnovation because of the organizational challenges faced by firms during the integrationprocess rather than because of a lessening of competition.The first essay contributes empirical evidence to the literature on the relationship be-tween competition and innovation, where the findings are mixed depending on the settingsor industries. Some authors find the negative effect of mergers on innovation, whereas otherauthors show the opposite results. Another strand of literature studies the effect of the pre-merger overlap on post-merger innovation, finding both positive and negative effects.The second essay studies another crucial aspect of pharmaceutical drug development,the relationship between intellectual property rights and innovation. Pharmaceutical com-panies rely on drug patents to recoup R&D costs and preclude infringement. Patents enablepharmaceutical firms to set high prices for their drugs, which incentivizes them to developdrug therapies that can save lives. However, those high prices create an inefficiency aspatients in need of a drug may be unable to afford it, possibly causing preventable deaths.To tackle the public health crisis, countries use compulsory licensing, a legislative measure3stipulated in the TRIPS agreement of 1995. The second essay analyzes and quantifies theimpact of weakened intellectual property rights via compulsory licensing on innovation.After the first approval of HIV medicine in 1987, more drugs became available in the1990s but were sold at high prices. Starting in 1997, countries like South Africa and Brazilattempted to procure low-cost HIV drugs through compulsory licensing, triggering con-troversial disputes and lawsuits between these countries and pharmaceutical firms. Thisessay uses this shock to the strength of patent rights in the HIV drug therapy industry as thebasis of the identification strategy. For the empirical analysis, the essay makes use of mul-tiple databases to construct disease–year–month level data and apply the synthetic controlmethod.The second essay estimates how the threat of compulsory licensing changed the tra-jectory of the drug development of HIV. The “synthetic” HIV drug therapy industry isconstructed as a weighted average of other industries chosen to resemble the values of co-variates and outcomes of the HIV drug therapy industry before the threat of compulsorylicensing. This essay finds that the weakening of patent rights caused a large reduction inthe number of drug therapies under development. The significance and credibility of theestimates are confirmed by a series of placebo studies. The results are robust to changes inthe synthetic weights.The second essay contributes to the literature that has explored different aspects of therelationship between intellectual property, innovation, and welfare. The results suggestthere is a trade-off in using compulsory licensing to deal with public health crises. Al-though such policies can save lives and therefore have great benefits in the short run, ourresults suggest that they are costly in that they may discourage the development of newdrug therapies that could save more lives in the future.4Finally, the third essay studies the impact of competition on price dispersion. How doincumbents adjust their prices in response to competitive threats raised by the expansionof their rivals? The third essay empirically addresses these questions in the context of theairline industry. Price dispersion is a feature of the airline industry (Borenstein and Rose,1994). An air carrier charges different prices to passengers in the same market, and thevariation in prices is substantial.For the empirical analysis, I make use of the airline industry data with airline ticketprices and detailed itinerary information for the 1993 to 2016 period. I exploit the ex-pansion of Southwest Airlines in the last two decades to capture the potential and actualcompetition (Goolsbee and Syverson, 2008). An entry threat is identified as the periodwhen Southwest Airlines established its presence in both endpoints of a given route with-out serving the route. An actual entry is identified as the period when Southwest Airlinesstart operating direct flights on that route. I estimate a two-way fixed effects model withtime dummies like a standard event study framework. A key identification assumption ofthe event study analysis is that there are no other confounding events occurring at the sametime as the event of our interest taking place.I find that when incumbents face the risk of entry by Southwest Airlines, price disper-sion declines in that route. The result is driven by a sharp decrease in the 90th percentileof the price distribution, suggesting that the incumbent airlines cut prices to make entryless attractive for Southwest Airlines. Incumbents cut down the 10th percentile of the pricedistribution after actual entry, increasing price dispersion to its pre-entry threat levels. Itis likely a strategic response of incumbents to avoid the loss of price-sensitive consumersto Southwest Airlines after its entry. The results are robust to different dispersion mea-sures and the addition of control variables. Competition can increase and decrease pricedispersion; thus, timing and strategic motives are essential to understand the relationship5between competition and price dispersion. This essay contributes empirical evidence to thedebate on the relationship between competition and price dispersion, where the findings oftheoretical and empirical literature are mixed.6Chapter 2Mergers, Research Synergies, andInnovation: Evidence from thePharmaceutical Industry2.1 IntroductionDeveloping new medicines is a crucial innovation activity that benefits society by improv-ing public health and quality of life. Inventing a successful new drug product is hard, andit involves incurring extraordinarily high R&D costs and a lengthy development process.What is the impact of mergers on innovation outcomes? On the one hand, firms may usemergers and acquisitions to expand their product portfolio and obtain the knowledge andknow-how of the acquired firms, exploiting research synergies and increasing the chancesof a drug reaching the market. On the other hand, firms may acquire firms developingsimilar drug projects (or acquire the projects) to remove their rivals and soften competi-7tion. Whether mergers and acquisitions improve innovation outcomes is thus an empiricalquestion.This essay empirically studies the impact of mergers on innovation outcomes of ac-quired drug projects and whether/how the effect differs depending on the existence of anoverlap between the acquired drug project and projects in the acquiring firm’s portfolio.The main finding of this essay is that mergers are harmful to innovation outcomes of thedrugs acquired in the deals. Among the outcomes that suffer the most after a merger is theclinical development of these drugs (e.g., initiation, progress, and completion of clinical tri-als). Unlike prior research (Cunningham et al., 2021), I find that mergers and acquisitionsfeaturing overlapping drug projects are not worse in their impacts on innovation.For the empirical analyses, I construct a drug development panel dataset by combin-ing a pharmaceutical drug database with an acquisition database and a drug originatordatabase. BioMedTracker, which is key for the purpose of this essay, is a comprehen-sive database that tracks the development milestones of drug projects from the clinicaldevelopment stage through the commercialization and regulatory process. Besides the de-velopment process, it documents the occurrence and timing of mergers and acquisitions inwhich each drug project is involved. The data also records drug-specific attributes such aschemical or generic drug names and biological targets. The data allows me to measure theinnovation of drugs in development between 1985 and 2020 and the overlap between thedrugs in the scientific space.The innovation outcome is measured by the occurrence of a development (or discontin-uation) event or activity in a given period. BioMedTracker uses detailed event types to trackdrug projects. By exploiting the comprehensiveness of the event types, I categorize theminto development, discontinuation, and neutral events. The development events impart the8progress of drug development in both science- and commercialization-related processes. Ifurther categorize science-related events and activities into ones that have a strategic nature(e.g., the announcement of plans to initiate the new clinical studies, the initiation of thepatent enrollment and dosing) and ones that are merely scientific. The dependent variablesfor the empirical analyses are constructed based on the above categorization. Specifically,the dependent variables are dummy variables indicating whether a drug project has a spe-cific event in a given quarter.The overlap between the acquired drug project and the projects in the acquiring firm’sportfolio is measured based on the drug’s intended diseases and biological targets. Ac-cording to medical terminology, the intended disease refers to a medical condition that adrug is used for (e.g., solid oncology), and the biological target refers to a molecule in thebody affected by a drug resulting in a desired therapeutic effect (e.g., androgen receptors).Specifically, an acquisition is tagged with the overlap indicator if the acquired drug projectis developed for the same medical condition and uses the same biological target as a projectin the acquiring firm’s portfolio. This overlap measure can capture the scientific similarityand potential substitutability between the drug projects. In addition to the main overlap in-dicator, I use four alternative measures to examine if the results hold if the overlap betweenthe projects is defined based on more lenient criteria.BioMedTracker updates the record of a drug project with the name of the current devel-oper when a merger occurs. In order to examine the overlap between the drug projects, itis necessary to identify the developer of each drug at each point in time, especially aroundthe time of the mergers. However, BioMedTracker does not report the target and acquiringfirms involved in the M&A deals and the originator firm of each drug project. To supple-ment the information, I combine the acquisition and drug originator data. The acquisitiondata is collected from SDC Platinum, and the drug originator data is collected from AdisIn-9sight.I estimate a difference-in-differences model to examine the effect of mergers on theinnovation outcomes of the acquired drugs. I include fixed effects to control for unobservedpotential confounding effects that cannot be captured by the explanatory variables. Next, Iestimate a triple differences model, where I use the overlap indicator to compare the drugprojects acquired by firms with overlapping projects to those acquired by firms with no suchprojects and to non-acquired drug projects. The identification assumption in the difference-in-differences analysis is that the development activities of drug projects acquired in thedeals would have followed the same trend as those of drug projects that are never acquiredover the course of development. I estimate an event study model that includes leads andlags of the period of the indicator for acquisitions, finding that acquired and non-acquireddrug projects before treatment are statistically the same.The estimation results suggest that mergers and acquisitions negatively affect the in-novation outcomes of the acquired drug projects. Acquired drug projects are less likelyto have a development event and more likely to have a discontinuation event compared tonon-acquired drug projects. The negative effects of mergers and acquisitions are driven bythe decline in science-related development activities rather than commercialization-relatedones. Moreover, the drug projects acquired by firms with overlapping projects in their port-folios are more likely to have development events after the acquisition than those acquiredby firms with no overlapping projects. The patterns of the results remain the same whenI look at the effects on the development events that are purely scientific. These resultssuggest that the R&D department or the clinical pharmaceutical scientists in the R&D de-partment may suffer from the consolidation process, suggesting an alternative explanationof the anti-competitive motive of mergers studies in Cunningham et al. (2021).10This essay contributes to the empirical literature that examines the impact of mergers oninnovation, with mixed results (Haucap et al., 2019; Entezarkheir and Moshiri, 2018; Dan-zon et al., 2007; Grabowski and Kyle, 2008; Comanor and Scherer, 2013; Ornaghi, 2009).This essay also contributes to the literature that examines the effect of similarity betweenfirms on merger outcomes (Cunningham et al., 2021; Ornaghi, 2009; BENA and LI, 2014;Ahuja and Katila, 2001; Bonaime and Wang, 2019; Kang, 2019). More broadly, the papercontributes to the broader discussion of the association between competition and innova-tion, which goes back to the theoretical work of Schumpeter (1942) and Arrow (1962).2.2 LiteratureThe debate over the relationship between mergers and innovation has its roots in the broaderdiscussion of the effect of competition on innovation, which goes back to the theoreticalwork of Schumpeter (1942) and Arrow (1962). Schumpeter (1942) argues that less com-petition enhances innovation. The innovator continues to invest and innovate to maintainits market lead. Arrow (1962) argues that competition enhances innovation, as firms un-der competitive pressure will strive to innovate to outperform competitors and escape thecompetition. The validity of the theory is the subject of a vast strand of theoretical andempirical literature.The findings of empirical studies on the effects of mergers on innovation activity andR&D productivity are mixed. Ornaghi (2009) and Comanor and Scherer (2013) find thenegative effect of mergers on innovation. Haucap et al. (2019) show that mergers hurtinnovation not only for the consolidating firms but also for the competitors in the mar-ket. In contrast, Ringel and Choy (2017) show that large pharmaceutical mergers result insignificant improvement of R&D productivity and Grabowski and Kyle (2008) find posi-11tive effects on innovation outcomes at later stages from acquisitions of mostly small firms.Maksimovic and Phillips (2001) show a positive correlation between mergers and firms’innovation using U.S. manufacturing industry data.The findings in the literature on the effect of the pre-merger overlap on post-mergerinnovation outcomes are diverse. BENA and LI (2014) shows the positive effect of pre-merger technological overlap on future innovation. Kang (2019) find that technologicalsimilarity between merger partners and technology diversity of individual firms have posi-tive impacts on merger value creation, using the data from the U.S. ICT industry. Ornaghi(2009) shows that higher levels of technological relatedness between the merged firms arenot associated with better R&D outcomes. Cunningham et al. (2021) examines the strate-gic motive of the acquirers to kill the target’s projects to preempt future product marketcompetition, being able to access the BioMedTracker database enables the empirical anal-ysis examining how the mergers and acquisitions and the similarities in merging parties’product portfolio impact the innovation performances.2.3 Industry BackgroundA drug candidate has to go through regimented procedures verifying their safety and effec-tiveness before they reach the public. Each step is closely monitored by the governmenthealth authority (e.g., the U.S. Food and Drug Administration and the European MedicinesAgency) throughout the process of discovery, preclinical research, clinical trials, and com-mercialization. The general development process for drugs seeking U.S. market approvalis briefly summarized below, similarly applied to drugs in other countries.The drug development process typically starts with discovering a molecular or bio-logical compound and identifying diseases it may have a therapeutic effect. Once a drug12candidate and an intended disease are identified, preclinical research is conducted to findout whether it has the potential to cause serious harm before it is tested in people.1 Af-ter the drug candidate passes the safety review, it goes through multiple clinical trials. Inthe U.S., firms must submit the Investigation of New Drugs (IND) application to requestauthorization from the FDA to initiate clinical trials.In Phase I clinical study, the drug candidate is administered to a small number of partic-ipants (20 to 100 people) to test the safety and efficacy of various dosages. It takes severalmonths, and approximately 70% of the drugs proceed to the next phase. The drug candi-date is more carefully tested in Phase II, administering the drug to more participants thanin Phase I (up to several hundred people) to obtain additional safety data. The study takesseveral months to 2 years, and approximately 33% of the drug can move to the next phase.The efficacy and long-term effects are rigorously tested in Phase III. The drug candidate isadministered to several hundred to mid-thousand participants. The studies are conductedfor 1 to 4 years, enabling researchers to monitor adverse reactions and detect side effectsthe drugs might have in the long term. Approximately 25-30% of drugs move to the nextstage. When the results of Phase III clinical trials are favorable, the drug developer filesa New Drug Application (NDA) to demonstrate that a drug is safe and effective for its in-tended use and get market approval. It includes everything about the drug candidate – alldata and reports from preclinical research through to Phase III clinical trial. In the case ofa biological product, the developer files a Biologics License Application (BLA). The FDAthoroughly reviews the applications and decides whether to approve the drug.Over the course of the scientific development of the drug candidates (i.e., early-stage1There are two types of preclinical research. One is in vitro, which refers to medical procedures, tests,and experiments performed outside of a living organism in a controlled environment, such as a petri dish ortest tube. The other one is in vivo, which refers to procedures performed in a whole living organism, such asa laboratory animal or person.13discovery and preclinical research and clinical trials), firms also engage in commercializa-tion activities, including patent filing, submitting drug applications, and requesting drugdesignations. As explained above, the IND application requests authorization for clini-cal trials and NDA and BLA request market approval in the United States. Additionally,firms submit an Abbreviated New Drug Application (ANDA) for review and approval ofa generic drug product. A Supplemental New Drug Application (sNDA) or SupplementalBiologics License Application (sBLA) is for making major changes in a product after ithas been approved.2 Firms also request drug designations from regulatory authorities toobtain benefits and expedite the FDA review and approval process. The drug designationsinclude orphan drug designation (granted to drugs intended to treat rare diseases), fast trackdesignation (granted to drugs that treat serious life-threatening conditions), breakthroughtherapy designation (given to drugs that demonstrate substantial improvement over exist-ing therapies), and priority review designation (given to drugs that faster evaluation ofapplications would lead to significant improvements in the safety or effectiveness of thetreatment). Firms also file patent applications to protect intellectual property rights overinnovative drugs or technologies.In short, a drug is approved if the government health authority concludes that its healthbenefits outweigh its known risks. However, the paths to approval and market launch arelengthy and costly. The process takes several years (usually 10 to 15 years) for a drugto reach the market. The average cost of developing a new drug has been the subject ofdebate, with recent estimates ranging from $314 million to $2.8 billion. According to themost widely cited studies, $2.6 billion is needed for a new drug to reach the market, whichincludes the amount spent on preclinical research and clinical trials, sunk costs on studiesthat ultimately fail, and inflation, considering the time required to commercialize the drug2A firm files sNDA or sBLA to expand a drug label to include a new usage, change the way it manufacturesa drug, or market a new dosage or strength.14(DiMasi et al., 2016a; Wouters et al., 2020).2.4 DataThe analysis in this essay makes use of three databases. I combine the drug developmentdata from BioMedTracker with acquisition data extracted from SDC Platinum and drugoriginator data collected from AdisInsight.2.4.1 Drug Development DataBioMedTracker is a comprehensive database that tracks the clinical development and com-mercialization process of drug projects, along with the firm associated with each project.Besides the development process, it tracks the mergers and acquisitions and firm namechanges in which the drug projects are involved. The raw data covers the events recordedfrom January 1985 to early June 2020. The data also records drug-specific attributes suchas chemical or generic drug names, biological targets, molecule types, routes of administra-tion, and FDA drug classifications. The information contained in the database is collectedfrom publicly available sources such as company press releases, medical conference pre-sentations, public company SEC filings, company website materials, and company slidepresentations.3Importantly for the purpose of this essay, for each drug project, BioMedTracker docu-ments the occurrence and timing of milestone events using 271 event types. By referringto the FDA and EMA website materials, I carefully categorize the event types into threegroups: development, discontinuation, and neutral event. As it can be conjectured from theterms, the events or activities indicating the progress of drug development are categorized3https://www.biomedtracker.com/Standards.cfm15as development events (e.g., “trial announcement”, “trial completed”, “NDA/BLA filing”,“product launch” and “orphan drug designation”), and those indicating suspension of drugdevelopment are categorized as discontinuation events (e.g., “trial/enrollment suspension”,“NDA/BLA withdrawal”, “program hold” and “suspension”). Neutral events include thedecisions made by the regulatory authority and the activities that convey little or no in-formation on the progress or discontinuation of drug development (e.g., “clinical hold”,“clinical hold lifted”, “approval”, and “complete response letter”). The development eventsreflect the progress of drug development in both science and commercialization-related as-pects. By exploiting the comprehensiveness of the event types tracked by BioMedTracker,I further categorize the science-related development events into the ones that convey thefirm’s strategic motive and that are merely scientific. Based on the categorization, I con-struct the dependent variable for the empirical analyses, the dummy variable indicating theoccurrence of a development (or discontinuation) event in a given quarter.If a drug project seeks to verify its therapeutic effect for more than one disease, thedevelopment process is separately tracked at each disease level. For such cases, BioMed-Tracker assigns a unique identifier to each drug-disease pair separating one from the other.For example, Rituxan is a drug used to treat a specific type of non-Hodgkin’s lymphomaand is also used for rheumatoid arthritis. Rituxan in non-Hodgkin’s lymphoma (in oncology– hematologic) follows a development path different from Rituxan in rheumatoid arthritis(in autoimmune/immunology – arthritis). So the data provides information on the periodin which a drug project is in development for each disease. This allows me to measurethe overlap between the drug projects based on the pool of diseases for which they weredeveloped around the time of mergers.The overlap between the acquired drug project and the projects in the acquiring firm’sportfolio is measured based on their intended disease and biological target. According to16medical terminology, an intended disease is a medical condition for which a drug is used(e.g., solid oncology). A biological target refers to a molecule in the body affected by adrug resulting in a desired therapeutic effect (e.g., androgen receptors). If the acquired drugproject is developed for the same medical condition and uses the same biological target asa project in the acquiring firm’s portfolio, an acquisition is tagged with the overlap indica-tor. This overlap measure can capture the scientific similarity and potential substitutabilitybetween the drug projects.Besides the development milestone events, BioMedTracker documents the occurrenceand timing of mergers and acquisitions in which drug projects are involved. More specifi-cally, the drug projects subject to acquisitions are tagged with the acquisition-related eventtypes (i.e., “acquisition announcement”, “acquisition closed”, and “drug acquisition”)4 andthe dates when the events happened. When a merger occurs, BioMedTracker updates therecord of a drug project with the name of the merged firm. However, it does not recordthe target and acquiring firms involved in the deal. For example, Genentech Inc. origi-nally developed Rituxan. Later, Roche Holdings AG acquired the firm in March 2009. Inthis case, BioMedTracker records Rituxan with Roche and does not keep the record thatGenentech is the developer of Rituxan before the merger. To measure the overlap betweenthe acquired drug project and projects in the acquirer’s project portfolio, it is necessary toidentify which of the merging firms developed each project prior to the merger as well asthe firm that originated each project.5 This is ultimately to identify the developer of eachdrug project at each point in time, especially before and after the acquisitions. To do so, I4If a target firm itself is acquired, then BioMedTracker records the event with the event type of “acquisitionannouncement,” or “acquisition closed” depending on the announcement. If only a drug pipeline is subjectto an acquisition, then the event is tracked with the event type of “drug acquisition”. One thing to note is thatthe firm acquisition events are often erroneously recorded as the drug acquisition events, and