Essays on Drivers of Quality and Compliance Performance in the Pharmaceutical

Home , Pharmaceutical policy

Industry: Policy, Manufacturing Strategy, and Organizational Learning Perspectives

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy

in the Graduate School of The Ohio State University

In Joon Noh

Graduate Program in Business Administration

The Ohio State University

2020

Dissertation Committee

John V. Gray, Advisor

Aravind Chandrasekaran

Hyunwoo Park

George P. Ball

Copyrighted by

In Joon Noh

2020

Abstract

In this dissertation, we examine some of the important drivers of quality and compliance performance. The context of this dissertation is pharmaceutical industry, where quality and compliance is crucial for public health and thus is heavily regulated by the U.S. Food and Drug Administration (FDA). Despite the critical importance, however, the failure incidents in this industry have been steadily increasing over time, a trend that runs counter to those observed in other regulated industries such as airline and railroads. Yet, research concerning this important phenomenon in the pharmaceutical industry is limited.

We consider three broad categories of quality and compliance drivers – pharmaceutical policy, manufacturing strategy and organizational learning. By investigating how the interplays between these categories of factors influence pharmaceutical quality and compliance performance, this dissertation not only advances the knowledge in the literature, but also sheds light on the increasing failure trend in this industry.

Two standalone essays in this dissertation examine pharmaceutical quality and compliance performance at different levels: 1) at the drug level, where quality performance is measured by serious drug recalls, and 2) at the inspection level, where compliance performance is measured by violations of current Good Manufacturing

Practice (cGMP) regulations as determined by the FDA during the plant inspection, respectively.

In the first essay (Chapter 2), we compare the quality risk of generic drugs, whose approval and use has been facilitated by the government to reduce healthcare costs, against that of the corresponding original brand-name drugs. Then, we further examine whether manufacturing drugs in less-advanced economies, an increasingly prevalent manufacturing strategy in the pharmaceutical industry, influences the generic drug- quality risk relationships. Based on a large-scale, drug-level panel dataset, we find that there is no significant difference in the frequency of serious recalls between generic drugs and their brand-name counterparts overall. However, generic drug recall risk becomes significantly higher than that of the original counterparts, when they are manufactured in less-advanced economies. Our post-hoc analysis indicates that learning-by-doing, as measured by drug age, significantly mitigates the generic drug quality risk relative to the corresponding original drugs, but that internal manufacturing and learning from failure experience does not. These findings have implications for policy makers and managers.

For managers, there is potentially a greater cost for quality and compliance associated with manufacturing generics in less-advanced economies. For policy makers, there should be an increased focus on generics manufacturing in less-advanced economies; and there may be value to requiring transparency regarding drug manufacturing location.

In the second essay (Chapter 3), we examine vicarious learning from the FDA warning letters – one of the means for the agency to disseminate failure information to the industry. More specifically, we investigate if the probability that the focal plant violates a cGMP regulation as determined by the FDA during the plant inspection decreases as other plants receive more warning letters citing that specific cGMP

iii regulation. We also explore if such learning becomes more pronounced among the plants that are similar in one of two dimensions – geographic location and manufacturing processes. Our analyses, based on the FDA’s public facing databases, reveal that there does not exist significant industry-wide learning or learning among the plants that have similar manufacturing processes. We do find, however, that the focal plant learns from the warning letters issued to the other plants located in geographical proximity. These findings imply that, in the pharmaceutical industry context, the FDA’s role as a central source of broadcast transmission of knowledge – one of the salient knowledge diffusion mechanism established in the vicarious learning literature – may not be sufficient alone to induce compliance-enhancing learning.

Overall, this dissertation enhances the understanding of how some of the important pharmaceutical policy-related factors (i.e., promoting market competition via low-cost generic drugs, disseminating failure information via warning letters), together with manufacturing strategy (i.e., manufacturing location, outsourcing) and organizational learning (i.e., learning from own and others’ experience), affect quality and compliance performance. The findings of this dissertation not only advance the aforementioned literature bases but also provide valuable implications to policy makers and managers. Our findings may be generalizable to other industry contexts, especially where quality is not easily observable and thus compliance is difficult to measure. This dissertation raises questions for future research about the quality and compliance implications of other pharmaceutical policies, such as Generic Drug User Fee Act

(GDUFA) or Drug Supply Chain Security Act (DSCSA), whose effect may be contingent on some organizational or supply chain characteristics.

Dedication

This work is dedicated to my loving wife Sujin, without whose support I would not have been where I am today, and to our beloved children, Yuna (Emily) and Yunseo (Chloe), who have given me energy to keep on going.

Acknowledgments

I am forever thankful for all the guidance and support that my advisor, Dr. John Gray, has provided me throughout my doctoral studies and dissertation work. His continuous advice and patient encouragement has made this journey possible and enjoyable. I truly appreciate all he has done. He will always be my role model in the pursuit of my academic career.

I would like to acknowledge the support of my dissertation committee members:

Dr. Aravind Chandrasekaran, for his very