vice versa.5When a firm terminates the development of a drug project before a merger, BiMedTracker does nottag the drug with an acquisition event but still updates the record with the name of the merged firm. Toavoid erroneously identifying the current developer as the originator for these drugs, I need to find the trueoriginator of each drug project.17rely on the acquisition data and drug originator data.Similarly to the acquisition events described above, when a firm changes its name,BioMedTracker also updates the records of the associated drug projects with the new firmname. BioMedTracker does not provide information on the old firm name. As a part of thedata-cleaning process of identifying the developer of each drug project at each point in time,I manually identify the old and new names for 237 company name change events in theraw BioMedTracker data. For the manual check, I refer to the company press release, SECfilings, and website materials. The company description variable in the raw BioMedTrackerdata is also used as a supplementary source.The drug projects are excluded from the sample if their ownership change history isunclear because of one of the following reasons: (1) a drug project is tagged with an ac-quisition event, but no matched transaction is found in the acquisition data (2) the targetor acquiring firm is recorded as an unknown entity (3) more than one firm is listed as thetarget or acquiring firm (4) the matched transaction is not aligned with the drug originatorinformation. I also drop drug projects if they are involved in a merger or reverse mergertransactions. The drug projects acquired more than once are excluded from the analyses.The drug projects are included in the dataset from when their development information isfirst recorded until they are launched in the product market.6The constructed dataset is, by nature, an unbalanced panel at the drug-time period level.After the data cleaning process, 8,990 drug projects remain in the data. Among them,821 projects are acquired once, and 128 projects are acquired by firms with overlapping6I drop the observations after their first launch in the U.S. market. Once a drug is launched in the productmarket, the firms have less incentive to continue developing it to expand their approved indications. The firmsare unwilling to conduct a series of clinical trials and follow the regulatory procedures they already did forthe previously approved indication. Also, expanded approval is not strictly required for the use of drugs bymedical practitioners, as the approved and launched drugs can still be used for unapproved indications underthe doctor’s judgment (“off-label” or unapproved use of approved drugs).18projects. As I exclude the drug projects acquired more than once from the sample, thedrugs in the treatment group are treated once but in different periods. In the main analyses,I aggregate the data at the quarter level from 1985 to 2020.2.4.2 Drug Acquisition DataThe acquisition data is collected from Refinitiv SDC Platinum. For each deal, it documentsthe target and acquiring firms, announcement and closed dates (or withdrawn dates if it iscanceled), and a brief description of how it is processed. I extract the data prescreenedto the ones in which the target firms operating in the US and non-US countries, target oracquiring firm is in the biotechnology industry,7 and announced between 1962 and 2020.After cleaning and standardizing the company names, I combine the acquisition data withthe BioMedTracker data through a fuzzy matching algorithm and manual check.S&P Capital IQ’s M&A Transaction Screening data is collected and used to supplementSDC Platinum, especially when no matched deal is found in SDC Platinum. I extract thedata prescreened to the deals with the target or acquiring firms in the healthcare industrythat were announced between 1967 and 2020.82.4.3 Drug Originator DataSince the raw BioMedTracker data does not record who initially started to develop thedrug projects, I need to reconstruct the true originator firm information. To do so, I web-scraped the publicly available information on Springer Nature’s AdisInsight website andconstructed a dataset containing the primary and alternative names of the drug projects7The industry classifications are listed in Table A.48Instead of combining the two datasets into a single acquisition data, I use it as a supplementary source inthe manual check because the firm recorded in the data is messy.19and their originators and developers.9 AdisInsight is an integrated database that tracksand provides drug development worldwide through the entire development process, fromdiscovery, through pre-clinical and clinical studies to market launch and beyond.10 Theoriginator refers to the institution that AdisInsight reviewers concluded originally inventedor discovered the active ingredient of a given drug, and the developers refer to institutionsthat are involved in conducting, funding, or supporting the clinical development of the drug.One challenge in reconstructing the true originator of the drug projects is to match thedrug names in BioMedTracker and AdisInsight. For each drug project, BioMedTrackerrecords its primary name (mostly the commercial name) as well as the alternative names,such as chemical or generic names. The drug originator data also reports all alternativenames assigned to each project. Thus, the list of all assigned names in both data should beused in the matching process, which requires more time and effort for manual checks.2.5 Empirical Analysis2.5.1 Effects of Acquisitions on Innovation OutcomesTo analyze how the development activities of the acquired drugs are affected by mergersand acquisitions in the post-acquisition periods, I estimate the difference-in-differencesmodel given as:Yit = β1×Acquisitioni ·Postit +α× (Fixed Effects)+ εit , (2.1)9I use the Python Selenium package to save publicly available information in the “Preview” section of thereports available from https://adisinsight.springer.com. The other publicly available information includes thedrug class, mechanism of action, and orphan drug designation. Data on 50,964 drug projects is scraped.10AdisInsight Report published in the Drugs journal.20separately for Y ∈ {Development,Discontinuation}. Developmentit and Discontinuationitare dummy variables indicating whether a drug project has a development event and dis-continuation event in period t, respectively. The independent variables are also dummyvariables such that Acquisitioni takes the value of one if drug i is acquired, and Postit takesthe value of one in the post-acquisition periods. I include fixed effects to control for the un-observed potential confounding effects that cannot be captured by the explanatory variables(e.g., drug fixed effects, disease group and target fixed effects, and initialization period fixedeffects). The model is estimated using a linear probability model, and the standard errorsare clustered at the drug project level. Since the dependent variable is a dummy variable,the coefficient β1 on interaction term, Acquisitioni ·Postit , measures the percentage pointchange in the probability that the acquired drug projects are developed (or discontinued)after acquisitions, compared to the non-acquired drug projects.The identification assumption in the difference-in-differences analysis is that the de-velopment activities of drug projects acquired in the deals would have followed the sametrend as those of drug projects in the control group (that are never acquired over the courseof development). In the following section, I estimate an event study model that includesleads and lags of the period of the indicator for acquisitions. The finding that the treatedand control groups were not statistically different in the pre-acquisition periods supportsthe identification assumption.Table 2.1 and Table 2.2 present the estimation results from (2.1). Since I include thedrug fixed effects, the Acquisitioni term is omitted. Table 2.1 presents the results for adrug project having a development event. In column 1 of Table 2.1, I include the drugfixed effects and the initialization period fixed effects to absorb variation from unobserv-able drug-specific characteristics and focus on the development of drug projects initiated inthe same period. The estimate of β1 is -0.0823 and statistically significant at the 1% level,21Table 2.1: Acquisitions and Drug Project DevelopmentHaving a development event(1) (2) (3) (4)Acquisition×Post -0.0823∗∗∗ -0.0838∗∗∗ -0.0838∗∗∗ -0.0823∗∗∗(0.0118) (0.0122) (0.0123) (0.0118)N 166,516 158,075 158,075 166,516R2 0.218 0.216 0.216 0.218Drug FE Y Y Y YInitialization Period FE Y YDisease×Target FE Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001Table 2.2: Acquisitions and Drug Project DiscontinuationHaving a discontinuation event(1) (2) (3) (4)Acquisition×Post 0.0263∗∗∗ 0.0264∗∗∗ 0.0264∗∗∗ 0.0263∗∗∗(0.00295) (0.00306) (0.00306) (0.00295)N 166,516 158,075 158,075 166,516R2 0.068 0.064 0.064 0.068Drug FE Y Y Y YInitialization Period FE Y YDisease×Target FE Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.00122which means that the acquired drug projects are 8.23 percentage points less likely to have adevelopment event in the post-acquisition periods than the non-acquired drug projects. Thecoefficient estimates are similar in column 2, where I include the disease group–biologicaltarget fixed effects and drug fixed effects. The inclusion of the disease group–biological tar-get fixed effects is to control for potential heterogeneities in the development process of thedrugs targeting different medical conditions and using different scientific technologies. Thecoefficient estimate in column 2 implies that the acquired drug projects are 8.38 percentagepoints less likely to have a development event in the post-acquisition periods compared tothe non-acquired drug projects. The coefficient estimates are similar in columns 3 and 4,where I include all three fixed effects and drug fixed effects only.Similarly, Table 2.2 presents the estimation results for a drug project having a discontin-uation event, obtained by including different combinations of fixed effects. In column 1 ofTable 2.2, the estimate of β1 is 0.0263 and statistically significant at the 1% level, meaningthat the acquired drug projects are 2.63 percentage points more likely to have a discontin-uation event in the post-acquisition periods compared to the non-acquired drug projects.The coefficient estimates are similar and all significant in columns 2 to 4, where I controlfor different unobservable drug-specific characteristics and potential heterogeneities.The results from the baseline difference-in-differences model imply that mergers andacquisitions have negative impacts on the innovation outcomes of the acquired drug projectsin both directions: the development process of the drug projects is less likely to be contin-ued and is more likely to be discontinued after the acquisitions, compared to the non-acquired drug projects.232.5.2 IdentificationI next explore dynamic effects using the following dynamic difference-in-differences model.Yit = α+−2∑k=t0βk×Acquisitionik +t1∑k=0βk×Acquisitionik +φi +ηt + εit , (2.2)where Acquisitionik is a dummy variable equals one in k quarter before acquisition if k isnegative and k quarter after acquisition for if k is positive, and zero for otherwise. And itis zero for all never acquired drugs. t0 and t1 are the lowest and highest number of leadsand lags surrounding the acquisition period, respectively. φi and ηt are drug and time fixedeffects. Standard errors are clustered at the drug project level. The coefficient for onequarter before the acquisition is normalized to zero.I do this for two reasons. First, to explore whether the effects change over time, andsecond, to evaluate the existence of pre-trends prior to treatment. Figure 2.1 visualizes thecoefficient estimates from the difference-in-differences event study. The results suggest nostatistically significant difference between acquired and non-acquired drugs before treat-ment. The results support the identification assumption of the difference-in-differencesanalyses that the development (or discontinuation) activities of acquired drug projectswould have followed the same trend as those of non-acquired projects.2.5.3 Effects of Overlap on Innovation OutcomesAs explained in the previous section, I define the overlap between the acquired drug projectand the projects in the acquiring firm’s portfolio based on their intended disease and bio-logical target. With the overlap indicator, I estimate the following specification to examinewhether/how the acquisitions by firms with overlapping projects differently affect the in-24-.1-.050.05.1-7 quarters Acquisition +8 quarters(a) Development events-.04-.020.02.04.06-7 quarters Acquisition +8 quarters(b) Discontinuation eventsFigure 2.1: Dynamics of the impact of acquisitions on innovation outcomes of ac-quired drugsnovation outcome of the acquired projects:Yit = β1×Acquisitioni ·Postit ·Overlapi +β2×Acquisitioni ·Postit+β3×Acquisitioni ·Overlapi +β4×Acquisitioni +α× (Fixed Effects)+ εit ,(2.3)separately for Y∈{Development,Discontinuation}. The dependent variables, Developmentitand Discontinuationit , and the two indicators, Acquisitioni and Postit , are defined the sameas above. Overlapi indicates whether drug i overlaps with any drug projects being devel-oped by the acquirer firm around the time of the acquisition. More specifically, the overlapindicator takes the value of one if any of the projects existing in the acquiring firm’s port-folio is developed to seek approval for the same disease group and uses the same biologicaltarget as the acquired drug.The coefficient on the triple interaction term, Acquisitioni ·Postit ·Overlapi, estimateshow likely (or unlikely) the drug projects acquired by firms with overlapping projectshave a development (or discontinuation) event in the post-acquisition periods than thedrug projects acquired by firms with no overlapping projects. I include the disease group-25biological target fixed effects and drug fixed effects, and the standard errors are clusteredat the drug project level. The interaction term Acquisitioni · Postit captures the effect ondevelopment (or discontinuation) activities of all acquired drug projects in the periods afterthe acquisitions. The interaction term Acquisitioni ·Overlapi captures the development (ordiscontinuation) activities of the drug projects acquired by firms with overlapping projectsbefore the acquisitions. By estimating the specification (2.3), I compare the effects of merg-ers and acquisitions on the innovation outcome of the drug projects acquired by firms withoverlapping drug projects, on those that are acquired by firms with no overlapping drugprojects, and on the non-acquired drug projects. Table 2.3 presents the estimation results.The coefficient on the interaction term Acquisitioni ·Overlapi and Acquisitioni are omitteddue to the drug fixed effects.Compared to column 1 (the estimate obtained from (2.1), as reported in column 2 ofTable 2.1), the coefficient on the Acquisitioni ·Postit term in column 2 is still negative andstatistically significant at the 1% significance level, suggesting the decline in the likelihoodof the acquired drugs having a development event after the acquisitions. Although the esti-mate of β1 is not statistically significant, its positive sign implies that acquired drug projectsthat overlap with the acquiring firms’ portfolio are more likely to have a development eventin the post-acquisition periods than the acquired drug projects that does not. It can be saidthat mergers and acquisitions hurt the innovation activities of the acquired drug projects.However, the negative effect would be mitigated when the drugs are acquired by firms withoverlapping projects than by the ones with no overlapping projects.Similarly, compared to column 3 (the same result reported in column 2 of Table 2.2),the coefficient on the Acquisitioni · Postit term is positive and statistically significant atthe 1% significance level, suggesting the acquired drug projects being more likely to bediscontinued after the acquisitions. The estimate of β1 is not statistically significant, and26the magnitude is closer to zero relative to the estimate of β1 in column 2.To summarize, the innovation outcomes of the acquired drug projects are not signifi-cantly affected by the overlap with the acquirers’ project portfolio in the scientific space.Considering the sign and magnitude of the coefficient estimate, acquisitions by firms withno overlapping projects would have a more negative effect on the development activitiesthan the ones with overlapping projects, yet the acquisitions are intrinsically harmful.Table 2.3: Project Overlap and Innovation OutcomesHaving a development event Having a discontinuation event(1) (2) (3) (4)Acquisition×Post -0.0838∗∗∗ -0.0912∗∗∗ 0.0264∗∗∗ 0.0272∗∗∗(0.0122) (0.0135) (0.00306) (0.00349)Acquisition×Post×Overlap 0.0376 -0.00410(0.0316) (0.00726)N 158,075 158,075 158,075 158,075R2 0.216 0.216 0.064 0.064Drug FE Y Y Y YDisease×Target FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.0012.5.4 Scientific versus Commercialization ActivitiesTo investigate whether science-related activities and commercialization-related activitiesare differently affected by mergers and acquisitions, I estimate the specifications (2.1)and (2.3) separately for Y ∈ {Science,Commercialization}. Scienceit is a dummy vari-able indicating whether a drug project i has a science-related development event in periodt and Commercializationit is a dummy variable indicating whether a drug project has acommercialization-related development event in period t. The independent variables aredefined the same as above. Columns 1 and 2 in Table 2.4 present the coefficient es-27timates for science-related development events, and columns 3 and 4 present those forcommercialization-related development events.In column 1, the coefficient on the interaction term, Acquisitioni · Postit , is -0.0848and statistically significant at the 1% significance level, suggesting that the acquired drugprojects are 8.48 percentage points less likely to have science-related development eventin the post-acquisition periods. In comparison, the coefficient estimate in column 3 is notstatistically significant, suggesting that there is no evidence that the acquired drug projectshave more (or less) commercialization-related development events in the post-acquisitionperiods. Thus, the negative impact of mergers and acquisitions on the innovation outcomesof the acquired drugs is mainly driven by the decline in science-related development activ-ities rather than commercialization-related development ones.Although it is not statistically significant, we have a positive coefficient estimate of β1.Similarly to the results in Table 2.3, it suggests that the clinical development of a drugproject is less likely to proceed after the acquisitions, however, it suffers less if the projectsare acquired by firms with overlapping projects in their portfolio.Considering that commercialization-related activities convey the strategic efforts of thefirms to make the drugs reach the market (e.g., submitting the applications for approvalor requests for designations), the insignificant coefficient estimates suggest that acquiringfirms do not strategically make more (or less) effort on the commercialization of the ac-quired drugs. And the acquiring firms also do not strategically invest more (or less) in theoverlapping acquired and non-overlapping acquired drugs.28Table 2.4: Scientific versus Commercialization ActivitiesHaving a development eventScience-related Commercialization-related(1) (2) (3) (4)Acquisition×Post -0.0848∗∗∗ -0.0923∗∗∗ 0.00228 0.00171(0.0113) (0.0124) (0.00508) (0.00553)Acquisition×Post×Overlap 0.0379 0.00285(0.0301) (0.0137)N 158,075 158,075 158,075 158,075R2 0.215 0.215 0.110 0.110Drug FE Y Y Y YDisease×Target FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.0012.5.5 Strategic versus Nonstrategic Scientific ActivitiesWould the decline in science-related development activities for the acquired drug projectsresult from the firm’s strategic decision of not initiating the new clinical trials? To addressthis question, I categorize the clinical development event types further into the activitiesthat can be strategically decided by firms and those that are merely scientific. The formerincludes the announcement of plans to initiate or the actual initiation of a clinical study.The latter consists of the progress and completion of the clinical studies and reporting ofintermediate or final data. Using this additional categorization, I estimate the specifica-tions (2.1) and (2.3) separately for Y ∈ {Strategic Science,Nonstrategic Science}, wherethe dependent variables Strategic Scienceit indicates whether a drug project i has a strate-gically decided science-related development event in period t and Nonstrategic Scienceitindicates whether a drug project has a purely scientific development event in period t. Theindependent variables are defined the same way as above, and the results are presented inTable 2.5.29The coefficient estimates on Acquisitioni · Postit are negative and statistically signifi-cant, suggesting the negative effect of mergers and acquisitions on the clinical developmentof the acquired drugs after the acquisitions. Although it is not statistically significant, thecoefficient estimate on Acquisitioni ·Postit ·Overlapi is positive, suggesting the existenceof an overlap between acquired drug projects and the projects in acquiring firm’s portfoliomitigates the negative effect of mergers.Table 2.5: Strategic versus Nonstrategic Scientific ActivitiesHaving a science-related development eventStrategic Nonstrategic(1) (2) (3) (4)post acq -0.0401∗∗∗ -0.0399∗∗∗ -0.0571∗∗∗ -0.0640∗∗∗(0.00639) (0.00679) (0.00980) (0.0107)post acq overlap -0.000820 0.0344(0.0182) (0.0261)N 158,075 158,075 158,075 158,075R2 0.127 0.127 0.189 0.189Drug FE Y Y Y YDisease×Target FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.0012.5.6 Development Patterns Before AcquisitionsI include dummy variables for six quarters before the acquisition and the interaction termsbetween those dummy variables and the overlap indicator. The estimation results shownin Table 2.6 suggest that development (or discontinuation) patterns in the pre-acquisitionperiods do not drive the results presented in the previous sections, as the coefficient onthose dummy variables and the interaction terms are mostly insignificant.30Table 2.6: Innovation Outcomes Before AcquisitionsHaving a development eventScientific Commercialization Strategic Nonstrategic Discontinuation(1) (2) (3) (4) (5) (6)Acquisition×Post -0.0987∗∗∗ -0.101∗∗∗ 0.00335 -0.0463∗∗∗ -0.0630∗∗∗ 0.0304∗∗∗(0.0157) (0.0143) (0.00603) (0.00759) (0.0124) (0.00361)Acquisition×Post 0.0385 0.0356 0.00936 -0.00359 0.0313 -0.00424×Overlap (0.0359) (0.0341) (0.0156) (0.0199) (0.0297) (0.00762)Before(-6)×Overlap -0.133∗∗ -0.116∗∗ 0.0449 -0.0265 -0.0998∗∗ -0.00985(0.0423) (0.0408) (0.0299) (0.0355) (0.0353) (0.0127)Before(-5)×Overlap -0.00848 -0.0201 0.00266 0.0139 -0.0350 -0.00502(0.0446) (0.0435) (0.0224) (0.0369) (0.0396) (0.0161)Before(-4)×Overlap 0.0322 0.0249 0.0269 0.00776 0.00971 0.0201(0.0467) (0.0459) (0.0278) (0.0355) (0.0421) (0.0209)Before(-3)×Overlap 0.0306 -0.0190 0.0341 0.0101 -0.0379 -0.00471(0.0493) (0.0471) (0.0308) (0.0366) (0.0411) (0.0146)Before(-2)×Overlap 0.0388 0.0327 0.0169 -0.0603∗ 0.0677 0.0125(0.0483) (0.0479) (0.0248) (0.0269) (0.0445) (0.0189)Before(-1)×Overlap 0.0106 0.0121 0.00225 -0.00161 0.00862 -0.0102(0.0458) (0.0444) (0.0234) (0.0324) (0.0406) (0.0168)Before(-6) 0.0444∗ 0.0299 0.000545 0.00909 0.0382∗ 0.00162(0.0204) (0.0193) (0.00853) (0.0134) (0.0178) (0.00619)Before(-5) -0.0260 -0.0223 -0.00668 -0.0255∗ 0.0111 0.00670(0.0191) (0.0182) (0.00816) (0.0117) (0.0171) (0.00675)Before(-4) -0.0145 -0.0145 -0.00268 -0.0243∗ 0.00441 0.00907(0.0195) (0.0186) (0.00815) (0.0115) (0.0167) (0.00708)Before(-3) -0.0271 -0.0293 0.0164 -0.0138 0.00712 0.00508(0.0189) (0.0182) (0.00942) (0.0119) (0.0171) (0.00617)Before(-2) -0.0464∗ -0.0473∗∗ 0.00454 -0.0279∗ -0.0183 0.0111(0.0183) (0.0171) (0.00840) (0.0109) (0.0157) (0.00719)Before(-1) -0.0334 -0.0339 0.0104 -0.0172 -0.0118 0.0150∗(0.0187) (0.0177) (0.00890) (0.0117) (0.0160) (0.00744)N 158,075 158,075 158,075 158,075 158,075 158,075R2 0.216 0.215 0.111 0.127 0.189 0.064Drug FE Y Y Y Y Y YDisease×Target FE Y Y Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001312.6 ConclusionIn this essay, I empirically study the impact of mergers on innovation using the pharmaceu-tical industry data. On the one hand, a merger can increase the chances of a drug reachingthe market when a merged firm expands its product portfolio and obtains knowledge ofthe acquired firm. On the other hand, a merger between competitors can be used as a toolto remove innovative rival drugs. These two effects working in opposite directions makethe relationship between mergers and innovation ambiguous, thus whether mergers andacquisitions improve innovation outcomes is an empirical question.By exploiting a novel dataset on pharmaceutical drug development, I find that mergersare harmful to the innovation outcomes of the acquired drugs. Acquired drugs are less likelyto be developed and more likely to be discontinued. I revisit the idea of killer acquisitions(Cunningham et al., 2021) by including an overlap indicator defined based on scientificcloseness. The estimation results suggest no significant effect of overlap on innovationoutcomes. By exploiting the comprehensiveness of the data, I find that mergers do nothave an impact on the probability of having a commercialization event, whereas mergershave a significant negative impact on the probability of having a scientific developmentevent. These results combined suggest that mergers negatively impact innovation outcomesbecause they negatively impact the scientific process of drug development, which relatesmore to the organizational effects of mergers. My findings contribute to the literature inthat suggesting competition and organizational effects of mergers.Putting less effort into developing acquired drug projects may be caused by efficientrelocation of the resources. The consolidated firms may strategically remove duplicatedinvestments, reconstruct their product portfolio, or utilize the acquired knowledge to de-velop other drugs. Acquired drugs being less successful does not necessarily imply that32the mergers are not beneficial for the innovation of the firm or industry as a whole. Thus,exploring different aspects of the relationship between mergers and innovation would helpto understand the whole picture.Future research may include studying the impact of mergers and scientific closenesson innovation outcomes of the acquiring firm’s drug projects. Whether the effects areheterogeneous depending on the stage of development around the timing of the mergerwould also be studied. Introducing an overlap measure with continuous values would helpquantify the extent to which the product portfolios of merging firms are close to each other.33Chapter 3The Impact of Weakened Patent Rightson Innovation: Evidence from HIV’sDrug Development3.1 IntroductionPharmaceutical companies spend billions of dollars researching and developing new drugsevery year. These companies rely on patent protection to recoup research and develop-ment (R&D) costs, as pharmaceutical drugs are easy to imitate once they reach the market.Patents preclude imitation, enabling pharmaceutical firms to set high prices for their drugs,which is what provides pharmaceutical firms with incentives to develop drug therapies thatcan save lives. However, those high prices create an inefficiency once a drug is invented, aspatients in need of a drug may be unable to afford it, possibly causing preventable deaths.This very point has motivated some countries to tackle public health emergencies using34various legislative measures or compulsory licensing—when a government agency grantsa license to an entity, which is not the patent holder, to produce the patented product—tooverride patents with the objective of making existing drugs available at a lower cost (e.g.,South Africa’s “South African Medicines and Related Substances Control Act” of 1997 totackle its HIV/AIDS crisis) (Rigamonti et al., 2005).Compulsory licensing or legislative measures that limit a pharmaceutical firm’s abilityto profit from a patent are policy instruments that may save lives in the short term, butresult in weakened intellectual property (IP) rights that may discourage pharmaceuticalfirms from developing new drugs in the future. Because of this trade-off, the literaturehas given arguments both in favor and against the use of these policy instruments to tacklepublic health crises (e.g., McMillan, 2003; Anderson Jr, 2010). However, no evidenceexists to date on the impact of weakened patent rights on the development of new drugtherapies to inform this debate.In this essay, we analyze and quantify the impact of weakened intellectual propertyrights on innovation by studying the case of HIV. Weakening patent rights, in the form ofa threat of compulsory licensing, raises issues of both intellectual property protection andpublic health. Loosely speaking, firms need to be provided with incentives to create newdrugs, but at the same time, there is a high social value of providing health treatment topeople who cannot afford drugs at market prices. The TRIPS agreement of 1995 both pro-tected incentives to innovate by harmonizing patent protection across borders and also setthe conditions under which a country may use compulsory licensing (Article 31 TRIPS).Following this agreement, many countries either threatened to impose or implemented com-pulsory licenses. The first of these measures was South Africa’s “South African Medicinesand Related Substances Control Act” of 1997, that enabled the country to procure patentedantiretroviral drugs at a lower cost to distribute them at prices below their retail prices. Af-35ter this, Brazil, Zimbabwe, and more than a dozen other countries have issued compulsorylicenses to procure low-cost HIV/AIDS drugs (Hoen et al., 2011).1 Although pharmaceu-tical companies fought these efforts to override their patents with a lawsuit, they withdrewthe lawsuit in the end because of the public relations disaster that it was creating.2To measure the impact of weakened IP rights on the development of drug therapies,we use the timing of South Africa’s “South African Medicines and Related SubstancesControl Act” of July 1997 as a shock to the strength of patent rights that was specific tothe HIV drug therapy industry, and use it as the basis of our identification strategy. In ouranalysis, we make use of a combination of datasets that allow us to measure the number ofdrugs in development for every disease at every moment of time during the period 1991-2007, as well as a number of time-varying covariates that predict drug development. Weconduct causal inference using the synthetic control methodology developed in Abadieand Gardeazabal (2003) and Abadie et al. (2010, 2015), which allows us to compare drugdevelopment outcomes between the HIV drug therapy industry and a control industry.The main difficulty in identifying how the weakening of intellectual property rightsaffected the development of HIV drugs lies in finding a control group for the HIV drugindustry. To overcome this difficulty, we use the synthetic-control methodology put for-ward by Abadie and Gardeazabal (2003) and Abadie et al. (2010, 2015). This frameworkconstructs a synthetic industry that “looks like” the HIV industry before the event under ex-amination. The evolution of the outcome of interest in this control industry after the eventprovides a counterfactual scenario against which we can compare the actual evolution ofthe HIV industry.1Compulsory licensing has been used by other countries to tackle other health crises. In 2006, Italy im-posed a compulsory license on Finasteride, a drug that treats benign prostatic hyperplasia. In 2007, Thailandimposed a compulsory license on Clopidogrel Bisulfate, a heart disease drug.2See “Drug Makers Drop South Africa Suit Over AIDS Medicine,” The New York Times, April 20, 2001.36Our main finding is that the weakening of patent rights in the HIV drug therapy industrycaused a large reduction in the number of therapies under development. Over the mid-1997–2006 period, the number of drugs in development decreased on average by 138,which is approximately 47% of the baseline level of outcome in July 1997 (i.e., when the“South African Medicines and Related Substances Control Act” was passed). We use analternative outcome variable in order to measure the effects among drug therapies that arecloser to completion. The number of drug therapies undergoing phase II or III clinical trialsdecreased on average by 42 in the post-intervention period, which is approximately 61% ofthe baseline level of outcome.This result holds under a series of placebo studies and robustness exercises describedin Abadie and Gardeazabal (2003) and Abadie et al. (2010, 2015). We test the credibilityof our estimates by reassigning the treatment to industries other than the HIV industry andperiods other than July 1997. The in-space placebo study results suggest that the likelihoodof obtaining estimates as large as our main results is 0.035 (0.031 when the number of drugsin phase II or III is used as the outcome variable), suggesting that our results are unlikely tohave been obtained by chance. The in-time placebo study results suggest negligible effectswhen the treatment period is reassigned to periods before the weakening of patent rights.Our results suggest the existence of a trade-off when making use of compulsory li-censes to tackle public health crises. Although such policies can save lives and thereforehave great benefits in the short run, our results suggest that they are costly in that theymay discourage the development of new drug therapies that could save more lives in thefuture. This evidence should be considered in future debates on the impacts of compulsorylicensing.373.2 LiteratureThe literature has explored different aspects of the relationship between intellectual prop-erty, innovation, and welfare. A well-documented fact is that there is a delay in the entryof new drugs into countries with weak intellectual property regimes and/or strong pricecontrols (which correlate with middle and low-income countries). Danzon et al. (2005)study the impact of price regulation on the entry delay of new drugs for 25 major marketsin the 1990s. The findings show that there are longer entry delays in a market with lowerprices. Cockburn et al. (2016) also shows that new drugs are launched with greater delaysin countries with price controls and weak property rights. Kyle (2006, 2007) finds similarevidence.Other authors have examined the effect of stronger patent protection on drug marketsin India. Chaudhuri et al. (2006) estimate the effect of patent protection on prices ofquinolones in India. Duggan et al. (2016) study the impact of the enactment of TRIPSin India on drug prices, finding small effects on the quantities sold and the number of firmsin the market. Mohapatra and Chatterjee (2015) find that the effect of price controls on themarket for Malaria drugs in India is to induce exit and reduce welfare.Some authors have explored compulsory licensing episodes after TRIPS. Harris (2010)discusses whether TRIPS has been a success from the point of view of the use of com-pulsory licensing. Beall and Kuhn (2012) describe 24 instances of compulsory licens-ing between 1995 to 2011. In 16 of these cases, compulsory licenses were issued for anHIV/AIDS-related drug. In 11 out of the 24 cases, drug companies reduced their prices orlicensed the drug voluntarily due to the threat of a compulsory license. Ramani and Urias(2015), Bond and Saggi (2017a) and Bond and Saggi (2017b), and Bond and Samuelson(2019) examine the equilibrium outcome of price negotiation in the shadow of a compul-38sory license and its effect on entry and welfare.Another strand of the literature explores how the threat of compulsory licensing affectsthe patent holder’s innovation incentives. Scherer and Watal (2002) argue that imposingcompulsory licensing on a firm reduces its incentives to innovate. Stavropoulou and Val-letti (2015) show a non-monotonic relationship between the cost of imposing compulsorylicensing and R&D investment. Chien (2003) examines 6 episodes of compulsory licensingover drug patents and concludes that compulsory licenses do not decrease the incentive toinnovate for the patent holder.The relationship between innovation and compulsory licensing also has been studied inindustries other than pharmaceuticals. Watzinger et al. (2017) study compulsory licensesfollowing the antitrust ruling against Bell Labs. They find that compulsory licensing canencourage innovation. Moser and Voena (2012) investigate the effects of compulsory li-censing on patent activity U.S. chemical inventions. They find that compulsory licensingincreases domestic invention by 20%. Baten et al. (2017) report similar results.Kyle and McGahan (2012) find that stronger patent protection as a consequence ofTRIPS increased R&D investments in wealthy countries but had no effect on developingdrugs targeting diseases prevalent in poor countries. One possible explanation for thisfinding is that firms were hesitant to invest in countries where compulsory licensing isa higher threat since TRIPS was a framework to justify compulsory licensing. Gallini(2017) discusses the role of patents in encouraging innovation in antibiotics. Sampat andWilliams (2019) find that gene patents had no effect on follow-on innovation. Budish et al.(2015) show that lower future expected payments due to shorter patent protection decreaseinnovation incentives. Finkelstein (2004), in the context of vaccination programs, showsthat a policy design to have a short-run effect (increase vaccination) can have long-term39effects (increase R&D for new vaccines).Finally, our paper also relates to the literature on market size and innovation becausemarket size determines the expected market profits. Acemoglu and Linn (2004) study theeffect of market size on entry and find that a 1 percent increase in the potential marketsize for a drug category leads to approximately a 4 percent growth in the entry of newnon-generic drugs and new molecular entities. Their findings also point to limited researchtoward drugs with small markets, which aligns with our findings. Dubois et al. (2015)estimate the elasticity of innovation to expected market size and find that, on average, 2.5billion is required in additional revenue to support the invention of one new chemical entity.Stern (2017) studies how regulatory uncertainty affects innovation in the context of medicaldevices. The main finding is that pioneer inventors are delayed longer than followers.3.3 Industry BackgroundThe worldwide revenue of the pharmaceutical industry in 2018 was about 1.2 trillion dol-lars.3 The total cost of developing a new drug is estimated to be about 2.5 billion dollars,which includes the cost of discovering new molecular targets, paying for clinical trials totest efficacy and safety of new drugs, and the cost of complying with the requirements im-posed by regulatory agencies (DiMasi et al., 2016b). The process of bringing a new drug tothe market also takes a long time (about 10 years) and involves a great deal of uncertainty(less than 10% success rate from discovery to reaching the market). Once a drug has beendeveloped, a generic manufacturer can copy the drug and produce it cheaply, which is whypharmaceutical firms rely on patent protection to appropriate rents from a successful newdrug.3http://www.statista.com/statistics/26310240In 1994, the World Trade Organization (WTO) established the agreement on Trade-Related Aspects of Intellectual Property Rights (TRIPS), which came into force in 1995.The goal of this agreement was to homogenize intellectual property laws among WTOmembers, requiring patent protection, with a duration of 20 years from the date of the filingof the patent application, in all of the relevant technology fields. One salient aspect of thisharmonization was the recognition of pharmaceutical patents—before the TRIPS agree-ment, many countries did not grant patents for pharmaceutical products—which wouldpresumably increase investment in developing countries. Some became concerned withthe consequences of strengthening patent protection. They argued that TRIPS could leadto higher drug prices and less access to drugs for life-threatening conditions. This debatewas particularly important in sub-Saharan countries where HIV/AIDS had became a globalepidemic (Hoen et al., 2011). Table B.1 shows the number of HIV/AIDS cases by regionin 1998.In February 1998, several pharmaceutical companies sued the government of SouthAfrica over amendments to its legislation that would increase the availability of afford-able drugs (namely, its “South African Medicines and Related Substances Control Act” of1997). According to the drug companies, the amendments comprised a violation of theTRIPS Agreement. Also, in 1998, Brazil threatened companies with compulsory licensesin order to bring drug prices down, which triggered a complaint by the U.S. against TRIPS’scompulsory license provisions. These events showcased the flexibilities contained in theTRIPS Agreement, specifically, the possibility for each country to determine the condi-tions required to issue a compulsory license (Article 31 TRIPS). The conflict between thesecountries and drug companies generated public outrage, and the backlash prompted drugcompanies to withdraw their cases. In 2001, the Doha Declaration on TRIPS and PublicHealth clarified that the TRIPS Agreement should protect public health and promote access41to affordable drugs (Feldman, 2009).Pharmaceutical companies addressed the threat of compulsory licenses by selling theirdrugs at discounted prices (e.g., by issuing voluntary licenses). As a consequence, the priceof HIV/AIDS treatment decreased considerably after the Doha Declaration of 2001. Forinstance, Brazil used the threat of compulsory licenses to negotiate lower prices of AIDSdrugs with Merck in 2001 and 2003, and with Abbott Laboratories in 2005. In 2004, In-donesia issued compulsory licenses on the production of the HIV/AIDS drugs Lamivudineand Nevirapine. In 2006, Thailand issued compulsory licensing for the HIV/AIDS drugEfavirenz and two other drugs. In 2007, Brazil issued a compulsory license for Efavirenz(which allowed imports of generic drugs from India) after a price-negotiation disagree-ment with Merck. In 2007, Indonesia renewed the compulsory licenses on Lamivudine andNevirapine and added a compulsory license on Efavirenz. In fact, since 2001, more than15 countries have issued compulsory licenses to procure low-cost HIV/AIDS drugs (Hoenet al., 2011).3.4 DataOur analysis makes use of a combination of datasets. First, we make use of Pharmapro-jects from Pharma Intelligence, which is widely used in the innovation literature (e.g., Kyle,2006). This database is a comprehensive and reliable source of information. The data iscollected from public sources such as press releases, reports by regulatory agencies, con-ference proceedings, medical literature, as well as pharmaceutical firms and researchers.The data contains information on pharmaceutical firms’ pipelines, including if/when a de-velopment project enters the discovery stage, phase I, II, or III, if/when it is approved bythe FDA, and if/when and where the drug is being commercialized. Importantly for our42purposes, Pharmaprojects tracks these development milestones for each drug project withassociated diseases that it intends to treat. The raw data covers 1983–2017 period, whichhas enough observations to reproduce the preintervention drug development in the syntheticcontrol analysis. If a drug is developed for more than one target disease, the list of diseasesis documented with the records of the drug. We transform the raw dataset, which is at thedrug level, into the disease level. For our empirical analysis, we construct disease–year–month level measures of the number of drugs in development for every disease at everymoment of time so as to capture the firms’ R&D effort.Second, we construct disease–year level measures of the number of deaths caused bya disease based on the World Health Organization Mortality Database so as to capture themarket size of each drug therapy industry (i.e., the potential number of patients who couldbenefit from curing the disease). The information in the database contains the cause ofdeath and the number of deaths by year, sex, and age group. The International Classificationof Diseases, a diagnostic tool for epidemiology, is used to record the cause of death. TheWHO Mortality database contains datasets associated with each of the four subsequentrevisions of the ICD, from ICD-7 through ICD-10. We use the datasets for the 9th and 10threvisions of the ICD that cover the estimation periods.Making use of these mortality data requires matching the disease definitions in theWHO Mortality Database and Pharmaprojects. We first assign likely ICD codes corre-sponding to each disease in Pharmaprojects using medical dictionaries, online ICD brows-ing tools by the WHO, and data documentation files. We then combine the two databasesusing identified ICD codes and aggregate the death statistics to construct our measure.In this process and the synthetic control analysis, we consider the top 100 diseases inPharmaprojects in terms of the number of drugs developed for each disease. These 100diseases account for about 70% of the drugs in the pipelines, which is fairly representative43to support the robustness of our analysis.Among the top 100 diseases, 11 diseases that are not medical conditions causing deaths(i.e., no mortality data had been reported for assigned ICD codes) or do not have corre-sponding ICD codes are excluded from the analysis. Dropping these 11 diseases leave uswith 89 diseases (88 control industries and HIV/AIDS therapy industry). Among these 89diseases, unspecified cancer has the largest number of drugs developed, 8,743 in total, andsepsis has the smallest number of drugs developed, 213 in total.Third, we use disease–year level information both on the number of patent applicationsand granted patents with subject matter relevant to each disease in Pharmaprojects. Thepatent data were collected from Google Patents and the United States Patent and TrademarkOffice.Lastly, we use disease–year level information on the number of academic publicationsrelevant to that disease. Similarly to the patent data, we use the disease definitions inPharmaprojects. The data were collected from PubMed, which contains citations and ab-stracts of biomedical literature from MEDLINE, life science journals, and online books.For each disease, we retrieve how many research articles are published each year between1950 and 2019.3.5 Empirical Framework and Results3.5.1 Empirical FrameworkThe objective of our empirical analysis is to quantify how weakened patent rights impactthe firms’ research pipelines. We do this by comparing the number of HIV therapies under44development relative to those of a control industry both before and after the weakening ofpatent rights in July 1997 (i.e., the timing of the “South African Medicines and RelatedSubstances Control Act”).To make this comparison, we construct a synthetic “HIV drug therapy” industry, whichwas not affected by the threat of compulsory licenses, making use of the framework pro-posed in Abadie and Gardeazabal (2003) and Abadie et al. (2010, 2015). In practice, weconstruct a synthetic HIV drug therapy industry by computing a weighted average of otherindustries unaffected by the threat of compulsory licensing at time T ∗. Given a set of J con-trol drug therapy industries, we seek a vector of non-negative weights, W = (w1, . . . ,wJ)′,which sum up to one and where w j represents the weight placed on control industry j.Each vector W leads to a different synthetic HIV drug therapy industry, so we choose thevector W that minimizes the differences between the synthetic and actual HIV drug therapyindustries in the period before the threat of compulsory licensing.More specifically, let XHIV = (Z′HIV ,YHIV,1, . . . ,YHIV,T ∗−1)′ be a (k×1) vector of bothcovariates (ZHIV ) and pre-intervention outcomes (YHIV,1, . . . ,YHIV,T ∗−1) for the HIV drugtherapy industry and let Xcontrols be a (k× J) matrix containing the same variables but forall J control drug therapy industries. We choose the vector of weights given byW∗ = argminW(XHIV −XcontrolsW)′V(XHIV −XcontrolsW)subject to ∑ j w j = 1, where V is a diagonal matrix with non-negative components. Notethat W∗ minimizes differences between the actual and synthetic HIV drug therapy industryboth in covariates and outcomes in the pre-intervention period.With estimates for W∗ in hand, we can then compute the outcomes for the syntheticHIV drug therapy industry using Ysynth = YcontrolsW∗, where Ycontrols is a (T × J) vector45containing the outcome variables for all J control industries for all T periods in the data(i.e., including both pre- and post-intervention periods). The synthetic control estimator ofthe effect of weakened intellectual property rights is given byYHIV −YcontrolsW∗,that is, the comparison between the post-intervention outcomes for the actual and syntheticHIV drug therapy industry.In our specifications, the observed covariates for each disease–year–month combina-tion include the number of patent applications and granted patents related to therapies ofthe disease, the number of research publications related to therapies of the disease, and thenumber of deaths caused by the disease. These variables are considered to be correlatedto pharmaceutical research and development. Patent information reflects the level of in-tellectual property protection, the novelty of inventions (as firms file patent applicationswhen they have developed an invention or innovation that they believe is novel, and thepatents are granted when the invention is considered novel), and the level of competition inthe therapeutic area. The number of academic publications reflects research advancementsand knowledge dissemination in discovering and developing drug therapies related to thedisease. Moreover, the number of deaths is a proxy to capture the potential market size ofeach drug therapy industry, which is found to determine the expected market profits and,thus an important factor in R&D decisions.Our main outcome variable is the number of drug therapies in development, which isalso measured at the disease–year–month level. We use the lagged values of the outcomevariable and matching covariates listed above in running the synthetic control analysis. Foreach control industry in the donor pool, the matching covariates are averaged over the years46from 1991 to 1997.In the analysis, we separately apply the synthetic control method to the number of alldrug therapies in development and the number of drugs in phase II or III clinical trials. Weuse the two kinds of outcome variables to examine whether the results are heterogeneousdepending on how far the development of drug therapies is achieved. Drug therapies withfavorable results in earlier development stages might be less likely to be canceled.3.5.2 Constructing a Synthetic HIV Drug Therapy IndustryAs explained above, we construct the synthetic HIV drug therapy industry as a weightedaverage of other industries chosen to resemble the values of covariates and outcomes of theHIV drug therapy industry before the threat of compulsory licensing. Table 3.1 presents theweights of each drug therapy industry in the synthetic HIV drug therapy industry, W∗. Theweights reported in the left panel of Table 3.1 indicate that the number of all drug therapiesin development for HIV before the intervention is best reproduced by a combination ofthe drug therapy industries for breast cancer, unspecified cancer, unspecified hypertension,unspecified infection, and cerebral ischemia. The weights reported in the right panel ofTable 3.1 indicate that the number of drug therapies for HIV in phase II or III is bestreproduced by a combination of the drug therapy industries for breast cancer, unspecifiedhypertension, colorectal cancer, and asthma. All other drug therapy industries in the donorpool are assigned zero weights. Breast cancer receives the highest weight in both of thesynthetic HIV drug therapy industry, 0.568 and 0.508.Table 3.2 compares the preintervention characteristics of the actual HIV drug ther-apy industry to those of the synthetic HIV drug therapy industry (obtained by Xsynth =XcontrolsWˆ∗), and also to those of an average of the 88 drug therapy industries in the donor47Table 3.1: Industry Weights in the Synthetic HIV Drug Therapy IndustryAll drug therapies in development Drug therapies in phase II or IIIDrug therapy industry Synthetic weight Drug therapy industry Synthetic weightCancer, breast 0.568 Cancer, breast 0.508Cancer, unspecified 0.249 Hypertension, unspecified 0.193Hypertension, unspecified 0.120 Cancer, colorectal 0.179Infection, unspecified 0.053 Asthma 0.121Ischemia, cerebral 0.011pool. The results in Table 3.2 suggest that the synthetic HIV drug therapy industry pro-vides a better comparison unit for the HIV drug therapy industry than the average of otherindustries in our donor pool. The synthetic HIV drug therapy industry is very similar to theactual HIV drug therapy industry in terms of lagged outcome values. The number of drugsunder development prior to the weakening of patent rights is much lower in the average of88 control industries than in the HIV drug therapy industry. Although the synthetic HIVdrug therapy industry loses its fit in the other predictors, it still matches the actual HIV drugtherapy industry closer than the average of 88 control industries, except for the number ofdeaths.Table 3.2: Predictor Means before Weakening of Patent RightsActual HIV drugtherapy industrySynthetic HIV drugtherapy industryAverage of 88control industriesNumber of all drugs in development 239.26 239.60 59.00Number of drugs in phase II or III 44.67 44.76 13.22Log(number of deaths) 10.99 11.91 12.12 10.71Number of publications 6.17 3.00 3.58 2.98Number of patents filed 48.00 22.49 20.84 19.83Number of patents granted 35.57 12.33 11.48 9.53Note: The number of drugs is averaged for January 1991–June 1997. The log of the number of deaths causedby each disease, number of publications, number of patents filed, and number of patents granted related toeach disease are averaged for 1991–1997. The number of publications is divided by 1,000 in the estimation.483.5.3 ResultsFigure 3.1 visualizes the result. Figure 3.1a displays the number of drugs in developmentfor the HIV drug therapy industry and its synthetic counterpart during 1991–2006. Thesynthetic HIV drug therapy industry closely reproduces the number of drugs in develop-ment for the HIV drug therapy industry during the entire preintervention period. The closefit for the predictors presented in Table 3.2 and the close fit for the preintervention drugdevelopment trajectory shown in panel (a) of Figure 3.1a suggest that the synthetic HIVdrug therapy industry approximates the number of drugs that would have been developedto treat HIV in the second half of 1997–2006 in the absence of the threat of compulsorylicensing.Our estimate of the effect of weakened patent rights on drug development for HIV isgiven by the difference between the actual HIV drug therapy industry and its synthetic ver-sion, as visualized in Figure 3.1a. The solid line visualizes the evolution of the actual HIVdrug therapy industry (i.e., YHIV ), whereas the dashed line visualizes that of its syntheticcounterpart (i.e., Ysynth = YcontrolsWˆ∗). The dashed vertical line indicates the period thatthe HIV drug therapy industry was exposed to a weakening of patent rights (i.e., July 1997)via a threat of compulsory licensing. After such measure is taken, the two lines divergenoticeably. While drug development in the synthetic HIV drug therapy industry contin-ued to increase at a pace similar to that of the preintervention period, the actual HIV drugtherapy industry experienced a substantial decline in drug development. The differencebetween the two lines increases over time toward the end of the estimation period. At theend of the estimation period, the number of drugs in development for the HIV industry isabout 92% (=(232-445.735)/232) lower than that for its synthetic version. Over the entiremid-1997–2006 period, the number of drugs in development decreases by about 138 per49month on average. This is approximately 47% of the baseline level of outcome in July1997. Thus, our results suggest a substantial negative effect of weakened patent rights onHIV drug development.As an alternative outcome variable that would allow us to measure the effects amongdrug therapies that are closer to completion, we use the number of drug therapies in devel-opment that are undergoing phase II or phase III clinical trials. As those drugs have alreadyachieved the earlier stages of development, firms might have less incentive to discontinuethem. Figure 3.1b displays the number of drugs in phase II or III for the HIV drug therapyindustry and its synthetic version during 1991-2006. The close fit for the preinterventioncharacteristics in Table 3.2 and the close fit for the preintervention drug development tra-jectory in Figure 3.1b suggest that the synthetic HIV drug therapy industry provides a closeapproximation to the number of HIV drugs that would have been undergone phase II or IIIclinical trials during mid-1997–2006 in the absence of the threat of compulsory licensing.After July 1997, the two lines shown in Figure 3.1b diverge noticeably, and the differencebetween the two lines increases in time. At the end of the estimation period, the numberof HIV drugs in phase II or III is about 71% (=(102-174.697)/102) lower than that for itssynthetic version. Over the entire mid-1997–2006 period, the number of drugs in phase IIor III decreases by about 42 per month on average. This is approximately 61% of the base-line level of outcome in July 1997. The magnitude of the negative effect is smaller for drugtherapies in the later stage of development (71% versus 92%). Combined with the discus-sion of the number of drug therapies in development above, our results suggest a negativeeffect of weakened patent rights via a threat of compulsory licensing on the incentives todevelop new drug therapies.50Weakening patent rights →100200300400500Number of drugs in development1991 1993 1995 1997 1999 2001 2003 2005 2007YearActual HIVSynthetic HIV(a) All drugs in developmentWeakening patent rights →050100150200Number of drugs in phase II and III1991 1993 1995 1997 1999 2001 2003 2005 2007YearActual HIVSynthetic HIV(b) Drugs in phase II or III clinical trialsFigure 3.1: Trends in drug development: actual HIV drug therapy industry versussynthetic HIV drug therapy industry3.6 Placebo StudyTo evaluate the significance and credibility of our estimates, we conduct placebo studieswhere the treatment of interest is reassigned to drug therapy industries for diseases otherthan HIV and to a period other than July 1997.3.6.1 In-Space Placebo StudyOne can raise the question of whether our results visualized in Figure 3.1 quantify thetrue effect of weakened patent rights on drug development or are driven merely by chance.To answer this question, we conduct an in-space placebo study by iteratively applying thesynthetic control method to drug therapy industries that did not experience the threat ofcompulsory licensing. In each iteration, we reassign the intervention to one of the 88 drugtherapy industries, shifting the HIV drug therapy industry to the donor pool. Then weestimate the placebo treatment effects associated with each control industry as the differ-ence between the drug development for the actual drug therapy industry and its syntheticversion. This gives us a distribution of placebo estimates that enables us to examine how51often we would obtain estimates of the magnitude of our main results if the interventionis randomly assigned. The estimated treatment effects in our main results are consideredsignificant if they are unusually large compared to the distribution of placebo estimates.Figure 3.2 displays the result of this in-space placebo study.Weakening patent rights →-2000200400600Gap in number of drugs in development1991 1993 1995 1997 1999 2001 2003 2005 2007YearHIV industryControl industries(a) All drugs in developmentWeakening patent rights →-100-50050100Gap in number of drugs in phase II and III1991 1993 1995 1997 1999 2001 2003 2005 2007YearHIV industryControl industries(b) Drugs in phase II or III clinical trialsFigure 3.2: In-space placebo study: estimated effect for the HIV drug therapy indus-try and placebo effects for 88 industries in the donor poolFigure 3.2a displays the results of this in-space placebo study, where the outcome vari-able is the number of all drugs in development. The light gray-colored lines represent theestimated placebo treatment effects associated with the 88 drug therapy industries in thedonor pool. The dashed line in dark-gray color plots the average of the placebo treatmenteffects in each post-intervention period, which is negative but close to zero. The navy-colored line represents the estimated effect associated with the HIV drug therapy industry.Drug development in the synthetic HIV drug therapy industry resembles the actual evolu-tion during the preintervention period (i.e., the placebo gaps are close to zero), and it startsto diverge from the actual trajectory after the intervention (i.e., placebo gaps differ fromzero), reflecting the results discussed above. As shown in Figure 3.2a, the estimated effectfor the HIV drug therapy industry after July 1997 is substantially large in the absolute valuerelative to the distribution of the estimated effects for the industries in the donor pool. The52likelihood of obtaining estimates as large as our main results is 0.035 if a treated industry israndomly chosen. It suggests that our results for the HIV drug therapy industry are unlikelyto have been obtained by chance.Similarly, Figure 3.2b presents the in-space placebo study results for the alternative out-come variable, the number of drugs undergoing phase II or III. As the figure indicates, theestimated effect for the HIV drug therapy industry is substantial relative to the distributionof placebo effects. It suggests that if we choose a treated industry at random, the chancesof obtaining the estimated effect as large as the one in our main results is 0.031.In Figure 3.2a, the plot with the worst fit in the preintervention period lying above allthe other lines is for unspecified cancer. The plot below the one for the HIV drug therapyindustry in the mid-1998 to mid-2005 period is for unspecified hypertension. The third andfourth-lowest plots are for unspecified infection and cerebral ischemia. In Figure 3.2b, theplot below the one for the HIV drug therapy industry during the 1996 to mid-1999 period isfor unspecified cancer. The second-lowest plot from mid-2001 is for Alzheimer’s disease.The plot for unspecified hypertension has the worst fit in the preintervention period.3.6.2 In-Time Placebo StudyAs an alternative way to evaluate the credibility of our main results, we perform an in-time placebo study, in which we reassign the hypothetical treatment to a period beforethe weakening of patent rights actually happened. If the estimated placebo effects arenegligible, it supports that our main estimates were not driven by chance and capture thetrue effect of our interest. Contrariwise, large placebo estimates undermine the credibilityof our main results.To conduct this in-time placebo study, we re-estimate the model for the case when the53treatment is reassigned to April 1994, roughly in the middle of our pretreatment period.We lag the covariates and pre-intervention outcomes accordingly for the placebo treatmentperiod. We estimate the model for the 1991–July 1997 period. Figure 3.3 presents theresult of this in-time placebo study.Figure 3.3a and Figure 3.3b display the result of the in-time placebo study when theoutcome variable of the number of all drugs in development and drugs in phase II or IIIclinical trials, respectively. As shown in the figures, the synthetic HIV drug therapy indus-try closely reproduces the path of the actual HIV drug therapy industry for the 1991–March1994 period. Importantly, the drug development paths of the actual HIV industry and itssynthetic counterpart do not diverge considerably in the April 1994–July 1997 period. Thissuggests that our main estimates visualized in Figure 3.1 reflects the effect of weakenedintellectual property rights and are not driven by chance.4Placebo weakening patent rights →150200250300Number of drugs in development1991 1992 1993 1994 1995 1996 1997YearActual HIVSynthetic HIV(a) All drugs in developmentPlacebo weakening patent rights →020406080Number of drugs in phase II and III1991 1992 1993 1994 1995 1996 1997YearActual HIVSynthetic HIV(b) Drugs in phase II or IIIFigure 3.3: In-time placebo study: estimated effects for placebo weakened intellec-tual property rights in April 19944In addition to this in-time placebo study, we rerun the estimation for the cases when the treatment isreassigned to April 1995 and April 1996. Figure B.1 in the appendix presents the result that confirms thecredibility of the estimates in our main results.543.6.3 Placebo Study using Breast Cancer Therapy IndustryTo test whether the estimated effects in our main results are driven by factors other thanthe weakening of patent rights, we apply the synthetic control method to a drug therapyindustry that was not subject to the event of interest. This placebo study compares drugdevelopment in an industry similar to the HIV drug therapy industry but unrelated to com-pulsory licensing to its synthetic counterpart.For this placebo study, we use the breast cancer drug therapy industry, which receivesthe largest weight in constructing the synthetic HIV drug therapy industry as presented inTable 3.1. We construct a synthetic breast cancer therapy industry as a weighted average of87 other industries (excluding the HIV drug therapy industry from the donor pool) that mostclosely resembles the preintervention values of covariates and outcomes of the actual breastcancer therapy industry. The estimates are given by the gap between the drug developmenttrends of the actual and synthetic breast cancer drug therapy industries.Figure 3.4a shows the evolution of the number of all drugs in development for the ac-tual breast cancer therapy industry (solid line) and that of the synthetic version (dashedline). Similarly, Figure 3.4b shows the evolution of the number of drugs in phase II or IIIclinical trials for the actual breast cancer therapy industry (solid line) and that of the syn-thetic version (dashed line). In both figures, the weighted average of non-HIV drug therapyindustries closely reproduces the preintervention drug development for the breast cancertherapy industry. In the post-intervention periods, the number of drugs in development in-tended to treat breast cancer exceeds that of synthetic counterparts. Since the breast cancertherapy industry receives the largest weight, it contributes the most to the synthetic controlfor the HIV drug therapy industry. Thus, this placebo study suggests that the treatmenteffect of weakened intellectual property rights on the development of HIV drug therapies55visualized in Figure 3.1 may have been overestimated.0100200300400500Number of drugs in development1991 1993 1995 1997 1999 2001 2003 2005 2007YearTreated unitSynthetic control unit(a) All drugs in development050100150200Number of drugs in phase II and III1991 1993 1995 1997 1999 2001 2003 2005 2007YearTreated unitSynthetic control unit(b) Drugs in phase II or III clinical trialsFigure 3.4: Trends in drug development: actual breast cancer therapy industry versussynthetic breast cancer therapy industry3.7 Leave-One-Out Robustness TestWe conduct a leave-one-out robustness test to check whether our main results are sensitiveto changes in the synthetic weights. Recall that the synthetic HIV drug therapy industryis estimated as a weighted average of breast cancer, unspecified cancer, unspecified hy-pertension, unspecified infection, and cerebral ischemia when our outcome variable is thenumber of drugs in development. The synthetic HIV drug therapy industry is estimated as aweighted average of breast cancer, unspecified hypertension, colorectal cancer, and asthmawhen our outcome variable is the number of drugs in phase II or III clinical trials.We iteratively estimate the effects for the HIV drug therapy industry, omitting one ofthe industries with a positive synthetic weight in Table 3.1 in each iteration. For example,we exclude breast cancer from the donor pool and run an estimation using the remaining87 industries in constructing the synthetic HIV drug therapy industry. We repeat this foreach of the five industries (four industries for the alternative drug development measure).56Figure 3.5a shows the leave-one-out estimates (gray lines) and our main estimates (solidand dashed black lines), where the number of drugs in development is used as our outcomevariable. The leave-one-out synthetic controls for breast cancer, unspecified hypertension,unspecified infection, and cerebral ischemia give us slightly larger effects compared to themain results. This suggests that our main results are robust to the exclusion of any of thesefour control industries.However, one thing to note in Figure 3.5a is that the main results would not be robustto the exclusion of the unspecified cancer therapy industry. Drug development for the syn-thetic HIV drug therapy industry that is reproduced as the weighted average of industriesother than unspecified cancer does not show an increasing trend as it does in the mainanalysis. Rather, its evolution is similar to the actual drug development trajectory. Theestimated effects are negligible and become negative only after the early 2000s.Figure 3.5b displays the leave-one-out estimates (gray lines) and the estimates in ourmain results for the alternative measure of drug development, the number of drugs in phaseII or III clinical trial (solid and dashed black lines). The leave-one-out synthetic controlgives us the smallest estimate when the breast cancer drug therapy industry is excludedfrom the donor pool. The other leave-one-out synthetic controls for unspecified hyperten-sion, colorectal cancer, and asthma show slightly smaller or larger effects of the weakeningof patent rights compared to the main results. This suggests that the results for the numberof drugs in later stages of development are fairly robust to the changes in the syntheticweights.57Weakening patent rights →100200300400500Number of drugs in development1991 1993 1995 1997 1999 2001 2003 2005 2007YearActual HIVSynthetic HIVSynthetic HIV (leave-one-out)(a) All drugs in developmentWeakening patent rights →050100150200Number of drugs in phase II and III1991 1993 1995 1997 1999 2001 2003 2005 2007YearActual HIVSynthetic HIVSynthetic HIV (leave-one-out)(b) Drugs in phase II or III clinical trialsFigure 3.5: Leave-one-out distribution of the synthetic HIV drug industry3.8 ConclusionIn this essay, we analyze and quantify the impact of weakened intellectual property rightson innovation. To recoup R&D costs, pharmaceutical industry relies heavily on patent pro-tection, which incentivizes pharmaceutical firms to develop more drugs but creates ineffi-ciency as patients in need of a drug may be unable to afford it, possibly causing preventabledeaths. We use the issuance of compulsory licensing on HIV drug therapies in countrieslike South Africa and Brazil as the base of our identification strategy. By implementing thesynthetic control method, we find that weakened patent rights caused a significant decreasein drug development in the HIV drug therapy industry. The development of drug therapiesalso declines for drugs in the later stages, although the magnitude of the estimated effectbecomes smaller compared to the results for all drugs in development. The results arerobust to changes in the synthetic weights.The results suggest the existence of a trade-off when making use of compulsory licensesto tackle public health crises. Although such policies can save lives and therefore havegreat benefits in the short run, our results suggest that they are costly in that they may58discourage the development of new drug therapies that could save more lives in the future.The evidence suggested by this essay should be considered in future debates on the impactsof compulsory licensing.59Chapter 4Price Dispersion and Competition:Evidence from the Expansion ofSouthwest Airlines4.1 IntroductionPrice dispersion is a feature of the airline industry (Borenstein and Rose, 1994). An air car-rier charges different prices to passengers in the same market, and the variation in prices issubstantial. The dispersion in ticket prices may arise from variations in costs to provide dif-ferent flight services; it may also arise from the airlines’ price discrimination, which will bediscussed with more emphasis in this paper. When a carrier enters a market, competition inthat route increases, so the incumbents may change their prices to prevent the entrant fromtaking their customers and profits. Moreover, facing an entry threat, the incumbents mayadjust their pricing strategies before the rival enters the market to discourage the potential60entrant.What is the impact of competition on price dispersion? More specifically, how doincumbents adjust their prices in response to the increase in competition via entry by therivals? In this paper, I exploit the expansion of Southwest Airlines to capture potential andactual competition. By examining the effect of potential and actual entry on incumbents’pricing decisions, this paper contributes empirical evidence to the debate on the relationshipbetween competition and price dispersion.To discuss the environment more in detail, to target consumers with different elasticitiesof demand, air carriers charge higher fares for tickets with fewer restrictions (i.e., fullfares) and charge lower fares for tickets with more restrictions (i.e., discount fares) suchas advance purchase restriction, inability to change the ticket or get refunds if canceled.Given that the airlines charge different prices to passengers depending on their elasticitiesof demand, the effect of entry threat and actual entry on the incumbents’ price dispersionis not straightforward. For example, the incumbents may cut down the discount fares andbarely change the full fares to attract more price-sensitive consumers and, at the same time,earn a similar amount of revenue from price-insensitive consumers. On the other hand,the incumbents may lower the full fares by a substantial amount to take the whole marketand signal the potential entrant to discourage entry. The price dispersion will increasein the former case, whereas it will decrease in the latter case. Thus, investigating theeffect of potential and actual entry by low-cost competitors on price dispersion can providenew evidence to understand the price discrimination behavior of firms facing an expectedincrease in competition.For the empirical analysis, I make use of the Airline Origin and Destination Survey(DB1B), which provides airline ticket prices and detailed itinerary information for the 199361to 2016 period. Importantly for the purpose of this paper, DB1B allows me to measure theprice dispersion of the incumbent carriers and identify the periods of entry threat and actualentry by Southwest Airlines. I use the Gini coefficient and the 10th and 90th percentiles ofthe price distribution as the price dispersion measures. I exploit the aggressive expansionof Southwest Airlines to identify the timing of competitive threats and threatened routes(Goolsbee and Syverson, 2008). The period of entry threat is the earliest period in whichSouthwest Airlines established its presence in both endpoints of a given route. The pe-riod of actual entry is identified by the earliest period in which Southwest Airlines startedoperating direct flights on that route.I estimate a two-way fixed effects model with time dummies like a standard event studyframework to examine the effect of entry threat and actual entry by Southwest Airlines onthe price dispersion of incumbent legacy carriers. I include time dummies for the periodsduring and after actual entry, as well as the periods before, during, and after the threatof entry. A key identification assumption of the event study is that there are no otherconfounding events occurring at the same time as the event of our interest taking place.The result shows that when incumbent carriers face the entry threat by Southwest Air-lines, the price dispersion declines in that route. The effect is driven by a sharp decrease inthe 90th percentile of the price distribution, suggesting that incumbents cut prices to makeentry less attractive for Southwest Airlines. The result also shows that incumbents cutdown the 10th percentile of the price distribution after actual entry by Southwest Airlines,increasing price dispersion to its pre-entry threat levels. It is likely a strategic responseof incumbents to avoid the loss of price-sensitive consumers to Southwest Airlines afterits entry. The results suggest that competition can increase and decrease price dispersion;thus, timing and strategic motive are essential to understand the relationship between com-petition and price dispersion. These results are robust to different dispersion measures and62the addition of control variables (e.g., Herfindahl index, bankruptcy, fuel price).Thus, this paper contributes empirical evidence to the debate on the relationship be-tween competition and price dispersion, suggesting the existence of preemptive strategicentry deterrence in the U.S. airline industry. The paper is organized as follows. Section 2describes the data, and Section 3 discusses the empirical specification. Section 4 presentsthe estimation results, and section 5 concludes.4.2 LiteratureThe relationship between competition and price dispersion has been studied in the literaturein different aspects. Traditional microeconomic theory suggests that the ability of firms toengage in price discrimination increases with the firm’s market power. A firm in perfectcompetition with no market power cannot price-discriminate, whereas a monopolist can ifit distinguishes the different demand elasticities of consumers. Thus, in a market betweenthe two extreme cases, the dispersion in prices charged by firms would decrease as themarket becomes more competitive.Some authors, however, find a positive relationship between competition and pricedispersion. Borenstein (1985) and Holmes (1989) suggest that distinguishing consumersbased on their cross-elasticity of demand leads to greater price dispersion for a more com-petitive market with heterogeneous brands and different consumer preferences over thosebrands. Modifying Borenstein (1985)’s model, Borenstein and Rose (1994) study pricediscrimination in multi-brand firms. Gale (1993) suggests more price discrimination undera duopoly than under a monopoly when price discrimination is measured as the differencebetween full fare and discount fare with advance-purchase restriction.The findings of empirical studies on the effect of competition on price dispersion are63mixed. Borenstein and Rose (1994) find a positive impact of competition on price disper-sion using cross-sectional data. Stavins (2001) suggests a positive relationship betweencompetition and price dispersion using implicit marginal prices of ticket restrictions to ap-proximate price discrimination. Giaume and Guillou (2004) also find a positive relationshipbetween competition and price dispersion using data from the European airline market. Incontrast, Gerardi and Shapiro (2009) find a negative effect of competition on price disper-sion in the U.S. market. Gaggero and Piga (2011) suggest a negative impact of competitionon price dispersion in the market between the U.K. and the Republic of Ireland. Moreover,Dai et al. (2014) and Chakrabarty and Kutlu (2014) respectively find a nonmonotonic andnonlinear relationship between market competition and price dispersion.A strand of literature studies strategic entry deterrence. Dixit (1979) studies the credi-ble threat of holding excess capacity as entry deterrence when the size of potential entrantsis comparable to incumbents. Milgrom and Roberts (1982) suggest that limit pricing canarise if an incumbent has private information on its payoff; in other words, if an entrant hasincomplete information. Aghion and Bolton (1987) find that an incumbent facing entry bylow-cost competitors may sign long-term contracts with its customers creating switchingcosts to deter entrance. Klemperer (1987, 1989, 1995) suggest a model that explains howthe threat of entry affects an incumbent’s behavior in a market with switching costs. Ac-cording to their findings, an incumbent would preemptively cut its price to deter entry orreduce the entrant’s profits if entry is unavoidable.Some authors empirically study the effect of increased competition via the entrance ofrivals on airline ticket prices. Bennett et al. (1993), Dresner et al. (1996), and Windle andDresner (1999) study the competitive impact of the entry of low-cost carriers and find itleads to a substantial decrease in ticket prices. Morrison (2001) and analyze the impactof actual, adjacent, and potential competition on savings to passengers. Brueckner et al.64(2013) study the effect of adjacent and potential competition from low-cost carriers onticket prices.Goolsbee and Syverson (2008) find a substantial decrease in incumbents’ fares on theroutes threatened by Southwest Airlines’ entry, suggesting preemptive actions. The paper isthe closest to mine in that the authors use Southwest Airlines’ establishment of a presencein the endpoints to capture competitive threats toward a given route; however, analyzingprice dispersion is not the main focus of their study. Tan (2016), another very close paperto mine, finds that incumbents’ price dispersion decreases in response to the actual entryby low-cost carriers. The author analyzes the effect of competition on price dispersion;however, incumbents’ preemptive action in response to potential entry is not discussed.4.3 DataFor the analysis in this paper, I use the Airline Origin and Destination Survey (DB1B). TheDB1B is a 10% random sample of airline tickets from reporting carriers collected by theBureau of Transportation Statistics (BTS) of the U.S. Department of Transportation (DOT).The database provides itinerary information, including origin, destination, travel distance,passengers transported, and carriers engaged (ticketing/operating/reporting). Importantlyfor the purpose of this paper, DB1B records the price of the airline tickets. The data isavailable on a quarterly basis, and I use the data from the first quarter of 1993 through thefourth quarter of 2016.In this paper, the route is defined as non-directional origin-and-destination pairs. Forexample, a flight from Chicago to Los Angeles and a flight from Los Angeles to Chicagoare regarded as products in the same market. In the literature, airports or cities are used todefine routes between the two endpoints, and city pairs are used to incorporate the fact that65low-cost carriers use the secondary airports rather than the primary ones in the same cityserved by legacy carriers.1For the empirical analysis, tickets for domestic flights traveled within the contiguousUnited States (i.e., 48 adjoining mainland states and the District of Columbia) are used.This excludes the non-contiguous states of Alaska, Hawaii, and all off-shore territories andpossessions (i.e., American Samoa, Guam, the Northern Mariana Islands, Puerto Rico, andthe U.S. Virgin Islands).I use tickets for one-way or round-trip flights. In other words, tickets for open-jaw andcircle trips are excluded. For a flight to be considered round-trip, passengers on an inboundflight must depart from the airport where the outbound flight arrived and return to the initialorigin airport where the outbound flight departed. I use tickets for direct or nonstop flightsand drop tickets for connecting flights. The underlying rationale is that connecting serviceis not a good substitute for nonstop service, as suggested in the previous studies (e.g.,Borenstein and Rose, 1994; Goolsbee and Syverson, 2008; Gerardi and Shapiro, 2009; Daiet al., 2014; Tan, 2016). Connecting flights typically take significantly longer than nonstopflights, and the risk of lost luggage or missed connections is greater than nonstop flights.Thus, time-sensitive passengers are unlikely to substitute connecting flights for nonstopflights in response to a small fare increase for nonstop service.Code-shared tickets in which flights are operated by a carrier other than the ticketingcarrier are excluded. Code-share is an agreement whereby one carrier (an operating carrier)1If a route is defined as flights between cities, then different airports within the same metropolitan areaare considered as a single endpoint of a given city route. For example, a flight that travels between ChicagoO’Hare International Airport (ORD) and Los Angeles International Airport (LAX) is considered to be onthe same route as a flight traveling between Chicago Midway International Airport (MDW) and Los AngelesInternational Airport (LAX). These two flights are considered to travel on different routes if a route is definedas airport pairs. Indeed, Southwest Airlines serves only Midway International Airport (MDW) to depart fromor arrive at Chicago metropolitan area, whereas other legacy carriers serve O’Hare International Airport.66allows the partner (a ticketing carrier) to make reservations and issue tickets for the oper-ating carrier’s flights. Competition between the partners is not straightforward under thecode-share agreement as multiple airlines serve flights jointly, and complicated accountingformula is used to split their profits. Tickets are excluded if the associated carriers changeduring the trip or are recorded unidentified on any segment. For example, if an outboundflight is ticketed (or operated) by American Airlines and the inbound flight is ticketed (oroperated) by Delta Airlines, dropping the return portion of this itinerary may distort themarket share of Delta Airlines. In the data, a carrier recorded as unidentified indicates thatthe trip segment is not operated by an airplane (e.g., ground travel), so the associated ticketsare dropped.Because the route is defined as non-directional origin-and-destination pair, the ticketprice is measured as single-directional fare. The price of a round-trip ticket is calculated ashalf of the reported value, whereas the price of a one-way ticket is used as recorded in thedata. As the directionality is suppressed, the return portions of the round-trip tickets areexcluded from the data to avoid the double-counting issue.To ensure the credibility of the price of the tickets, I exclude tickets with fares recordedas uncredible in the database, as well as tickets with extremely low or high fares. DB1Buses an indicator to identify whether fare value is questionable based on credible limits. Byexcluding those observations from the data, tickets with extremely high fares are dropped.2Tickets with fares below zero are dropped to exclude potential erroneously recorded data.Tickets with fares below $10 are excluded as they are presumed to be discounted tickets forpassengers in frequent flyer programs or airline employees. Tickets with fares above the2Although the database does not report how the fare credibility is determined, a representative from theDOT confirms that tickets with a fare-per-mile value higher than 3 are considered the ones with questionablefares. Thus, excluding tickets with uncredible fares affects the high values in the price distribution, whereasit rarely affects the low values.6799th percentile of the distribution of each carrier-route-quarter combination are excludedto avoid potential bias in the dispersion measures. This drops the unusually high airfares inthe early 1990s.Before calculating the dispersion measures, I restrict the analysis to routes where thecarrier transported at least 100 passengers within the quarter. This ensures that the dis-persion measures are calculated based on a large enough number of observations. Foreach carrier-route-quarter, I calculate the Gini coefficient, mean, and the 10th and 90thpercentile of the ticket prices. The Gini coefficient, a statistic to measure economic in-equality, is adopted by previous literature studying price dispersion in the airline industry.Aggregating individual ticket information into summary statistics yields 190,050 carrier-route-quarter observations as the final sample.The period of entry threat towards a given route is identified by the earliest periodin which Southwest Airlines established a presence in both endpoints of the route; theactual entry is identified by the earliest period in which Southwest Airlines started operatingdirect flights on the route. In the data, a quarter that Southwest Airlines departed from(or arrived at) a given airport (or city) for the first time is identified separately for theorigin and destination, and the period which comes later is coded as the period of entrythreat. Similarly, a quarter in which an observation for a direct flight by Southwest Airlinesappeared in the data for the first time on a given route is coded as the actual entry. AsSouthwest Airlines did not establish an airport in the first quarter of 1993, observations inwhich the period of threatened entry is coded as the very first quarter are excluded from theanalysis. These observations are for the routes already threatened or served by SouthwestAirlines before 1993.3 This paper restricts the analysis to eight major legacy carriers;American, Alaska, Continental, Delta, Northwest, TWA, United, and US Airways.3Table C.8 in the appendix presents the dates Southwest Airlines established its presence at each airport.68Table 4.1: Expansion of Southwest AirlinesNumber of Routes Number of ObservationsSouthwest operating direct service 727 54,182Threatened by Southwest after 1993:Q2 567 45,547Actual entry occurs at least 1 quarter after threat 394 33,405Actual entry occurs at most 3 years after threat 236 23,544Table 4.1 presents summary statistics for the variables about routes in which South-west Airlines operated direct services. For the whole period of interest, Southwest Airlinesoperated direct service on 727 routes at least once. Among those routes, 567 routes expe-rienced actual entry, given that the threat of entry occurred after the first quarter of 1993.4The actual entry occurred at least one quarter after the threat on 394 routes, and the actualentry occurred no later than three years from the threat on 236 routes.Table 4.2: Summary StatisticsVariable Mean MedianFare in the whole industry $171.75 $160.72Gini coefficient 0.228 0.224Number of passengers transported 1,402 857Number of carriers in the market 1.73 1Herfindahl index 0.77 1Market share 0.72 1Time gap between threat of entry and actual entry 14.36 9Table 4.2 presents summary statistics on Southwest Airlines’ entry threat and actualentry and general information on competition in the airline industry. The average time gapbetween entry and threat is 14.36 quarters, and the median is nine quarters. The averageprice for the whole sample and the whole period is, on average $171.75. There is substantialdispersion in the airline ticket prices charged to passengers traveling between the sameorigin and destination pair. The mean Gini coefficient for the whole sample is 0.228. By4As mentioned in the data description, no airport was established by Southwest Airlines. So, a routeindicating that entry threat occurred in the very first period of the sample should not be used in the analysisbecause it implies that Southwest Airlines has been operating since before 1993.69definition, it can be interpreted as half of the mean absolute fare difference normalizedby the average fare value. That is, if we randomly select two passengers from those whotraveled on a given carrier and route, the absolute difference between the ticket prices paidby the two passengers is expected to be about 45.6% of the mean fare on the route servedby the carrier. Markets are mostly monopoly or duopoly.54.4 Empirical SpecificationTo study how the price dispersion of incumbent legacy carriers responds to entry threatand actual entry by Southwest Airlines, I estimate a two-way fixed effects model with timedummies like a standard event study framework. A key identification assumption of theevent study is that there are no other confounding events occurring at the same time as theevent of our interest taking place.The empirical specification includes a set of time dummies to separately evaluate theimpact of potential and actual entry. It also specifies two-way fixed effect terms to capturetime-invariant carrier-route and time-variant effects. The model specification is as follows:yi jt = α+3+∑τ=−8β0,τ threat j,t0+τ +3+∑τ=0βe,τentry j,te+τ + γi j +µt +Xi jtθ + εi jt , (4.1)where yi jt is the variable of interest for incumbent carrier i serving route j in quarter t,such as the Gini coefficient.{threat j,t0+τ}3+τ=−8 is a set of time dummies for each quarter5There is one thing to note that the sample may not be equivalent to the one in Goolsbee and Syverson(2008). They use the data files constructed by Severin Borenstein, which are already aggregated to the carrier-route-quarter observation. Although the detailed sample restriction criteria are not explicitly provided in theirpaper, several apparent differences are still easily found. For example, this paper and theirs are different inrestricting the fare class of the ticket, restricting the minimum number of passengers traveled within eachcarrier-route-quarter observation, and calculating the threshold used to exclude extremely high fares. Thesemay influence the estimation results for the mean fare, generating estimates different from those in theirpaper. It will be discussed briefly in Section 4.70before, during, and after the period of entry threat on a route j, and{entry j,te+τ}3+τ=0 is a setof time dummies for each quarter during and after the period of actual entry. The subscriptst0 and te denote the period of entry threat and actual entry, respectively. As described in theprevious section, each period is identified within the sample. γi j and µt are carrier-routeand year-quarter fixed effects, respectively, and Xi jt is a set of control variables.6For the dependent variable yi jt , the log of average fares, log-odds ratio of the Ginicoefficients, log of Gini coefficients, and log of 10th and 90th percentile fares are used inthe regression.7 As the Gini coefficient has a possible disadvantage of emphasizing relativechanges around the middle part of the distribution while being insensitive towards the tailsof the distribution, 10th and 90th percentile fares are used to complement the estimationresults for the Gini coefficients. Moreover, as the percentile values summarize informationon the shape of the distribution, the estimation results for the 10th and 90th percentile faresmay have the same implication as the results for the dispersion measure.In the baseline specification, observations from three years before and after the threatof entry and three years after the actual entry are used in estimation. To evaluate the impactof entry threat by Southwest Airlines, the regression specification includes dummies foreight quarters before the period of entry threat, which is denoted by t0, and also for theperiod t0 itself. It also includes dummies for two quarters after the period t0 and uses asingle dummy for three or more quarters after the entry threat. Similarly, the specificationincludes a dummy for the period of actual entry, as well as dummies for two quarters afterthe period te, and a single dummy for three or more quarters after the actual entry.6Indicator for periods under bankruptcy, the quarterly data for jet fuel prices, Herfindahl index are usedas control variables in the analysis for comparison. Table C.2 through Table C.6 in the appendix report theestimation results for the regression, including these control variables. Table C.7 in the appendix presents theperiod when the carriers went bankrupt.7The log-odds ratio of the Gini coefficient is defined as ln[Ginii jt1−Ginii jt]. This transformation is used inprevious studies to produce unbounded statistics from the Gini coefficient, which is, by definition, boundedbetween zero and one.71Regarding time dummies for quarters after the threat of entry, they take the value ofone only if Southwest Airlines has established both endpoints but has yet to operate directflights on a route. For example, if Southwest Airlines enters a route j in the second quar-ter after the threat of entry, then threat j,t0+1 takes the value of one, while threat j,t0+2 andthreat j,t0+3+ take value of zero. In this example, entry j,te takes the value of one in periodt0 +2, which is now equivalent to period te. Regarding time dummies for quarters after theactual entry, they take the value of one only if Southwest Airlines starts operating directflights on a given route no later than twelve quarters after the period of entry threat.The four quarters in the third year (i.e., from the twelfth quarter through the ninthquarter) prior to the threat of entry are excluded periods, and these are the basis periodfor the estimation. Hence, estimated coefficients can be interpreted as relative sizes of thedependent variable in the period when the corresponding dummy takes the value of one,compared to the mean value of the dependent variable during the excluded quarters. Sincea single dummy is used for four or more quarters after t0 or te, estimated coefficients forthese periods can be interpreted as the average effect during these periods relative to theexcluded periods.4.5 Estimation ResultsTable 4.3 presents the estimation results. As mentioned above, the dependent variables arein logarithmic terms. The estimated coefficient for βτ can be recovered in a percentage termby calculating exp(βˆτ)−1. The estimated coefficients can be interpreted as the relative sizeof the dependent variable in each period compared to that in the excluded period.72Table 4.3: Estimation Results(1) (2) (3) (4) (5)ln(meanfare) log-odds Gini ln(Gini) ln(P10) ln(P90)threatt0−8 −0.0215 −0.0238 −0.0200 −0.00853 −0.0186(0.0144) (0.0203) (0.0157) (0.0132) (0.0196)threatt0−7 −0.0392∗∗∗ −0.0227 −0.0180 −0.00220 −0.0316(0.0142) (0.0214) (0.0162) (0.0155) (0.0209)threatt0−6 −0.0309∗ 0.0301 0.0206 −0.0255 −0.00687(0.0179) (0.0213) (0.0159) (0.0175) (0.0231)threatt0−5 −0.00403 0.0250 0.0158 0.0136 0.0351(0.0209) (0.0261) (0.0194) (0.0197) (0.0275)threatt0−4 −0.0232 0.0183 0.0106 0.00102 0.0290(0.0220) (0.0305) (0.0230) (0.0219) (0.0298)threatt0−3 −0.0313 0.0151 0.0109 −0.00323 −0.00870(0.0226) (0.0305) (0.0228) (0.0236) (0.0276)threatt0−2 −0.0415∗ −0.0216 −0.0200 0.0000832 −0.0265(0.0248) (0.0327) (0.0242) (0.0263) (0.0305)threatt0−1 −0.0493∗ −0.0392 −0.0339 −0.0155 −0.0532(0.0262) (0.0369) (0.0273) (0.0267) (0.0330)threatt0 −0.0802∗∗∗ −0.0400 −0.0332 −0.0433 −0.0752∗∗(0.0285) (0.0418) (0.0311) (0.0319) (0.0356)threatt0+1 −0.0948∗∗∗ −0.109∗∗∗ −0.0886∗∗∗ −0.0266 −0.119∗∗∗(0.0298) (0.0414) (0.0308) (0.0330) (0.0361)threatt0+2 −0.139∗∗∗ −0.0933∗∗ −0.0759∗∗ −0.0531 −0.159∗∗∗(0.0316) (0.0441) (0.0328) (0.0345) (0.0395)threatt0+3:+12 −0.150∗∗∗ −0.122∗∗ −0.0955∗∗∗ −0.0567 −0.171∗∗∗(0.0363) (0.0482) (0.0360) (0.0395) (0.0452)entryte −0.211∗∗∗ −0.0472 −0.0405 −0.161∗∗∗ −0.206∗∗∗(0.0410) (0.0564) (0.0423) (0.0433) (0.0490)entryte+1 −0.267∗∗∗ −0.0557 −0.0471 −0.193∗∗∗ −0.261∗∗∗(0.0450) (0.0593) (0.0444) (0.0458) (0.0537)entryte+2 −0.294∗∗∗ −0.0710 −0.0589 −0.205∗∗∗ −0.287∗∗∗(0.0472) (0.0622) (0.0467) (0.0500) (0.0557)entryte+3:+12 −0.294∗∗∗ −0.0686 −0.0558 −0.223∗∗∗ −0.294∗∗∗(0.0501) (0.0674) (0.0499) (0.0544) (0.0597)Carrier-route fixed effects, year-quarter fixed effects and constant term are suppressed.Standard errors reported in parentheses are clustered by carrier-route.∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001734.5.1 Impact of Competition on Average FaresColumn 1 of Table 4.3 presents the estimation results for the average logged ticket pricescharged by incumbent legacy carriers. The estimated coefficients are negative in periodssurrounding the entry threat and actual entry by Southwest Airlines. By the time SouthwestAirlines establishes its presence in both endpoints of routes (period t0), the average pricesare 7.7% (exp(−0.0802) = 0.923) lower than those in the excluded period. The averageprices fall further by 14% (exp(−0.150) = 0.860) compared to the excluded period onroutes in which Southwest Airlines establishes its presence in both endpoints but does notstart operating direct flights for at least three quarters.The average prices are lower in quarters before t0 compared to the excluded period, andthe estimated coefficients are statistically significant at the 10% level in t0− 2 and t0− 1.This suggests that incumbents cut their prices before Southwest Airlines establishes a pres-ence in both endpoints when they first realize the chances of entry by Southwest Airlineshave risen. Once Southwest Airlines starts operating direct flights (period te), the averageprices are 19% (exp(−0.211) = 0.810) below the excluded period and continue to fall by25.5% (exp(−0.294) = 0.745) as of the end of the estimation period. The estimated coef-ficients are statistically significant at the 1% level. The difference between the preemptiveprice drop of 7.7% and the ultimate price drop of 25.5% indicates the price drop due toactual entry by Southwest Airlines.The results suggest a negative effect of competition on average fares. Incumbent legacycarriers cut ticket prices substantially in response to potential and actual entry by SouthwestAirlines. The price drops on the routes with the chances of Southwest Airlines’ entry,suggesting preemptive actions by incumbents. The results align with the main results ofGoolsbee and Syverson (2008), although the magnitude of estimates is smaller than theirs74in general.4.5.2 Impact of Competition on Price DispersionColumns 2 and 3 of Table 4.3 present the results for the price dispersion measures. Columns2 and 3 show how the log-odds ratio and the logarithm of the Gini coefficient respond topotential and actual entries by Southwest Airlines. One thing to note is that price dispersiondeclines significantly when incumbents face the risk of entry by Southwest Airlines. OnceSouthwest Airlines establishes its presence in both endpoints without actually entering theroute, both of the dispersion measures decrease and continue to decline until the end of theestimation period.Compared to the excluded period, the absolute difference between the fares paid bytwo random passengers becomes less when incumbents face an entry threat by SouthwestAirlines, and the magnitudes of the declines are statistically significant. However, onceSouthwest Airlines actually enters the market, the price dispersion in that route increasesto the pre-entry threat level. The results contribute empirical evidence to the literaturesuggesting a negative relationship between competition and price dispersion, in the sensethat incumbents respond preemptively to the expansion of Southwest Airlines.4.5.3 Impact of Competition on 10th and 90th Percentile FaresTo unpack the results for price dispersion measures given in columns 2 and 3 of Table 4.3, Iestimate the model for the 10th and 90th percentile of the price distribution. This allows meto observe whether incumbents’ response toward potential and actual entry by SouthwestAirlines is heterogeneous across full and discount fares. Columns 4 and 5 of Table 4.3present the results.75Similar to the results for average fares, the estimates are negative in quarters surround-ing potential and actual entry by Southwest Airlines. Facing the entry threat by SouthwestAirlines, incumbents lower the 10th percentile fares by 4.2% and lower the 90th percentilefares by 7.2% compared to the excluded period. If Southwest Airlines established bothendpoints of a route but did not enter the route for at least three quarters and at most threeyears, 10th percentile fares fall by 5.5% while 90th percentile fares fall by 15.7% comparedto the excluded period. Once Southwest Airlines actually enters a route, the 10th percentilefares are 14.9% lower than the excluded period, and the 90th percentile fares are 18.6%lower than the excluded period. Both percentile fares continue to fall by 20% and 25.5%,respectively, until the end of the estimation period.Over the 25-quarter window, the 90th percentile fares tend to decrease more than the10th percentile fares. Especially for the periods after the threat of entry (i.e., from t0 throught0 + 3:+12), the difference between the estimates for the percentile fares is considerable.The 90th percentile fares fall, on average, three times more than the 10th percentile faresdo. In contrast, the difference between the estimates for the percentile fares is smaller in theperiods after actual entry. The 90th percentile fares still fall more than the 10th percentilefares do, however, the size of the estimates for 90th percentile fares are now, on average,1.3 times greater than that of 10th percentile fares. The estimates for the 90th percentilefares are statistically significant at the 1% level both for the periods during and after entrythreat and for the periods during and after actual entry, whereas the estimates for the 10thpercentile fares are statistically significant only for the periods during and after actual entry.Combined with the results for price dispersion measures, the results suggest that thedecline in price dispersion in the route threatened by Southwest Airlines’ entry is driven bya substantial decrease in the 90th percentile of the price distribution. This aligns with thestrategic entry deterrence argument that incumbents cut prices to make entry less attractive76for potential entrants. The results also suggest that the increase in price dispersion to itspre-entry threat levels is driven by a considerable decrease in the 10th percentile of the pricedistribution as well as a continued decrease in the 90th percentile of the price distribution.This implies a strategic response of incumbents to avoid losing price-sensitive passengersto their low-cost rival, who is now directly competing with them after the actual entry.The results suggest that increased competition via the expansion of a low-cost entrant canboth increase and decrease price dispersion, and it is important to understand the strategicmotive to understand the relationship between competition and price dispersion.4.6 ConclusionThis paper examines the effect of potential entry on the incumbents’ price dispersion asdistinct from actual entry. Using data from the U.S. airline industry, I show competitioncan both increase and decrease price dispersion, depending on the strategic motive. Facingpotential entry by Southwest Airlines, incumbent carriers cut the 90th percentile fare ofthe price distribution, which drives a substantial decline in price dispersion. This resultaligns with the limit pricing practice to make entry less attractive. I also find that whenSouthwest Airlines actually enters a route, incumbents cut down 10th percentile fares ofthe price distribution, increasing price dispersion to its pre-entry threat levels. This findingaligns with the firm’s motive to compete with the entrant to prevent losing customers.This paper suggests empirical evidence to the literature on the relationship betweencompetition and price dispersion. The results suggest the importance of timing and strate-gic motive to understand the relationship. Future research may include building a theo-retical framework incorporating the limit pricing motive in the pre-entry periods and thecompetition motive in the post-entry periods.77Chapter 5ConclusionThis dissertation consists of three chapters that empirically study competition, innovation,and pricing decisions with a focus on the pharmaceutical and airline industries. Threeessays aim to understand the responses and strategic motives of firms facing competitiveshocks.The second and third chapters study pharmaceutical drug development, a crucial inno-vation activity that improves public health and saves lives. The second chapter studies theimpact of mergers on innovation outcomes of the drugs acquired in the deals. I make use ofa novel dataset with detailed information about the drug development process and mergeractivity. The main findings of this essay can be summarized as follows. First, mergersnegatively impact the innovation outcomes of acquired drugs. Second, unlike Cunninghamet al. (2021), the overlap in drug portfolios of consolidating firms is not driving the nega-tive impact of mergers on innovation. A lessening of competition within a very narrowlydefined market is not driving the results. Third, the negative impact of mergers is drivenby their negative impact on the scientific process of drug development. These results com-78bined suggest that the pharmaceutical scientists or the R&D department may suffer duringthe integration process, which relates more to the organizational effects of mergers. Thisessay contributes empirical evidence to the literature with mixed results and is informativefor policymakers in that it studies whether a transaction will create the right incentives forinnovation.In the third chapter, my coauthors and I study the impact of weakened intellectual prop-erty rights on innovation using drug development data. We use the threat of compulsorylicensing on HIV drug therapies starting in 1997 as the basis of our identification strategy.By implementing the synthetic control method (Abadie and Gardeazabal 2003; Abadieet al. 2010, 2015), we find that the development of HIV drug therapies declines when phar-maceutical firms, the patent holders, face weakened patent rights via compulsory licensing.The results suggest a trade-off of using compulsory licenses to tackle public health crises.Practicing such legislative measures can save lives and therefore have great benefits in theshort run. However, our results suggest that those measures weakening patent rights maydiscourage the development of new drug therapies that could save more lives in the fu-ture. This evidence should be considered in future debates on the impacts of compulsorylicensing.The fourth chapter examines the pricing decision of incumbents facing potential andactual entry by a low-cost competitor. The U.S. airline industry data is used to measureprice dispersion. I exploit the aggressive expansion of Southwest Airlines to identify thecompetitive threats and threatened routes (Goolsbee and Syverson, 2008). The results sug-gest that competition can both increase and decrease price dispersion. When SouthwestAirlines threatens to enter a route, incumbents cut down the prices of the expensive tick-ets, making price dispersion declines in that route. Incumbents cut down the cheap ticketsafter Southwest Airlines flies the route, increasing price dispersion to its pre-entry threat79levels. The results suggest that incumbents make entry less attractive for their rivals fac-ing entry threats. After the low-cost competitors enter, incumbents fight for price-sensitiveconsumers. Thus, timing and strategic motive are essential to understand the relationshipbetween competition and price dispersion. This essay contributes empirical evidence tothe literature with mixed results and suggests the existence of preemptive strategic entrydeterrence in the U.S. airline industry.80BibliographyAbadie, A., Diamond, A., and Hainmueller, J. (2010). Synthetic control methods forcomparative case studies: Estimating the effect of california’s tobacco control program.Journal of the American statistical Association, 105(490):493–505. → pages36, 37, 45, 79Abadie, A., Diamond, A., and Hainmueller, J. (2015). Comparative politics and thesynthetic control method. American Journal of Political Science, 59(2):495–510. →pages 36, 37, 45, 79Abadie, A. and Gardeazabal, J. (2003). The economic costs of conflict: A case study ofthe basque country. American economic review, 93(1):113–132. → pages 36, 37, 45, 79Acemoglu, D. and Linn, J. (2004). Market size in innovation: Theory and evidence fromthe pharmaceutical industry. The Quarterly Journal of Economics, 119(3):1049–1090.→ page 40Aghion, P. and Bolton, P. (1987). Contracts as a barrier to entry. The American EconomicReview, 77(3):388–401. → page 64Ahuja, G. and Katila, R. (2001). Technological acquisitions and the innovationperformance of acquiring firms: a longitudinal study. Strategic management journal,22(3):197–220. → page 11Anderson Jr, H. E. (2010). We can work it out: Co-op compulsory licensing as the wayforward in improving access to anti-retroviral drugs. BUJ Sci. & Tech. L., 16:167. →page 35Arrow, K. (1962). Economic welfare and the allocation of resources for invention. In Therate and direction of inventive activity: Economic and social factors, pages 609–626.Princeton University Press. → page 11Baten, J., Bianchi, N., and Moser, P. (2017). Compulsory licensing andinnovation–historical evidence from german patents after wwi. Journal of DevelopmentEconomics, 126:231–242. → page 3981Beall, R. and Kuhn, R. (2012). Trends in compulsory licensing of pharmaceuticals sincethe doha declaration: a database analysis. PLoS medicine, 9(1):e1001154. → page 38BENA, J. and LI, K. (2014). Corporate Innovations and Mergers and Acquisitions. TheJournal of Finance, 69(5):1923–1960. → pages 11, 12Bennett, R., Craun, J., and of Transportation. Office of Aviation Analysis, U. S. D. (1993).The Airline Deregulation Evolution Continues: The Southwest Effect. United StatesDepartment of Transportation, Office of Aviation Analysis. → page 64Bonaime, A. A. and Wang, Y. E. (2019). Mergers, product prices, and innovation:evidence from the pharmaceutical industry. Available at SSRN 3445753. → page 11Bond, E. W. and Saggi, K. (2017a). Bargaining over entry with a compulsory licensedeadline: price spillovers and surplus expansion. American Economic Journal:Microeconomics, 9(1):31–62. → page 38Bond, E. W. and Saggi, K. (2017b). Compulsory licensing and patent protection: anorth-south perspective. The Economic Journal, 128(610):1157–1179. → page 38Bond, E. W. and Samuelson, L. (2019). Bargaining with private information and theoption of a compulsory license. Games and Economic Behavior. → page 38Borenstein, S. (1985). Price discrimination in free-entry markets. The RAND Journal ofEconomics, 16(3):380–397. → page 63Borenstein, S. and Rose, N. L. (1994). Competition and price dispersion in the u.s. airlineindustry. Journal of Political Economy, 102(4):653–683. → pages 5, 60, 63, 64, 66Brueckner, J. K., Lee, D., and Singer, E. S. (2013). Airline competition and domestic usairfares: A comprehensive reappraisal. Economics of Transportation, 2(1):1–17. →page 64Budish, E., Roin, B. N., and Williams, H. (2015). Do firms underinvest in long-termresearch? evidence from cancer clinical trials. American Economic Review,105(7):2044–85. → page 39Chakrabarty, D. and Kutlu, L. (2014). Competition and price dispersion in the airlinemarkets. Applied Economics, 46(28):3421–3436. → page 64Chaudhuri, S., Goldberg, P. K., and Gia, P. (2006). Estimating the effects of global patentprotection in pharmaceuticals: a case study of quinolones in india. American EconomicReview, 96(5):1477–1514. → page 38Chien, C. (2003). Cheap drugs at what price to innovation: does the compulsory licensingof pharmaceuticals hurt innovation. Berkeley Tech. LJ, 18:853. → page 3982Cockburn, I. M., Lanjouw, J. O., and Schankerman, M. (2016). Patents and the globaldiffusion of new drugs. The American Economic Review, 106(1):136–164. → page 38Comanor, W. S. and Scherer, F. (2013). Mergers and innovation in the pharmaceuticalindustry. Journal of Health Economics, 32(1):106–113. → page 11Cunningham, C., Ederer, F., and Ma, S. (2021). Killer Acquisitions. Journal of PoliticalEconomy, 129(3):649–702. → pages 3, 8, 10, 11, 12, 32, 78Dai, M., Liu, Q., and Serfes, K. (2014). Is the effect of competition on price dispersionnonmonotonic? evidence from the u.s. airline industry. The Review of Economics andStatistics, 96(1):161–170. → pages 64, 66Danzon, P. M., Epstein, A., and Nicholson, S. (2007). Mergers and acquisitions in thepharmaceutical and biotech industries. Managerial and Decision Economics,28(4–5):307–328. → page 11Danzon, P. M., Wang, Y. R., and Wang, L. (2005). The impact of price regulation on thelaunch delay of new drugs—evidence from twenty-five major markets in the 1990s.Health economics, 14(3):269–292. → page 38DiMasi, J. A., Grabowski, H. G., and Hansen, R. W. (2016a). Innovation in thepharmaceutical industry: New estimates of R&D costs. Journal of Health Economics,47:20–33. → page 15DiMasi, J. A., Grabowski, H. G., and Hansen, R. W. (2016b). Innovation in thepharmaceutical industry: new estimates of r&d costs. Journal of health economics,47:20–33. → page 40Dixit, A. (1979). A model of duopoly suggesting a theory of entry barriers. The BellJournal of Economics, 10(1):20–32. → page 64Dresner, M., Lin, J.-S. C., and Windle, R. (1996). The impact of low-cost carriers onairport and route competition. Journal of Transport Economics and Policy,30(3):309–328. → page 64Dubois, P., de Mouzon, O., Scott-Morton, F., and Seabright, P. (2015). Market size andpharmaceutical innovation. The RAND Journal of Economics, 46(4):844–871. → page40Duggan, M., Garthwaite, C., and Goyal, A. (2016). The market impacts of pharmaceuticalproduct patents in developing countries: Evidence from india. American EconomicReview, 106(1):99–135. → page 38Entezarkheir, M. and Moshiri, S. (2018). Mergers and innovation: evidence from a panelof US firms*. Economics of Innovation and New Technology, 27(2):132–153. → page1183Feijoo, F., Palopoli, M., Bernstein, J., Siddiqui, S., and Albright, T. E. (2020). Keyindicators of phase transition for clinical trials through machine learning. DrugDiscovery Today, 25(2):414–421. → page 88Feldman, J. (2009). Compulsory licenses: the dangers behind the current practice. J. Int’lBus. & L., 8:137. → page 42Finkelstein, A. (2004). Static and dynamic effects of health policy: Evidence from thevaccine industry. The Quarterly Journal of Economics, 119(2):527–564. → page 39Gaggero, A. A. and Piga, C. A. (2011). Airline market power and intertemporal pricedispersion. The Journal of Industrial Economics, 59(4):552–577. → page 64Gale, I. (1993). Price dispersion in a market with advance-purchases. Review of IndustrialOrganization, 8(4):451–464. → page 63Gallini, N. (2017). Do patents work? thickets, trolls and antibiotic resistance. CanadianJournal of Economics/Revue canadienne d’e´conomique, 50(4):893–926. → page 39Gerardi, K. and Shapiro, A. (2009). Does competition reduce price dispersion? newevidence from the airline industry. Journal of Political Economy, 117(1):1–37. →pages 64, 66Giaume, S. and Guillou, S. (2004). Price discrimination and concentration in europeanairline markets. Journal of Air Transport Management, 10(5):305–310. → page 64Goolsbee, A. and Syverson, C. (2008). How do incumbents respond to the threat of entry?evidence from the major airlines. The Quarterly Journal of Economics,123(4):1611–1633. → pages 5, 62, 65, 66, 70, 74, 79Grabowski, H. and Kyle, M. (2008). Mergers and Alliances in Pharmaceuticals: Effectson Innovation and R&D Productivity. Edward Elgar Publishing. → page 11Harris, D. (2010). Trips after fifteen years: Success or failure, as measured by compulsorylicensing. J. Intell. Prop. L., 18:367. → page 38Haucap, J., Rasch, A., and Stiebale, J. (2019). How mergers affect innovation: Theory andevidence. International Journal of Industrial Organization, 63:283–325. → page 11Hay, M., Thomas, D. W., Craighead, J. L., Economides, C., and Rosenthal, J. (2014).Clinical development success rates for investigational drugs. Nature Biotechnology,32(1):40–51. → page 88Hoen, E., Berger, J., Calmy, A., and Moon, S. (2011). Driving a decade of change:Hiv/aids, patents and access to medicines for all. Journal of the International AIDSSociety, 14(1):15. → pages 36, 41, 4284Holmes, T. J. (1989). The effects of third-degree price discrimination in oligopoly. TheAmerican Economic Review, 79(1):244–250. → page 63Kang, G. S. (2019). The Effects of Technological Similarity and Diversity on Merger andInnovation. SSRN Electronic Journal. → pages 11, 12Klemperer, P. (1987). Entry deterrence in markets with consumer switching costs. TheEconomic Journal, 97:99–117. → page 64Klemperer, P. (1989). Price wars caused by switching costs. The Review of EconomicStudies, 56(3):405–420. → page 64Klemperer, P. (1995). Competition when consumers have switching costs: An overviewwith applications to industrial organization, macroeconomics, and international trade.The Review of Economic Studies, 62(4):515–539. → page 64Kyle, M. K. (2006). The role of firm characteristics in pharmaceutical product launches.The RAND journal of economics, 37(3):602–618. → pages 38, 42Kyle, M. K. (2007). Pharmaceutical price controls and entry strategies. The Review ofEconomics and Statistics, 89(1):88–99. → page 38Kyle, M. K. and McGahan, A. M. (2012). Investments in pharmaceuticals before and aftertrips. Review of Economics and Statistics, 94(4):1157–1172. → page 39Maksimovic, V. and Phillips, G. (2001). The market for corporate assets: Who engages inmergers and asset sales and are there efficiency gains? The Journal of finance (NewYork), 56(6):2019–2065. → page 12McMillan, J. (2003). Reinventing the bazaar: A natural history of markets. WW Norton& Company. → page 35Milgrom, P. and Roberts, J. (1982). Limit pricing and entry under incomplete information:An equilibrium analysis. Econometrica, 50(2):443–459. → page 64Mohapatra, D. and Chatterjee, C. (2015). Price control and access to drugs: The case ofindia’s malaria market. Technical report, Working Paper. Cornell University. → page 38Morrison, S. A. (2001). Actual, adjacent, and potential competition: Estimating the fulleffect of southwest airlines. Journal of Transport Economics and Policy,35(2):239–256. → page 64Moser, P. and Voena, A. (2012). Compulsory licensing: Evidence from the trading withthe enemy act. American Economic Review, 102(1):396–427. → page 39Ornaghi, C. (2009). Mergers and innovation in big pharma. International Journal ofIndustrial Organization, 27(1):70–79. → pages 11, 1285Ramani, S. V. and Urias, E. (2015). Access to critical medicines: When are compulsorylicenses effective in price negotiations? Social Science & Medicine, 135:75–83. →page 38Rigamonti, C. P. et al. (2005). The south africa aids controversy: A case study in patentlaw and policy. → page 35Ringel, M. S. and Choy, M. K. (2017). Do large mergers increase or decrease theproductivity of pharmaceutical R&D? Drug Discovery Today, 22(12):1749–1753. →page 11Sampat, B. and Williams, H. L. (2019). How do patents affect follow-on innovation?evidence from the human genome. American Economic Review, 109(1):203–36. →page 39Scherer, F. M. and Watal, J. (2002). Post-trips options for access to patented medicines indeveloping nations. Journal of International Economic Law, 5(4):913–939. → page 39Schumpeter, J. (1942). Capitalism, Socialism, and Democracy. New York: Harper. →page 11Stavins, J. (2001). Price discrimination in the airline market: The effect of marketconcentration. The Review of Economics and Statistics, 83(1):200–202. → page 64Stavropoulou, C. and Valletti, T. (2015). Compulsory licensing and access to drugs. TheEuropean Journal of Health Economics, 16(1):83–94. → page 39Stern, A. D. (2017). Innovation under regulatory uncertainty: evidence from medicaltechnology. Journal of public economics, 145:181–200. → page 40Tan, K. M. (2016). Incumbent response to entry by low-cost carriers in the u.s. airlineindustry. Southern Economic Journal, 82(3):874–892. → pages 65, 66Watzinger, M., Fackler, T. A., Nagler, M., and Schnitzer, M. (2017). How antitrustenforcement can spur innovation: Bell labs and the 1956 consent decree. → page 39Windle, R. and Dresner, M. (1999). Competitive responses to low cost carrier entry.Transportation Research Part E: Logistics and Transportation Review, 35(1):59–75. →page 64Wouters, O. J., McKee, M., and Luyten, J. (2020). Estimated Research and DevelopmentInvestment Needed to Bring a New Medicine to Market, 2009-2018. JAMA,323(9):844–853. → page 1586Appendix AChapter 2 AppendicesA.1 Drug Development DataA.1.1 BioMedTrackerBioMedTracker is a comprehensive database maintained by Informa Pharma Intelligencethat tracks the development milestones of novel drug candidates at the indication level forwhich they seek approval. The database records the occurrence and timing of detailedevents throughout the clinical development and commercialization process. Besides thedevelopment-related events and activities, it records company partnership-related eventssuch as mergers & acquisitions, drug acquisitions, and licensing deals associated with eachdrug candidate. The raw data also contains pharmacological information on each drugproject, such as the alternative and generic names, biological targets, FDA drug classifica-tion, molecule types, and route of administration. The data vendor collects the informationfrom publicly available sources such as the company press releases, medical conference87presentations, public company SEC filings, individual inquiries to company investor re-lations, company websites, and slide presentations.1 The database is available for paidsubscribers. The dataset used in the empirical analysis in this paper is constructed basedon the raw data downloaded on June 5, 2020. The earliest date recorded in the database isJanuary 17, 1985.The database consists of six data files described as follows. The “Events” file is the mostsubstantial for this paper, which tracks the clinical development and regulatory history ofeach drug project. Other data files in BioMedTracker provide the necessary informationfor the empirical analysis, including the upper-level categorization of the drug indications,drug characteristics, and alternative drug names. The “Clinical Trials” file contains thesponsors, drug indications, initiation and completion dates, dosage information, patient de-scriptions, unique identifier, and clinical trial phase. The list of all firms ever appear in theraw data is recorded in the “Companies” file, which contains information such as the cityand countries where they are located, subsidiaries, old names, and company descriptions.The brief history of each company is summarized in the description field. The informationis used in manual checking to find the M&A transactions that match the information in theSDC Platinum. The “Drugs” file summarizes specific information in the “Clinical Trials”and “Events” files at the drug level. It is combined with the “Company Products” file togenerate the cleaned list of all alternative and generic names of the drugs and the drugcharacteristics. Lastly, the “Drug Sales” file records the dollar amounts of the drug sales indifferent regions.1See Informa Business Intelligence BioMedTracker Research and Analyst Conduct Policies and Proce-dures (https://www.biomedtracker.com/Standards.cfm), Hay et al. (2014), Feijoo et al. (2020).88A.1.2 Categorization of Event TypesBioMedTracker uses 271 detailed event types to track the development process of drugprojects.2 By referring to the FDA, EMA, and other relevant resources, I categorize theevent types into the following three groups: development events, discontinuation events,and neutral events. The development events are the ones capturing the firm’s effort tomake progress in developing the drug project, whereas discontinuation events are the onescapturing the firm’s effort to discontinue the project development. The event types relatedto the announcements or decisions made by the regulatory authority, such that they havelittle or no impact on drug development, are categorized as neutral events.Table A.1 presents the list of event types categorized as development or discontinuationevents. The neutral events are not listed in this table for convenience in reporting. 95event types are classified as development events, and the remaining 10 event types arecategorized as discontinuation events. The development events are grouped further into 23science-related and 72 commercialization-related events.As shown in Table A.2, I categorize the science-related event types into the follow-ing two groups: the events or activities that are induced by the firms’ underlying strategicmotives (e.g., the announcement of plans to initiate the new clinical studies, the actual initi-ation of the patient enrollment and dosing, conducting clinical studies under the ExpandedAccess Program,3 etc.) and those happen during the merely scientific development pro-2Compared to 28 event types (or 36, including acquisitions or other partnership-related event types)recorded in Pharmaprojects, 271 event types in BioMedTracker provide us more detailed information on thedrug development history and enable us to disentangle the underlying motive or reasons in the developmentactivities and events.3The expanded access program is a way for a patient with severe and life-threatening disease or conditionto get access to an investigational medical product (which has not been approved yet and is still subject toclinical trials) when no alternative therapy options are available. A company willing to provide its medicalproduct (subject to clinical trials) submits the expanded access request to FDA and provides the necessaryinformation. In this sense, the relevant event type is considered a science-related development event withunderlying strategic motives of the company.89cedures (e.g., progress and completion of each step in the clinical studies, obtaining theintermediate or final data and results, etc.). Among 23 science-related development eventtypes, 5 are classified into the former group, and the remaining 17 are classified into thelatter group.I divide the discontinuation events into 8 events caused by non-clinical reasons (e.g.,cease of development or withdrawal of applications due to lack of funds or a change instrategy, etc.) and 2 events caused by clinical reasons (e.g., cease of patient enrollment,failure to reach the endpoints of clinical trials, lack of statistical significance of the clinicaltrial results, etc.). Table A.3 presents the list of discontinuation events and correspondingreasons.In categorizing the event types, I carefully consider whether the events are induced byfirms. For instance, development events include filing for drug applications or requestingdrug designations (e.g., Orphan Drug, Fast Track, Accelerated Approval, or BreakthroughTherapy, etc.). In contrast, accepting the firms’ requests or granting approvals are consid-ered neutral events since the regulatory authorities make those decisions. Similarly, thesuspension of the development process is classified as a neutral event if it is determined bylaw or the regulatory authorities (e.g., a hold of programs and clinical trials, a lift of thehold of clinical trials, etc.).I use these categorizations to construct the innovation outcomes as the occurrence ofeach type of event in a given time period. I aggregate the data at the drug-quarter level togenerate the dependent variables for the empirical analysis (e.g., development versus dis-continuation, science-related versus commercialization-related development, and science-related activities with and without strategic motives).90Table A.1: Categorization of Event TypesEvent Group Event Type CategoryOther Drug Name Change DevelopmentMarketing Discontinuation DiscontinuationProgress Update Hospital/Health System Product Launch DevelopmentManufacturing/Supply DevelopmentManufacturing/Supply (Emerging Markets) DevelopmentProduct Launch (Australia/New Zealand) DevelopmentProduct Launch (Canada) DevelopmentProduct Launch (Emerging Markets) DevelopmentProduct Launch (Europe) DevelopmentProduct Launch (Europe) - Individual Country DevelopmentProduct Launch (Japan) DevelopmentProduct Launch (U.S.) DevelopmentProduct Launch Generic (U.S.) DevelopmentProduct Relaunch DevelopmentProgram Hold DiscontinuationSuspension DiscontinuationSuspension (Emerging Markets) DiscontinuationRegulatory 510(k) Filing DevelopmentANDA Filing DevelopmentANDA Paragraph IV Filing DevelopmentAdvanced Therapy Medicinal Product (ATMP) Classification DevelopmentAppeal to Approvable Letter DevelopmentAppeal to Complete Response Letter (CRL) DevelopmentAppeal to Non-Approvable Letter DevelopmentBiosimilar 351(k) Filing DevelopmentBreakthrough Therapy Designation (U.S.) DevelopmentBreakthrough Therapy Designation Filing (U.S.) Development91Event Group Event Type CategoryCE Mark Filing DevelopmentCompassionate Use DevelopmentDecentralized Filing (Europe) DevelopmentEmergency Use Authorization (EUA) Submission DevelopmentEuropean Accelerated Assessment (CHMP) DevelopmentFast Track Status DevelopmentFile Over Protest DevelopmentFiling for Approval (Australia/New Zealand) DevelopmentFiling for Approval (Canada) DevelopmentFiling for Approval (Emerging Markets) DevelopmentIND Filing DevelopmentIND Filing (Emerging Markets) DevelopmentIndividual Country Filing (Europe) DevelopmentJ-NDA Filing (Japan) DevelopmentJ-NDA Withdrawal (Japan) DiscontinuationMAA Submission (Europe) DevelopmentMAA Withdrawal DiscontinuationMutual Recognition Filing (Europe) DevelopmentNCE Exclusivity (5 years) DevelopmentNDA/BLA Filing DevelopmentNDA/BLA Withdrawal DiscontinuationOrphan Drug Designation (Australia) DevelopmentOrphan Drug Designation (Europe) DevelopmentOrphan Drug Designation (Japan) DevelopmentOrphan Drug Designation (U.S.) DevelopmentPRIME Designation (Europe) DevelopmentPediatric Exclusivity DevelopmentPriority Review Development92Event Group Event Type CategoryPriority Review (Japan) DevelopmentPriority Review for sNDA/sBLA DevelopmentQualified Infectious Disease Product (QIDP) Designation DevelopmentRTOR Approval (U.S.) DevelopmentRTOR Approval for sNDA/sBLA DevelopmentRare Pediatric Disease (RPD) Designation DevelopmentRegenerative Medicine Advanced Therapy (RMAT) Designation DevelopmentRequest for Formal Dispute Resolution DevelopmentResponse Submitted to Approvable Letter DevelopmentResponse Submitted to Approvable Letter to sNDA/sBLA DevelopmentResponse Submitted to Complete Response Letter (CRL) DevelopmentResponse Submitted to Complete Response Letter (CRL) to sNDA/sBLA DevelopmentResponse Submitted to Non-Approvable Letter DevelopmentResponse to European Medicines Agency DevelopmentRolling NDA/BLA Completed DevelopmentRolling NDA/BLA Initiated DevelopmentRolling sNDA/sBLA Completed DevelopmentRx-to-OTC Filing (U.S.) DevelopmentSupplemental Biosimilar 351(k) Filing DevelopmentSupplemental Filing (Canada) DevelopmentSupplemental Filing (Emerging Markets) DevelopmentSupplemental Filing (Europe) DevelopmentSupplemental Filing (Japan) DevelopmentsNDA/sBLA Filing DevelopmentsNDA/sBLA Withdrawal DiscontinuationTrial Announcement Trial Announcement DevelopmentTrial Announcement (Emerging Markets) DevelopmentDosing Completed Development93Event Group Event Type CategoryExpanded Access Program DevelopmentInitiation DevelopmentInitiation (Emerging Markets) DevelopmentPatient Enrollment Completed DevelopmentPatient Enrollment Completed (Emerging Markets) DevelopmentProtocol Amendment DevelopmentTrial Completed DevelopmentTrial Completed (Emerging Markets) DevelopmentTrial Progressing DevelopmentTrial Progressing (Emerging Markets) DevelopmentTrial/Enrollment Reinitiated DevelopmentTrial/Enrollment Suspension DiscontinuationTrial Data Trial Data DevelopmentTrial Data (Emerging Markets) DevelopmentFinal Results DevelopmentPreclinical Results DevelopmentPublished Results DevelopmentRetrospective Analysis DevelopmentSubgroup Analysis DevelopmentSuspension DiscontinuationTop-Line Results DevelopmentUpdated Results Development94Table A.2: Strategic Motives in Scientific ActivitiesEvent Group Event Type Strategic Motive?Trial Announcement Trial Announcement YesTrial Announcement (Emerging Markets) YesDosing Completed NoExpanded Access Program YesInitiation YesInitiation (Emerging Markets) YesPatient Enrollment Completed NoPatient Enrollment Completed (Emerging Markets) NoProtocol Amendment NoTrial Completed NoTrial Completed (Emerging Markets) NoTrial Progressing NoTrial Progressing (Emerging Markets) NoTrial/Enrollment Reinitiated NoTrial Data Trial Data NoTrial Data (Emerging Markets) NoFinal Results NoPreclinical Results NoPublished Results NoRetrospective Analysis NoSubgroup Analysis NoTop-Line Results NoUpdated Results NoA.1.3 Drug Characteristics and Overlap MeasuresThe overlap between the acquired drug project and the projects existing in the acquirer’sportfolio is measured based on the intended disease and biological target. According tomedical terminology, an intended disease is a medical condition for which a drug producesa therapeutic effect (e.g., solid oncology, cardiovascular hypertension, endocrine diabetes,etc.)4. It would include the treatment, prevention, and diagnosis of a disease. A biolog-ical target (often called a drug target) refers to a molecule in the body associated with a4The raw BioMedTracker data uses a medical term, indication, to document the medical condition ordisease that each drug project is developed for. Although the terminology is commonly used in the field ofmedicine, I use intended disease instead of indication to avoid using technical terminology.95Table A.3: Reasons for Drug Project DiscontinuationEvent Group Event Type ReasonOther Marketing Discontinuation Non-clinicalProgress Update Program Hold Non-clinicalSuspension Non-clinicalSuspension (Emerging Markets) Non-clinicalRegulatory J-NDA Withdrawal (Japan) Non-clinicalMAA Withdrawal Non-clinicalNDA/BLA Withdrawal Non-clinicalsNDA/sBLA Withdrawal Non-clinicalTrial Announcement Trial/Enrollment Suspension ClinicalTrial Data Suspension Clinicalparticular disease that could be addressed by a drug to produce a desired therapeutic effect(e.g., androgen receptors, T lymphocytes, antibody-drug conjugate, etc.). Proteins (includ-ing certain types of receptors, ion channels, enzymes, and carrier molecules) and nucleicacids are mainly used as biological targets.An acquisition is tagged with the overlap indicator if the acquired drug project and drugprojects in the acquiring firm’s portfolio are developed for the same medical condition anduse the same biological target. The overlap measure can capture the scientific similarityand potential substitutability between the drug projects.In the raw BioMedTracker data, 728 intended disease indications are identified, whichare classified into 102 disease groups and 21 upper-level disease groups. For example,the indication of solid tumors, which has the most drug projects developed to be approvedfor, belongs to the disease group of “Oncology, Solid” with the other 60 different types ofindications such as breast cancer, non-small cell lung cancer, and colorectal cancer. Andthe “Oncology, Solid” is one of the five disease groups that belong to the upper-level group,“Oncology”. For each drug project, biological target information is recorded as a compiled96list of all corresponding molecules in the body. In the raw BioMedTracker data, 1,656biological targets are identified.BioMedTracker also documents three other drug characteristics: the drug classification,molecule, and route of administration. The drug classification indicates more or less thetype of applications submitted to the FDA (e.g., new molecular entity, biologic, biosimilar,etc.), and the molecule classifies the molecular feature of the drugs (e.g., small molecule,monoclonal antibody, protein, etc.). The route of administration refers to the location atwhich the medication is applied or injected (e.g., oral, intravenous, subcutaneous, topical,etc.).A.1.4 Mergers and AcquisitionsDrug projects in BioMedTracker are tagged with the acquisition-related event types (i.e.,Acquisition Announcement, Acquisition Closed, Drug Acquisition) when their develop-ment rights are transferred from one firm to the other. In most cases, an acquirer firmpurchases all the rights to a target firm, making the target firm a wholly owned subsidiaryof the acquirer. The target firm is delisted from the stock market if it is a public entity. Inother cases, firms purchase the drug candidates (e.g., oncology business, a specific drugproject, pipeline, product line, and drug compounds) or regional/worldwide rights.Among the 1,331 acquisition or drug acquisition events in which the corresponding tar-get and acquirer firms are matched, 835 transactions are the acquisitions of the entire firms,and the remaining 471 transactions are the acquisitions of the rights to drug candidates ortechnologies.97A.1.5 Standardization of Company NamesI cleaned the company names in BioMedTracker to match them with those recorded inthe drug acquisition data and drug originator data explained in the following sections. Thecompany name standardization includes cleaning Unicode characters and deleting the busi-ness entity type parts (e.g., Inc, Ltd, LLC, GmbH, A/S, etc.). The target and acquiring firmsin SDC Platinum and the drug originators and developers in AdisInsight are cleaned fol-lowing the same rule.A.2 Drug Acquisition DataWhen a firm or a drug project is acquired, BioMedTracker tags the drug projects withthe acquisition or drug acquisition event type. However, the target and acquiring firmsassociated with the transaction are not recorded. I combine the drug acquisition data totrack the ownership changes of drug projects. This is ultimately to identify the developerof each drug project at each point in time.A.2.1 Refinitiv SDC PlatinumTo find the target and acquiring firms associated with the mergers & acquisitions transac-tions recorded in BioMedTracker, I collect the acquisition data from Refinitiv SDC Plat-inum (formerly known as Thomson Reuters). It is a comprehensive database of cross-border and domestic business transactions. It contains information on the mergers & ac-quisitions, including the target and acquiring firms (and their immediate and ultimate par-ent companies), the announcement and closed dates (or withdrawn dates if a transaction iscanceled), and a brief description of how the transactions are consummated.98I extract the deals prescreened by the following criteria: (i) the target firms operatingboth in the US and non-US countries, (ii) either the target or acquiring firm is in biotechnol-ogy (the industry classifications are listed in Table A.4), and (iii) the deals are announcedbetween January 1962 and December 31, 2020. After cleaning the company names, I com-bine the acquisition data with the BioMedTracker data through a fuzzy matching algorithmand manual check.Table A.4: SDC Platinum Industry Codes in All Biotechnology within High Tech In-dustryCode Description111 In-Vivo Diagnostic Products112 In-Vitro Diagnostic Products113 Genetically Eng. Prod (Human)114 Genetically Eng. Prod (Animal)115 Vaccines/Specialty Drugs116 General Pharmaceuticals117 Over-The-Counter Drugs118 Nuclear Chemicals119 Medicinal Chemicals120 Drug Delivery Sys (Not IV Sys)121 Blood Derivatives122 Research & Development Firm129 Other Biotechnology131 Medical Lasers132 Medical Imaging Systems133 Surgical Instruments/Equipment134 Lab Equipment135 Rehabilitation Equipment136 Artificial Organs/Limbs137 Medical Monitoring Systems138 General Med. Instruments/Supp.140 Healthcare Services99A.2.2 S&P Capital IQIn addition to SDC Platinum, I use S&P Capital IQ’s M&A Transaction Screening data asa supplementary source to find matched transactions. I extract the transactions between thetarget or acquiring firms in the healthcare industry.A.3 Drug Originator DataA.3.1 Springer Nature AdisInsightBioMedTracker updates the company name associated with each drug project as its latestdeveloper. More specifically, if a drug project (or a firm) is acquired, the acquiring firmbecomes the associated company of the drug project (or the drug projects of the target firm)in the raw BioMedTracker data. For example, Genentech Inc originally developed Rituxan,a drug used to treat specific kinds of Non-Hodgkin’s Lymphoma, and later the firm wasacquired by Roche Holdings AG in March 2009. In the raw data, Roche is recorded as thecompany associated with Rituxan, but no information is reported about the true originatorfirm, Genentech. Even if a target firm discontinues developing a drug project before it isacquired, the acquiring firm is still recorded as the associated company name of that drugproject. Besides the mergers & acquisitions, the data also updates the company name withthe new name when the firms change their corporate names.As the raw BioMedTracker data does not provide information on who initially startedto develop the drug projects, I need to combine other data to reconstruct the true origina-tor firms. For this purpose, I web-scrape the publicly available information on SpringerNature’s AdisInsight website. AdisInsight is an integrated database that tracks and pro-vides drug development worldwide through the entire development process, from discov-100ery, through pre-clinical and clinical studies to market launch and beyond.5 The collecteddata includes the primary and alternative names of the drug projects and their originatorsand developers.6 The originator refers to the institution that AdisInsight reviewers con-cluded originally invented or discovered the active ingredient of a given drug. The devel-opers refer to institutions involved in conducting, funding, or supporting the drug’s clinicaldevelopment.One challenge in reconstructing the true originator of the drug projects is to match thedrug names in BioMedTracker and AdisInsight. For each drug project, BioMedTrackerrecords its primary name (mostly the commercial name) and the alternative names (i.e.,chemical or generic names). The drug originator data also reports all alternative namesassigned to each project. Thus, the list of all assigned names in both data should be used inthe matching process, which requires more time and effort for manual checks.A.4 Robustness CheckA.4.1 Alternative Overlap IndicatorsI use alternative overlap indicators in the estimation to check whether the main results arerobust to changes in the overlap measures. In the main analysis, two scientific attributes(i.e., intended diseases and biological targets) are used to define the overlap indicator. I tagan acquired drug project with the overlap indicator when any of the acquirer’s drug projectsare developed for the same diseases using the same biological targets as the acquired one. Iuse the overlap indicators assigned based on more lenient criteria and repeat the estimation.5AdisInsight Report published in the Drugs journal.6I use the Python Selenium package to save publicly available information in the “Preview” section of thereports available from https://adisinsight.springer.com. The other publicly available information includes thedrug class, mechanism of action, and orphan drug designation. Data on 50,964 drug projects is scraped.101Table A.5: Drug Project Development: Alterative Overlap MeasuresHaving a development event(1) (2) (3) (4) (5)Acquisition×Post -0.0912∗∗∗ -0.0930∗∗∗ -0.0849∗∗∗ -0.102∗∗∗ -0.0906∗∗∗(0.0135) (0.0138) (0.0175) (0.0214) (0.0144)Acquisition×Post×Overlap 0.0376 0.0405 0.00222 0.0296 0.0257(0.0316) (0.0295) (0.0245) (0.0259) (0.0273)N 158,075 158,075 158,075 158,075 158,075R2 0.216 0.216 0.216 0.216 0.216Drug FE Y Y Y Y YDisease×Target FE Y Y Y Y YOverlap measure Subdisease&Target Disease&Target Subdisease Disease TargetStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001Table A.6: Drug Project Discontinuation: Alterative Overlap MeasuresHaving a discontinuation event(1) (2) (3) (4) (5)Acquisition×Post 0.0272∗∗∗ 0.0278∗∗∗ 0.0207∗∗∗ 0.0153∗∗∗ 0.0276∗∗∗(0.00349) (0.00360) (0.00392) (0.00457) (0.00367)Acquisition×Post×Overlap -0.00410 -0.00622 0.0116 0.0178∗∗ -0.00440(0.00726) (0.00675) (0.00611) (0.00607) (0.00660)N 158,075 158,075 158,075 158,075 158,075R2 0.064 0.064 0.064 0.064 0.064Drug FE Y Y Y Y YDisease×Target FE Y Y Y Y YOverlap measure Subdisease&Target Disease&Target Subdisease Disease TargetStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001Table A.5 and Table A.6 present the estimation results obtained by using the overlapindicator defined based on (1) 101 disease groups & 1,656 biological target combinations,(2) 21 upper-level disease groups & 1,656 biological target combinations, (3) 101 diseasegroups, (4) 21 upper-level disease groups, (5) 1,656 biological targets. The results obtainedfor the development event are fairly robust to changes in the overlap indicators. The resultsobtained for the discontinuation event are robust to the alternative overlap indicators basedon the upper-level disease groups & biological targets and only biological targets. The102estimated coefficient in column (4) of Table A.6 is positive and significant. I attribute thisto a too broadly defined overlap measure based on only 21 disease groups.A.4.2 Alternative Data FrequenciesThe main results are obtained using the data aggregated at the quarter level. I repeat theestimation to check whether the results are robust to the use of the data at the year andmonth level. Table A.7 through Table A.10 present the estimation results. The resultssuggest that the main results are robust to alternative data frequencies. The magnitudesof the coefficient estimates are larger in the year-level estimation than in the quarter-levelestimation. This is because the binary dependent variable is, by construction, more likelyto take the value of one in a given year than in a given quarter.103Table A.7: Drug Project Development: Yearly DataHaving a development eventScience-related Commercialization-related Science-related Strategic Science-related Nonstrategic(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Acquisition×Post -0.162∗∗∗ -0.184∗∗∗ -0.161∗∗∗ -0.186∗∗∗ -0.00283 -0.00714 -0.121∗∗∗ -0.123∗∗∗ -0.0976∗∗∗ -0.120∗∗∗(0.0180) (0.0201) (0.0172) (0.0190) (0.0121) (0.0132) (0.0135) (0.0147) (0.0164) (0.0178)Acquisition×Post 0.116∗∗ 0.130∗∗ 0.0223 0.00738 0.113∗∗×Overlap (0.0438) (0.0427) (0.0327) (0.0367) (0.0433)N 45,323 45,323 45,323 45,323 45,323 45,323 45,323 45,323 45,323 45,323R2 0.341 0.341 0.350 0.350 0.254 0.254 0.266 0.266 0.322 0.322Drug FE Y Y Y Y Y Y Y Y Y YDisease×Target FE Y Y Y Y Y Y Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001104Table A.8: Drug Project Development: Monthly DataHaving a development eventScience-related Commercialization-related Science-related Strategic Science-related Nonstrategic(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Acquisition×Post -0.0323∗∗∗ -0.0374∗∗∗ -0.0328∗∗∗ -0.0374∗∗∗ 0.00257 0.00164 -0.0145∗∗∗ -0.0159∗∗∗ -0.0212∗∗∗ -0.0252∗∗∗(0.00633) (0.00691) (0.00572) (0.00628) (0.00227) (0.00243) (0.00280) (0.00301) (0.00470) (0.00514)Acquisition×Post 0.0256 0.0225 0.00462 0.00698 0.0198×Overlap (0.0166) (0.0149) (0.00626) (0.00773) (0.0123)N 458,288 458,288 458,288 458,288 458,288 458,288 458,288 458,288 458,288 458,288R2 0.114 0.114 0.109 0.109 0.047 0.047 0.060 0.060 0.090 0.090Drug FE Y Y Y Y Y Y Y Y Y YDisease×Target FE Y Y Y Y Y Y Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001105Table A.9: Drug Project Discontinuation: Yearly DataHaving a discontinuation eventClinical Nonclinical(1) (2) (3) (4) (5) (6)Acquisition×Post 0.0765∗∗∗ 0.0745∗∗∗ 0.0155∗∗ 0.0111 0.0674∗∗∗ 0.0691∗∗∗(0.00923) (0.0103) (0.00518) (0.00567) (0.00787) (0.00872)Acquisition×Post 0.00994 0.0230 -0.00897×Overlap (0.0235) (0.0135) (0.0203)N 45,323 45,323 45,323 45,323 45,323 45,323R2 0.174 0.174 0.185 0.185 0.162 0.162Drug FE Y Y Y Y Y YDisease×Target FE Y Y Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001Table A.10: Drug Project Discontinuation: Monthly DataHaving a discontinuation eventClinical Nonclinical(1) (2) (3) (4) (5) (6)Acquisition×Post 0.0102∗∗∗ 0.0105∗∗∗ 0.00167∗∗ 0.00127 0.00874∗∗∗ 0.00936∗∗∗(0.00117) (0.00131) (0.000602) (0.000666) (0.000949) (0.00106)Acquisition×Post -0.00124 0.00196 -0.00307×Overlap (0.00295) (0.00152) (0.00238)N 458,288 458,288 458,288 458,288 458,288 458,288R2 0.024 0.024 0.024 0.024 0.022 0.022Drug FE Y Y Y Y Y YDisease×Target FE Y Y Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001A.4.3 Inclusion of Time Period Fixed EffectsAs a robustness check for the main results in Table 2.3 to Table 2.5, I estimate a difference-in-differences model with time period fixed effects. Including time period fixed effects isto control for unobserved time-specific factors that may affect the dependent variable. Ta-ble A.11 through Table A.13 present the estimation results. The results with the inclusion106of time period fixed effects are consistent with the results without the inclusion of time pe-riod fixed effects in that they suggest mergers have a negative impact on drug developmentand the existence of overlap does not have a significant impact on innovation outcomes.Table A.11: Project Overlap and Innovation Outcomes: with Time Period Fixed Ef-fectsHaving a development event Having a discontinuation event(1) (2) (3) (4)Acquisition×Post -0.0543∗∗∗ -0.0644∗∗∗ -0.00565 -0.00507(0.0120) (0.0130) (0.00335) (0.00382)Acquisition×Post×Overlap 0.0512 -0.00295(0.0310) (0.00756)N 158,075 158,075 158,075 158,075R2 0.241 0.241 0.091 0.091Time Period FE Y Y Y YDrug FE Y Y Y YDisease×Target FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001Table A.12: Scientific versus Commercialization Activities: with Time Period FixedEffectsHaving a development eventScience-related Commercialization-related(1) (2) (3) (4)Acquisition×Post -0.0537∗∗∗ -0.0637∗∗∗ -0.00636 -0.00708(0.0113) (0.0121) (0.00516) (0.00559)Acquisition×Post×Overlap 0.0506 0.00363(0.0296) (0.0136)N 158,075 158,075 158,075 158,075R2 0.239 0.239 0.115 0.115Time Period FE Y Y Y YDrug FE Y Y Y YDisease×Target FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001107Table A.13: Strategic versus Nonstrategic Scientific Activities: with Time PeriodFixed EffectsHaving a science-related development eventStrategic Nonstrategic(1) (2) (3) (4)Acquisition×Post -0.00527 -0.00600 -0.0582∗∗∗ -0.0668∗∗∗(0.00648) (0.00685) (0.00990) (0.0106)Acquisition×Post×Overlap 0.00369 0.0437(0.0180) (0.0261)N 158,075 158,075 158,075 158,075R2 0.137 0.137 0.210 0.210Time Period FE Y Y Y YDrug FE Y Y Y YDisease×Target FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001A.4.4 Logistic RegressionI estimate a logistic regression as a robustness check for the main results presented in Ta-ble 2.3 to Table 2.5. The dependent variables are dummy variables indicating whether adrug project has an event specified for each estimation (e.g., development event). The inde-pendent variables are also dummy variables indicating whether a drug project is acquiredand whether a given period is before or after the acquisition of the drug project. I includedrug fixed effects to control for variation from unobservable drug-specific characteristics.Table A.14 through Table A.16 present the estimation results.The logit estimation results are generally consistent with the results from the linearprobability model in that they imply the negative impact of mergers on innovation out-comes. The results also suggest the negative effect of mergers is driven by their negativeeffect on the scientific process of drug development rather than on the commercialization108process.Table A.14: Project Overlap and Innovation Outcomes: Logistic RegressionHaving a development event Having a discontinuation event(1) (2) (3) (4)Acquisition×Post -0.527∗∗∗ -0.587∗∗∗ 0.959∗∗∗ 1.015∗∗∗(0.0414) (0.0470) (0.100) (0.113)Acquisition×Post×Overlap 0.279∗∗ -0.265(0.100) (0.244)N 166,516 166,516 166,516 166,516Drug FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001Table A.15: Scientific versus Commercialization Activities: Logistic RegressionHaving a development eventScience-related Commercialization-related(1) (2) (3) (4)Acquisition×Post -0.572∗∗∗ -0.643∗∗∗ 0.0417 0.0297(0.0430) (0.0491) (0.0773) (0.0909)Acquisition×Post×Overlap 0.319∗∗ 0.0434(0.103) (0.173)N 166,516 166,516 166,516 166,516Drug FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001A.5 FiguresA.5.1 Difference-in-Differences Event Study based on Yearly DataFigure A.1 visualize the coefficient estimates from the difference-in-differences event studyobtained by using the data aggregate at the year level. The results suggest no statistically109Table A.16: Strategic versus Nonstrategic Scientific Activities: Logistic RegressionHaving a science-related development eventStrategic Nonstrategic(1) (2) (3) (4)Acquisition×Post -0.508∗∗∗ -0.551∗∗∗ -0.459∗∗∗ -0.537∗∗∗(0.0589) (0.0688) (0.0466) (0.0535)Acquisition×Post×Overlap 0.165 0.338∗∗(0.134) (0.110)N 166,516 166,516 166,516 166,516Drug FE Y Y Y YStandard errors are clustered at the drug project level and shown in parentheses.Significance levels: ∗ p < 0.05, ∗∗ p < 0.01, ∗∗∗ p < 0.001significant difference between acquired and non-acquired drugs before treatment. The re-sults support the identification assumption of the difference-in-differences analyses that thedevelopment (or discontinuation) activities of acquired drug projects would have followedthe same trend as those of non-acquired projects.-.6-.4-.20.2-7 years Acquisition +8 years(a) Development Events-.10.1.2-7 years Acquisition +8 years(b) Discontinuation EventsFigure A.1: Dynamics of the impact of acquisitions on innovation outcomes of ac-quired drugs using yearly data110Appendix BChapter 3 AppendicesB.1 TablesTable B.1: Adults and Children Living with HIV/AIDS in 1997Region of the World Number of Cases Percentage of theInfected PopulationNorth America 860,000 2.81Caribbean 310,000 1.01Latin America 1,300,000 4.25Western Europe 480,000 1.57Eastern Europe & Central Asia 190,000 0.62East Asia & Pacific 420,000 1.37North Africa & Middle East 210,000 0.69South & South-East Asia 5,800,000 18.97Australia & New Zealand 12,200 0.04sub-Saharan Africa 21,000,000 68.67Total 30,582,200 100Source: Report on the global HIV/AIDS epidemic, June 1998. Prepared by UNAIDS and WHO.111B.2 FiguresB.2.1 In-Time Placebo Study with Alternative Placebo TreatmentPeriodsIn addition to the in-time placebo study in Figure 3.3, we rerun the study for alternativeplacebo treatment periods, April 1995 and April 1996. Figure B.1 presents the result andconfirms the credibility of our main results. The drug development path of the syntheticHIV drug therapy industry closely follows that of the actual HIV drug therapy industry. Inall cases, the placebo treatment effects are negligible.Placebo weakening patent rights →150200250300Number of drugs in development1991 1992 1993 1994 1995 1996 1997YearActual HIVSynthetic HIV(a) All drugs in development, April 1995Placebo weakening patent rights →020406080Number of drugs in phase II and III1991 1992 1993 1994 1995 1996 1997YearActual HIVSynthetic HIV(b) Drugs in phase II or III, April 1995Placebo weakening patent rights →150200250300Number of drugs in development1991 1992 1993 1994 1995 1996 1997YearActual HIVSynthetic HIV(c) All drugs in development, April 1996Placebo weakening patent rights →020406080Number of drugs in phase II and III1991 1992 1993 1994 1995 1996 1997YearActual HIVSynthetic HIV(d) Drugs in phase II or III, April 1996Figure B.1: In-time placebo study with alternative placebo treatment periods112Appendix CChapter 4 AppendicesC.1 Robustness CheckC.1.1 Use of Alternative Price Dispersion MeasuresI repeat the estimation to check whether the main results are robust to the use of alternativeprice dispersion measures. In the main analysis, I use the log-odds ratio and the logarithmof the Gini coefficient as price dispersion measures. To unpack the results, I estimate themodel separately for the 10th and 90th percentiles of the price distribution. Alternatively, Iuse the coefficient of variation, Theil indices, the Gini coefficient of the logged fares, andthe 20th and 80th percentiles of the price distribution. Table C.1 presents the estimationresults. The estimates suggest that the results in the main analysis are fairly robust to usingalternative measures.113C.1.2 Inclusion of Control VariablesI repeat the estimation with the inclusion of control variables as a robustness check. Iuse the Herfindahl index, a bankrupt indicator, and jet fuel prices, which would controlfor the route-level competition faced by incumbents, financial shock, and operating costs.Table C.2 through Table C.6 present the estimation results. The results suggest that themain results hold with additional control variables.114Table C.1: Alternative Price Dispersion Measures(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)ln(meanfare) log-odds Gini ln(Gini) Gini(logfare) ln(coefvar) ln(theill) ln(theilt) ln(P10) ln(P20) ln(P80) ln(P90)threatt0−8 −0.0215 −0.0238 −0.0200 −0.000867 −0.0126 −0.0283 −0.0250 −0.00853 −0.0174 −0.0313∗ −0.0186(0.0144) (0.0203) (0.0157) (0.000645) (0.0164) (0.0285) (0.0295) (0.0132) (0.0141) (0.0178) (0.0196)threatt0−7 −0.0392∗∗∗ −0.0227 −0.0180 −0.00145∗∗ −0.000128 −0.0281 −0.0135 −0.00220 −0.0184 −0.0553∗∗∗ −0.0316(0.0142) (0.0214) (0.0162) (0.000737) (0.0169) (0.0303) (0.0309) (0.0155) (0.0162) (0.0189) (0.0209)threatt0−6 −0.0309∗ 0.0301 0.0206 0.000382 0.0362∗∗ 0.0372 0.0545∗ −0.0255 −0.0373∗∗ −0.0314 −0.00687(0.0179) (0.0213) (0.0159) (0.000755) (0.0173) (0.0304) (0.0309) (0.0175) (0.0185) (0.0234) (0.0231)threatt0−5 −0.00403 0.0250 0.0158 −0.0000812 0.0314 0.0306 0.0453 0.0136 −0.00288 −0.0110 0.0351(0.0209) (0.0261) (0.0194) (0.000898) (0.0214) (0.0371) (0.0381) (0.0197) (0.0203) (0.0276) (0.0275)threatt0−4 −0.0232 0.0183 0.0106 −0.000778 0.0368 0.0222 0.0459 0.00102 −0.00346 −0.0583∗∗ 0.0290(0.0220) (0.0305) (0.0230) (0.00103) (0.0264) (0.0446) (0.0465) (0.0219) (0.0230) (0.0293) (0.0298)threatt0−3 −0.0313 0.0151 0.0109 −0.000575 0.0238 0.0120 0.0270 −0.00323 −0.0237 −0.0303 −0.00870(0.0226) (0.0305) (0.0228) (0.00107) (0.0265) (0.0440) (0.0462) (0.0236) (0.0249) (0.0287) (0.0276)threatt0−2 −0.0415∗ −0.0216 −0.0200 −0.00154 0.000581 −0.0389 −0.0225 0.0000832 −0.00901 −0.0647∗∗ −0.0265(0.0248) (0.0327) (0.0242) (0.00117) (0.0291) (0.0473) (0.0501) (0.0263) (0.0278) (0.0325) (0.0305)threatt0−1 −0.0493∗ −0.0392 −0.0339 −0.00115 −0.0237 −0.0545 −0.0550 −0.0155 −0.0190 −0.0575 −0.0532(0.0262) (0.0369) (0.0273) (0.00132) (0.0306) (0.0520) (0.0538) (0.0267) (0.0287) (0.0349) (0.0330)threatt0 −0.0802∗∗∗ −0.0400 −0.0332 −0.00178 −0.0126 −0.0651 −0.0488 −0.0433 −0.0440 −0.0989∗∗∗ −0.0752∗∗(0.0285) (0.0418) (0.0311) (0.00154) (0.0338) (0.0590) (0.0603) (0.0319) (0.0335) (0.0362) (0.0356)threatt0+1 −0.0948∗∗∗ −0.109∗∗∗ −0.0886∗∗∗ −0.00396∗∗∗ −0.0680∗ −0.172∗∗∗ −0.155∗∗ −0.0266 −0.0352 −0.135∗∗∗ −0.119∗∗∗(0.0298) (0.0414) (0.0308) (0.00147) (0.0346) (0.0584) (0.0608) (0.0330) (0.0340) (0.0373) (0.0361)threatt0+2 −0.139∗∗∗ −0.0933∗∗ −0.0759∗∗ −0.00402∗∗ −0.0401 −0.155∗∗ −0.118∗ −0.0531 −0.0799∗∗ −0.197∗∗∗ −0.159∗∗∗(0.0316) (0.0441) (0.0328) (0.00159) (0.0366) (0.0620) (0.0645) (0.0345) (0.0364) (0.0406) (0.0395)threatt0+3:+12 −0.150∗∗∗ −0.122∗∗ −0.0955∗∗∗ −0.00477∗∗∗ −0.0703∗ −0.201∗∗∗ −0.169∗∗ −0.0567 −0.0779∗ −0.192∗∗∗ −0.171∗∗∗(0.0363) (0.0482) (0.0360) (0.00172) (0.0404) (0.0677) (0.0708) (0.0395) (0.0414) (0.0423) (0.0452)entryt0 −0.211∗∗∗ −0.0472 −0.0405 −0.00128 −0.0141 −0.0969 −0.0622 −0.161∗∗∗ −0.180∗∗∗ −0.228∗∗∗ −0.206∗∗∗(0.0410) (0.0564) (0.0423) (0.00198) (0.0457) (0.0776) (0.0810) (0.0433) (0.0467) (0.0485) (0.0490)entryt0+1 −0.267∗∗∗ −0.0557 −0.0471 −0.00171 −0.00590 −0.109 −0.0600 −0.193∗∗∗ −0.228∗∗∗ −0.302∗∗∗ −0.261∗∗∗(0.0450) (0.0593) (0.0444) (0.00209) (0.0479) (0.0814) (0.0847) (0.0458) (0.0511) (0.0533) (0.0537)entryt0+2 −0.294∗∗∗ −0.0710 −0.0589 −0.00216 −0.0267 −0.144∗ −0.0954 −0.205∗∗∗ −0.248∗∗∗ −0.314∗∗∗ −0.287∗∗∗(0.0472) (0.0622) (0.0467) (0.00221) (0.0513) (0.0867) (0.0907) (0.0500) (0.0534) (0.0552) (0.0557)entryt0+3:+12 −0.294∗∗∗ −0.0686 −0.0558 −0.00155 −0.0365 −0.145 −0.107 −0.223∗∗∗ −0.254∗∗∗ −0.321∗∗∗ −0.294∗∗∗(0.0501) (0.0674) (0.0499) (0.00243) (0.0561) (0.0929) (0.0981) (0.0544) (0.0584) (0.0595) (0.0597)Carrier-route fixed effects, year-quarter fixed effects and constant term are suppressed.Standard errors reported in parentheses are clustered by carrier-route.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01115Table C.2: Competition and Average Fare: Inclusion of Control VariablesLog of Average Fare(1) (2) (3) (4) (5) (6) (7) (8)ln(HHI) 0.187∗∗∗ 0.186∗∗∗ 0.187∗∗∗ 0.186∗∗∗(0.0260) (0.0262) (0.0260) (0.0262)bankrupt −0.0205 −0.0104 −0.0205 −0.0104(0.0187) (0.0180) (0.0187) (0.0180)jetfuelprice 0.0932∗∗ 0.0965∗∗ 0.0654 0.0714∗(0.0456) (0.0444) (0.0436) (0.0428)threatt0−8 −0.0215 −0.0213 −0.0228 −0.0215 −0.0219 −0.0213 −0.0228 −0.0219(0.0144) (0.0146) (0.0145) (0.0144) (0.0146) (0.0146) (0.0145) (0.0146)threatt0−7 −0.0392∗∗∗ −0.0379∗∗∗ −0.0406∗∗∗ −0.0392∗∗∗ −0.0386∗∗∗ −0.0379∗∗∗ −0.0406∗∗∗ −0.0386∗∗∗(0.0142) (0.0141) (0.0143) (0.0142) (0.0142) (0.0141) (0.0143) (0.0142)threatt0−6 −0.0309∗ −0.0290∗ −0.0346∗ −0.0309∗ −0.0309∗ −0.0290∗ −0.0346∗ −0.0309∗(0.0179) (0.0171) (0.0185) (0.0179) (0.0177) (0.0171) (0.0185) (0.0177)threatt0−5 −0.00403 −0.00507 −0.00746 −0.00403 −0.00682 −0.00507 −0.00746 −0.00682(0.0209) (0.0203) (0.0213) (0.0209) (0.0206) (0.0203) (0.0213) (0.0206)threatt0−4 −0.0232 −0.0307 −0.0277 −0.0232 −0.0329 −0.0307 −0.0277 −0.0329(0.0220) (0.0217) (0.0225) (0.0220) (0.0222) (0.0217) (0.0225) (0.0222)threatt0−3 −0.0313 −0.0378∗ −0.0350 −0.0313 −0.0397∗ −0.0378∗ −0.0350 −0.0397∗(0.0226) (0.0222) (0.0230) (0.0226) (0.0227) (0.0222) (0.0230) (0.0227)threatt0−2 −0.0415∗ −0.0463∗ −0.0455∗ −0.0415∗ −0.0483∗ −0.0463∗ −0.0455∗ −0.0483∗(0.0248) (0.0243) (0.0255) (0.0248) (0.0250) (0.0243) (0.0255) (0.0250)threatt0−1 −0.0493∗ −0.0563∗∗ −0.0522∗ −0.0493∗ −0.0577∗∗ −0.0563∗∗ −0.0522∗ −0.0577∗∗(0.0262) (0.0260) (0.0267) (0.0262) (0.0264) (0.0260) (0.0267) (0.0264)threatt0 −0.0802∗∗∗ −0.0854∗∗∗ −0.0831∗∗∗ −0.0802∗∗∗ −0.0869∗∗∗ −0.0854∗∗∗ −0.0831∗∗∗ −0.0869∗∗∗(0.0285) (0.0281) (0.0291) (0.0285) (0.0287) (0.0281) (0.0291) (0.0287)threatt0+1 −0.0948∗∗∗ −0.101∗∗∗ −0.0986∗∗∗ −0.0948∗∗∗ −0.102∗∗∗ −0.101∗∗∗ −0.0986∗∗∗ −0.102∗∗∗(0.0298) (0.0293) (0.0307) (0.0298) (0.0302) (0.0293) (0.0307) (0.0302)threatt0+2 −0.139∗∗∗ −0.145∗∗∗ −0.143∗∗∗ −0.139∗∗∗ −0.147∗∗∗ −0.145∗∗∗ −0.143∗∗∗ −0.147∗∗∗(0.0316) (0.0309) (0.0326) (0.0316) (0.0319) (0.0309) (0.0326) (0.0319)threatt0+3:+12 −0.150∗∗∗ −0.156∗∗∗ −0.153∗∗∗ −0.150∗∗∗ −0.157∗∗∗ −0.156∗∗∗ −0.153∗∗∗ −0.157∗∗∗(0.0363) (0.0360) (0.0372) (0.0363) (0.0368) (0.0360) (0.0372) (0.0368)entryte −0.211∗∗∗ −0.167∗∗∗ −0.213∗∗∗ −0.211∗∗∗ −0.169∗∗∗ −0.167∗∗∗ −0.213∗∗∗ −0.169∗∗∗(0.0410) (0.0385) (0.0417) (0.0410) (0.0393) (0.0385) (0.0417) (0.0393)entryte+1 −0.267∗∗∗ −0.222∗∗∗ −0.270∗∗∗ −0.267∗∗∗ −0.224∗∗∗ −0.222∗∗∗ −0.270∗∗∗ −0.224∗∗∗(0.0450) (0.0421) (0.0456) (0.0450) (0.0428) (0.0421) (0.0456) (0.0428)entryte+1 −0.294∗∗∗ −0.245∗∗∗ −0.296∗∗∗ −0.294∗∗∗ −0.247∗∗∗ −0.245∗∗∗ −0.296∗∗∗ −0.247∗∗∗(0.0472) (0.0442) (0.0478) (0.0472) (0.0449) (0.0442) (0.0478) (0.0449)entryte+3:+12 −0.294∗∗∗ −0.249∗∗∗ −0.297∗∗∗ −0.294∗∗∗ −0.251∗∗∗ −0.249∗∗∗ −0.297∗∗∗ −0.251∗∗∗(0.0501) (0.0474) (0.0508) (0.0501) (0.0482) (0.0474) (0.0508) (0.0482)Carrier-route fixed effects, year-quarter fixed effects and constant term are suppressed.Standard errors reported in parentheses are clustered by carrier-route.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01116Table C.3: Competition and Log-Odds Ratio of Gini Coefficient: Inclusion of ControlVariablesLog-Odds Ratio of Gini Coefficient(1) (2) (3) (4) (5) (6) (7) (8)ln(HHI) −0.0469 −0.0495 −0.0469 −0.0495(0.0324) (0.0328) (0.0324) (0.0328)bankrupt −0.0384 −0.0411 −0.0384 −0.0411(0.0264) (0.0267) (0.0264) (0.0267)jetfuelprice 0.00604 0.00521 0.0627 0.0612(0.0705) (0.0702) (0.0670) (0.0667)threatt0−8 −0.0238 −0.0239 −0.0261 −0.0238 −0.0263 −0.0239 −0.0261 −0.0263(0.0203) (0.0203) (0.0204) (0.0203) (0.0204) (0.0203) (0.0204) (0.0204)threatt0−7 −0.0227 −0.0231 −0.0253 −0.0227 −0.0259 −0.0231 −0.0253 −0.0259(0.0214) (0.0214) (0.0214) (0.0214) (0.0214) (0.0214) (0.0214) (0.0214)threatt0−6 0.0301 0.0296 0.0230 0.0301 0.0220 0.0296 0.0230 0.0220(0.0213) (0.0213) (0.0218) (0.0213) (0.0219) (0.0213) (0.0218) (0.0219)threatt0−5 0.0250 0.0252 0.0185 0.0250 0.0184 0.0252 0.0185 0.0184(0.0261) (0.0262) (0.0263) (0.0261) (0.0264) (0.0262) (0.0263) (0.0264)threatt0−4 0.0183 0.0202 0.0100 0.0183 0.0114 0.0202 0.0100 0.0114(0.0305) (0.0304) (0.0311) (0.0305) (0.0310) (0.0304) (0.0311) (0.0310)threatt0−3 0.0151 0.0167 0.00818 0.0151 0.00942 0.0167 0.00818 0.00942(0.0305) (0.0304) (0.0306) (0.0305) (0.0305) (0.0304) (0.0306) (0.0305)threatt0−2 −0.0216 −0.0204 −0.0291 −0.0216 −0.0283 −0.0204 −0.0291 −0.0283(0.0327) (0.0326) (0.0328) (0.0327) (0.0327) (0.0326) (0.0328) (0.0327)threatt0−1 −0.0392 −0.0374 −0.0446 −0.0392 −0.0432 −0.0374 −0.0446 −0.0432(0.0369) (0.0368) (0.0366) (0.0369) (0.0365) (0.0368) (0.0366) (0.0365)threatt0 −0.0400 −0.0386 −0.0455 −0.0400 −0.0445 −0.0386 −0.0455 −0.0445(0.0418) (0.0417) (0.0419) (0.0418) (0.0417) (0.0417) (0.0419) (0.0417)threatt0+1 −0.109∗∗∗ −0.108∗∗∗ −0.116∗∗∗ −0.109∗∗∗ −0.115∗∗∗ −0.108∗∗∗ −0.116∗∗∗ −0.115∗∗∗(0.0414) (0.0411) (0.0416) (0.0414) (0.0413) (0.0411) (0.0416) (0.0413)threatt0+2 −0.0933∗∗ −0.0918∗∗ −0.101∗∗ −0.0933∗∗ −0.0997∗∗ −0.0918∗∗ −0.101∗∗ −0.0997∗∗(0.0441) (0.0439) (0.0441) (0.0441) (0.0440) (0.0439) (0.0441) (0.0440)threatt0+3:+12 −0.122∗∗ −0.120∗∗ −0.128∗∗∗ −0.122∗∗ −0.127∗∗∗ −0.120∗∗ −0.128∗∗∗ −0.127∗∗∗(0.0482) (0.0480) (0.0480) (0.0482) (0.0476) (0.0480) (0.0480) (0.0476)entryte −0.0472 −0.0580 −0.0525 −0.0472 −0.0643 −0.0580 −0.0525 −0.0643(0.0564) (0.0558) (0.0560) (0.0564) (0.0556) (0.0558) (0.0560) (0.0556)entryte+1 −0.0557 −0.0670 −0.0609 −0.0557 −0.0733 −0.0670 −0.0609 −0.0733(0.0593) (0.0584) (0.0585) (0.0593) (0.0578) (0.0584) (0.0585) (0.0578)entryte+1 −0.0710 −0.0831 −0.0758 −0.0710 −0.0889 −0.0831 −0.0758 −0.0889(0.0622) (0.0611) (0.0615) (0.0622) (0.0606) (0.0611) (0.0615) (0.0606)entryte+3:+12 −0.0686 −0.0798 −0.0746 −0.0686 −0.0869 −0.0798 −0.0746 −0.0869(0.0674) (0.0661) (0.0667) (0.0674) (0.0655) (0.0661) (0.0667) (0.0655)Carrier-route fixed effects, year-quarter fixed effects and constant term are suppressed.Standard errors reported in parentheses are clustered by carrier-route.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01117Table C.4: Competition and Log of Gini Coefficient: Inclusion of Control VariablesLog of Gini Coefficient(1) (2) (3) (4) (5) (6) (7) (8)ln(HHI) −0.0373 −0.0391 −0.0373 −0.0391(0.0248) (0.0251) (0.0248) (0.0251)bankrupt −0.0263 −0.0285 −0.0263 −0.0285(0.0199) (0.0201) (0.0199) (0.0201)jetfuelprice 0.0104 0.00972 0.0545 0.0533(0.0537) (0.0534) (0.0510) (0.0507)threatt0−8 −0.0200 −0.0200 −0.0215 −0.0200 −0.0217 −0.0200 −0.0215 −0.0217(0.0157) (0.0157) (0.0157) (0.0157) (0.0157) (0.0157) (0.0157) (0.0157)threatt0−7 −0.0180 −0.0182 −0.0198 −0.0180 −0.0202 −0.0182 −0.0198 −0.0202(0.0162) (0.0163) (0.0162) (0.0162) (0.0162) (0.0163) (0.0162) (0.0162)threatt0−6 0.0206 0.0202 0.0157 0.0206 0.0150 0.0202 0.0157 0.0150(0.0159) (0.0160) (0.0163) (0.0159) (0.0163) (0.0160) (0.0163) (0.0163)threatt0−5 0.0158 0.0160 0.0114 0.0158 0.0113 0.0160 0.0114 0.0113(0.0194) (0.0194) (0.0195) (0.0194) (0.0195) (0.0194) (0.0195) (0.0195)threatt0−4 0.0106 0.0121 0.00490 0.0106 0.00601 0.0121 0.00490 0.00601(0.0230) (0.0229) (0.0234) (0.0230) (0.0233) (0.0229) (0.0234) (0.0233)threatt0−3 0.0109 0.0123 0.00621 0.0109 0.00719 0.0123 0.00621 0.00719(0.0228) (0.0227) (0.0229) (0.0228) (0.0228) (0.0227) (0.0229) (0.0228)threatt0−2 −0.0200 −0.0191 −0.0252 −0.0200 −0.0246 −0.0191 −0.0252 −0.0246(0.0242) (0.0241) (0.0242) (0.0242) (0.0241) (0.0241) (0.0242) (0.0241)threatt0−1 −0.0339 −0.0325 −0.0377 −0.0339 −0.0365 −0.0325 −0.0377 −0.0365(0.0273) (0.0272) (0.0271) (0.0273) (0.0270) (0.0272) (0.0271) (0.0270)threatt0 −0.0332 −0.0322 −0.0370 −0.0332 −0.0362 −0.0322 −0.0370 −0.0362(0.0311) (0.0310) (0.0311) (0.0311) (0.0310) (0.0310) (0.0311) (0.0310)threatt0+1 −0.0886∗∗∗ −0.0875∗∗∗ −0.0934∗∗∗ −0.0886∗∗∗ −0.0926∗∗∗ −0.0875∗∗∗ −0.0934∗∗∗ −0.0926∗∗∗(0.0308) (0.0306) (0.0309) (0.0308) (0.0307) (0.0306) (0.0309) (0.0307)threatt0+2 −0.0759∗∗ −0.0747∗∗ −0.0811∗∗ −0.0759∗∗ −0.0803∗∗ −0.0747∗∗ −0.0811∗∗ −0.0803∗∗(0.0328) (0.0327) (0.0328) (0.0328) (0.0327) (0.0327) (0.0328) (0.0327)threatt0+3:+12 −0.0955∗∗∗ −0.0943∗∗∗ −0.0997∗∗∗ −0.0955∗∗∗ −0.0989∗∗∗ −0.0943∗∗∗ −0.0997∗∗∗ −0.0989∗∗∗(0.0360) (0.0358) (0.0358) (0.0360) (0.0355) (0.0358) (0.0358) (0.0355)entryte −0.0405 −0.0492 −0.0442 −0.0405 −0.0535 −0.0492 −0.0442 −0.0535(0.0423) (0.0419) (0.0420) (0.0423) (0.0417) (0.0419) (0.0420) (0.0417)entryte+1 −0.0471 −0.0562 −0.0507 −0.0471 −0.0605 −0.0562 −0.0507 −0.0605(0.0444) (0.0439) (0.0439) (0.0444) (0.0434) (0.0439) (0.0439) (0.0434)entryte+1 −0.0589 −0.0685 −0.0622 −0.0589 −0.0725 −0.0685 −0.0622 −0.0725(0.0467) (0.0459) (0.0462) (0.0467) (0.0455) (0.0459) (0.0462) (0.0455)entryte+3:+12 −0.0558 −0.0648 −0.0599 −0.0558 −0.0696 −0.0648 −0.0599 −0.0696(0.0499) (0.0489) (0.0493) (0.0499) (0.0485) (0.0489) (0.0493) (0.0485)Carrier-route fixed effects, year-quarter fixed effects and constant term are suppressed.Standard errors reported in parentheses are clustered by carrier-route.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01118Table C.5: Competition and 10th Percentile Fare: Inclusion of Control Variables10th Percentile Fare(1) (2) (3) (4) (5) (6) (7) (8)ln(HHI) 0.200∗∗∗ 0.200∗∗∗ 0.200∗∗∗ 0.200∗∗∗(0.0247) (0.0247) (0.0247) (0.0247)bankrupt −0.00576 0.00504 −0.00576 0.00504(0.0166) (0.0157) (0.0166) (0.0157)jetfuelprice 0.0423 0.0458 −0.00140 0.00501(0.0499) (0.0490) (0.0472) (0.0464)threatt0−8 −0.00853 −0.00825 −0.00888 −0.00853 −0.00795 −0.00825 −0.00888 −0.00795(0.0132) (0.0131) (0.0133) (0.0132) (0.0132) (0.0131) (0.0133) (0.0132)threatt0−7 −0.00220 −0.000777 −0.00259 −0.00220 −0.000433 −0.000777 −0.00259 −0.000433(0.0155) (0.0155) (0.0154) (0.0155) (0.0154) (0.0155) (0.0154) (0.0154)threatt0−6 −0.0255 −0.0235 −0.0266 −0.0255 −0.0226 −0.0235 −0.0266 −0.0226(0.0175) (0.0170) (0.0175) (0.0175) (0.0170) (0.0170) (0.0175) (0.0170)threatt0−5 0.0136 0.0125 0.0127 0.0136 0.0133 0.0125 0.0127 0.0133(0.0197) (0.0190) (0.0198) (0.0197) (0.0191) (0.0190) (0.0198) (0.0191)threatt0−4 0.00102 −0.00694 −0.000223 0.00102 −0.00586 −0.00694 −0.000223 −0.00586(0.0219) (0.0214) (0.0220) (0.0219) (0.0215) (0.0214) (0.0220) (0.0215)threatt0−3 −0.00323 −0.0102 −0.00427 −0.00323 −0.00930 −0.0102 −0.00427 −0.00930(0.0236) (0.0234) (0.0236) (0.0236) (0.0235) (0.0234) (0.0236) (0.0235)threatt0−2 0.0000832 −0.00501 −0.00104 0.0000832 −0.00404 −0.00501 −0.00104 −0.00404(0.0263) (0.0259) (0.0265) (0.0263) (0.0260) (0.0259) (0.0265) (0.0260)threatt0−1 −0.0155 −0.0230 −0.0163 −0.0155 −0.0223 −0.0230 −0.0163 −0.0223(0.0267) (0.0264) (0.0269) (0.0267) (0.0266) (0.0264) (0.0269) (0.0266)threatt0 −0.0433 −0.0489 −0.0441 −0.0433 −0.0482 −0.0489 −0.0441 −0.0482(0.0319) (0.0313) (0.0322) (0.0319) (0.0315) (0.0313) (0.0322) (0.0315)threatt0+1 −0.0266 −0.0327 −0.0277 −0.0266 −0.0318 −0.0327 −0.0277 −0.0318(0.0330) (0.0325) (0.0333) (0.0330) (0.0328) (0.0325) (0.0333) (0.0328)threatt0+2 −0.0531 −0.0595∗ −0.0542 −0.0531 −0.0585∗ −0.0595∗ −0.0542 −0.0585∗(0.0345) (0.0338) (0.0350) (0.0345) (0.0343) (0.0338) (0.0350) (0.0343)threatt0+3:+12 −0.0567 −0.0628 −0.0577 −0.0567 −0.0620 −0.0628 −0.0577 −0.0620(0.0395) (0.0388) (0.0399) (0.0395) (0.0391) (0.0388) (0.0399) (0.0391)entryte −0.161∗∗∗ −0.115∗∗∗ −0.162∗∗∗ −0.161∗∗∗ −0.114∗∗∗ −0.115∗∗∗ −0.162∗∗∗ −0.114∗∗∗(0.0433) (0.0396) (0.0435) (0.0433) (0.0399) (0.0396) (0.0435) (0.0399)entryte+1 −0.193∗∗∗ −0.144∗∗∗ −0.194∗∗∗ −0.193∗∗∗ −0.144∗∗∗ −0.144∗∗∗ −0.194∗∗∗ −0.144∗∗∗(0.0458) (0.0424) (0.0461) (0.0458) (0.0426) (0.0424) (0.0461) (0.0426)entryte+1 −0.205∗∗∗ −0.153∗∗∗ −0.205∗∗∗ −0.205∗∗∗ −0.153∗∗∗ −0.153∗∗∗ −0.205∗∗∗ −0.153∗∗∗(0.0500) (0.0459) (0.0503) (0.0500) (0.0462) (0.0459) (0.0503) (0.0462)entryte+3:+12 −0.223∗∗∗ −0.175∗∗∗ −0.224∗∗∗ −0.223∗∗∗ −0.174∗∗∗ −0.175∗∗∗ −0.224∗∗∗ −0.174∗∗∗(0.0544) (0.0506) (0.0547) (0.0544) (0.0510) (0.0506) (0.0547) (0.0510)Carrier-route fixed effects, year-quarter fixed effects and constant term are suppressed.Standard errors reported in parentheses are clustered by carrier-route.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01119Table C.6: Competition and 90th Percentile Fare: Inclusion of Control Variables90th Percentile Fare(1) (2) (3) (4) (5) (6) (7) (8)ln(HHI) 0.184∗∗∗ 0.182∗∗∗ 0.184∗∗∗ 0.182∗∗∗(0.0334) (0.0336) (0.0334) (0.0336)bankrupt −0.0343 −0.0244 −0.0343 −0.0244(0.0233) (0.0230) (0.0233) (0.0230)jetfuelprice 0.0460 0.0493 0.0449 0.0507(0.0590) (0.0583) (0.0562) (0.0559)threatt0−8 −0.0186 −0.0184 −0.0207 −0.0186 −0.0198 −0.0184 −0.0207 −0.0198(0.0196) (0.0198) (0.0196) (0.0196) (0.0197) (0.0198) (0.0196) (0.0197)threatt0−7 −0.0316 −0.0302 −0.0339 −0.0316 −0.0319 −0.0302 −0.0339 −0.0319(0.0209) (0.0207) (0.0211) (0.0209) (0.0209) (0.0207) (0.0211) (0.0209)threatt0−6 −0.00687 −0.00499 −0.0131 −0.00687 −0.00946 −0.00499 −0.0131 −0.00946(0.0231) (0.0225) (0.0238) (0.0231) (0.0234) (0.0225) (0.0238) (0.0234)threatt0−5 0.0351 0.0341 0.0294 0.0351 0.0300 0.0341 0.0294 0.0300(0.0275) (0.0270) (0.0282) (0.0275) (0.0277) (0.0270) (0.0282) (0.0277)threatt0−4 0.0290 0.0217 0.0216 0.0290 0.0165 0.0217 0.0216 0.0165(0.0298) (0.0298) (0.0309) (0.0298) (0.0308) (0.0298) (0.0309) (0.0308)threatt0−3 −0.00870 −0.0151 −0.0149 −0.00870 −0.0195 −0.0151 −0.0149 −0.0195(0.0276) (0.0276) (0.0285) (0.0276) (0.0284) (0.0276) (0.0285) (0.0284)threatt0−2 −0.0265 −0.0312 −0.0332 −0.0265 −0.0359 −0.0312 −0.0332 −0.0359(0.0305) (0.0302) (0.0316) (0.0305) (0.0314) (0.0302) (0.0316) (0.0314)threatt0−1 −0.0532 −0.0600∗ −0.0580∗ −0.0532 −0.0634∗ −0.0600∗ −0.0580∗ −0.0634∗(0.0330) (0.0330) (0.0338) (0.0330) (0.0338) (0.0330) (0.0338) (0.0338)threatt0 −0.0752∗∗ −0.0804∗∗ −0.0802∗∗ −0.0752∗∗ −0.0839∗∗ −0.0804∗∗ −0.0802∗∗ −0.0839∗∗(0.0356) (0.0356) (0.0365) (0.0356) (0.0364) (0.0356) (0.0365) (0.0364)threatt0+1 −0.119∗∗∗ −0.125∗∗∗ −0.126∗∗∗ −0.119∗∗∗ −0.129∗∗∗ −0.125∗∗∗ −0.126∗∗∗ −0.129∗∗∗(0.0361) (0.0360) (0.0374) (0.0361) (0.0373) (0.0360) (0.0374) (0.0373)threatt0+2 −0.159∗∗∗ −0.164∗∗∗ −0.165∗∗∗ −0.159∗∗∗ −0.169∗∗∗ −0.164∗∗∗ −0.165∗∗∗ −0.169∗∗∗(0.0395) (0.0393) (0.0408) (0.0395) (0.0406) (0.0393) (0.0408) (0.0406)threatt0+3:+12 −0.171∗∗∗ −0.176∗∗∗ −0.176∗∗∗ −0.171∗∗∗ −0.180∗∗∗ −0.176∗∗∗ −0.176∗∗∗ −0.180∗∗∗(0.0452) (0.0453) (0.0462) (0.0452) (0.0462) (0.0453) (0.0462) (0.0462)entryte −0.206∗∗∗ −0.163∗∗∗ −0.211∗∗∗ −0.206∗∗∗ −0.167∗∗∗ −0.163∗∗∗ −0.211∗∗∗ −0.167∗∗∗(0.0490) (0.0489) (0.0498) (0.0490) (0.0498) (0.0489) (0.0498) (0.0498)entryte+1 −0.261∗∗∗ −0.216∗∗∗ −0.265∗∗∗ −0.261∗∗∗ −0.220∗∗∗ −0.216∗∗∗ −0.265∗∗∗ −0.220∗∗∗(0.0537) (0.0527) (0.0543) (0.0537) (0.0534) (0.0527) (0.0543) (0.0534)entryte+1 −0.287∗∗∗ −0.239∗∗∗ −0.291∗∗∗ −0.287∗∗∗ −0.243∗∗∗ −0.239∗∗∗ −0.291∗∗∗ −0.243∗∗∗(0.0557) (0.0550) (0.0563) (0.0557) (0.0557) (0.0550) (0.0563) (0.0557)entryte+3:+12 −0.294∗∗∗ −0.250∗∗∗ −0.300∗∗∗ −0.294∗∗∗ −0.254∗∗∗ −0.250∗∗∗ −0.300∗∗∗ −0.254∗∗∗(0.0597) (0.0594) (0.0605) (0.0597) (0.0602) (0.0594) (0.0605) (0.0602)Carrier-route fixed effects, year-quarter fixed effects and constant term are suppressed.Standard errors reported in parentheses are clustered by carrier-route.∗ p < 0.10, ∗∗ p < 0.05, ∗∗∗ p < 0.01120C.2 TablesC.2.1 U.S. Airline BankruptciesTable C.7 presents the U.S. airline bankruptcies filed in the 1993–2016 period.Table C.7: U.S. Airline BankruptciesCarrier Date Filed Date Exited ChapterContinental Airlines Dec 3, 1990 Apr, 1993 11America West Airlines Jun 27, 1991 Aug 26, 1994 11Trans World Airlines Jan 31, 1992 Nov 3, 1993 11Hermans/Markair Express Jun 8, 1992 Mar, 1994 11Markair Jun 8, 1992 Mar, 1994 11Hawaiian Airlines Sep 21, 1993 Sep, 1994 11Markair Apr 14, 1995 11Trans World Airlines Jun 30, 1995 Aug 23, 1995 11Grand Airways Nov 28, 1995 11Kiwi International Airlines Sep 30, 1996 11Western Pacific Airlines Oct 5, 1997 11Pan American World Airways Feb 26, 1998 11Kiwi International Airlines Mar 23, 1999 11Eastwind Airlines Sep 30, 1999 7Access Air Nov 29, 1999 11Tower Air Feb 29, 2000 11Pro Air Sep 19, 2000 11National Airlines Dec 6, 2000 11Allegiant Air Dec 13, 2000 Mar, 2002 11Trans World Airlines Jan 11, 2001 11Sun Country Airlines Jan 2, 2002 Apr 15, 2002 7Vanguard Airlines July 30, 2002 11US Airways Aug 11, 2002 Mar 31, 2003 11United Airlines Dec 9, 2002 Feb 1, 2006 11Hawaiian Airlines Mar 21, 2003 Jun 2, 2005 11US Airways Sep 12, 2004 Sep 27, 2005 11ATA Airlines Oct 26, 2004 Feb 28, 2006 11Aloha Airlines Dec 30, 2004 Feb 17, 2006 11Northwest Airlines Sep 14, 2005 May 31, 2007 11Delta Air Lines Sep 14, 2005 Apr 30, 2007 11121Carrier Date Filed Date Exited ChapterMesaba Airlines Oct 13, 2005 Apr 24, 2007 11Independence Air Nov 7, 2005 11Independence Air Jan 6, 2006 7Florida Coastal Airlines Feb 21, 2006 11Aloha Airlines Mar 31, 2008 7Champion Air Mar 31, 2008 11ATA Airlines Apr 2, 2008 11Skybus Airlines Apr 5, 2008 11Frontier Airlines Apr 10, 2008 Oct 1, 2009 11Sun Country Airlines Oct 6, 2008 Feb 23, 2011 11Mesa Airlines Jan 5, 2010 Mar 11, 2011 11American Airlines Nov 29, 2011 Dec 8, 2013 11Global Aviation Holdings Feb 16, 2012 Feb 13, 2013 11Republic Airways Holdings Feb 25, 2016 Apr 30, 2017 11Source: Data & Statistics compiled by Airlines for AmericaC.2.2 Southwest Airlines Arrival HistoryTable C.8 presents the dates when Southwest Airlines established its presence at each air-port. The information is available on Southwest Airlines’ website, which updates mediareleases and news.Table C.8: Southwest Airlines Arrival HistoryCity Code DateDallas (Love Field) DAL 06/18/71San Antonio SAT 06/18/71Houston (Hobby) HOU 11/14/71*Harlingen/South Padre Island HRL 02/11/75Corpus Christi CRP 03/01/77Lubbock LBB 05/20/77Midland/Odessa MAF 05/20/77El Paso ELP 06/30/77Austin AUS 09/15/77Amarillo AMA 12/12/78122City Code DateNew Orleans MSY 01/25/79Oklahoma City OKC 04/01/80Tulsa TUL 04/02/80Albuquerque ABQ 04/03/80Las Vegas LAS 01/31/82Phoenix PHX 01/31/82San Diego SAN 01/31/82Kansas City MCI 02/18/82Los Angeles LAX 09/18/82Little Rock LIT 02/27/84Chicago (Midway) MDW 03/17/85St. Louis STL 03/17/85Ontario ONT 05/15/85Nashville BNA 03/18/86Birmingham BHM 03/10/87Detroit DTW 06/04/87Indianapolis IND 04/03/89Oakland OAK 05/15/89Burbank BUR 04/16/90Reno RNO 11/15/90Sacramento SMF 06/17/91Cleveland CLE 02/13/92Columbus CMH 06/04/92Louisville SDF 05/24/93San Jose, California SJC 06/01/93Baltimore/Washington BWI 09/15/93Orange County SNA 05/26/94Portland, OR PDX 06/06/94Seattle/Tacoma SEA 06/06/94Spokane GEG 06/06/94Boise BOI 10/04/94Salt Lake City SLC 10/04/94Tucson TUS 10/04/94Omaha OMA 03/04/95Ft. Lauderdale/Hollywood FLL 01/22/96Tampa Bay TPA 01/22/96Orlando MCO 04/07/96Providence PVD 10/27/96Jacksonville JAX 01/15/97Manchester MHT 06/07/98123City Code DateLong Island MacArthur Airport ISP 03/14/99Raleigh/Durham RDU 06/06/99Hartford/Springfield BDL 10/31/99Albany ALB 05/07/00Buffalo/Niagara Falls BUF 10/08/00West Palm Beach PBI 01/21/01Norfolk/Virginia Beach ORF 10/07/01Philadelphia PHL 05/09/04Pittsburgh PIT 05/04/05Ft. Myers/Naples RSW 10/02/05Denver DEN 01/03/06**Washington Dulles International IAD 10/05/06San Francisco SFO SFO 08/26/07***Minneapolis/St. Paul MSP 03/08/09New York (LaGuardia) LGA 06/28/09Boston Logan BOS 08/16/09Milwaukee MKE 11/01/09Panama City Beach, FL ECP 05/23/10Greenville-Spartanburg GSP 03/13/11Charleston CHS 03/13/11New York (Newark) EWR 03/27/11Atlanta ATL 02/12/12Ronald Reagan Washington National DCA 07/08/12Des Moines DSM 09/30/12Charlotte CLT 04/14/13San Juan SJU 04/14/13Flint FNT 04/14/13Rochester ROC 04/14/13Portland, ME PWM 04/14/13Wichita ICT 06/02/13Grand Rapids GRR 08/11/13Memphis MEM 11/03/13Pensacola PNS 11/03/13Richmond RIC 11/03/13Montego Bay MBJ 07/01/14Aruba AUA 07/01/14Nassau NAS 07/01/14Cancun CUN 08/10/14San Jose del Cabo/Los Cabos SJD 08/10/14Punta Cana PUJ 11/02/14124City Code DateMexico City MEX 11/02/14San Jose, Costa Rica SJO 03/07/15Puerto Vallarta PVR 06/07/15Belize BZE 10/15/15Liberia/Guanacaste, Costa Rica LIR 11/01/15Long Beach LGB 06/05/16Havana, Cuba HAV 12/12/16Grand Cayman GCM 06/04/17Cincinnati CVG 06/04/17Providenciales, Turks&Caicos PLS 11/05/17Beaumont/Port Arthur, TX BPT 3/5/79 - 9/5/80Detroit City Airport, MI DET 7/6/88 - 9/14/93Jackson, MS JAN 8/9/97 - 6/7/14Key West, FL EYW 11/4/12 - 6/7/14Branson, MO BKG 3/9/13 - 6/7/14Akron/Canton, OH CAK 8/12/12 - 6/3/17Dayton, OH (DAY) DAY 8/12/12 - 6/3/17Varadero, Cuba (VRA) VRA 11/13/16 - 9/4/17Santa Clara, Cuba SNU 12/15/16 - 9/4/17* Houston Intercontinental was Southwest’s original and only Houston airport with service from 6/18/71until 11/14/71, when service began from Houston Hobby. Service at Intercontinental ceased 5/14/72.** Southwest served Denver Stapleton (DEN) from 5/26/83 to 9/30/86.*** Southwest served San Francisco Int’l Airport (SFO) from 10/31/82 to 3/4/01.Source: Southwest Airlines Newsroom125
Thesis/Dissertation
eng
Business Administration - Strategy and Business Economics
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Marshall, Guillermo, (Professor)
Essays on innovation and industrial economics
Text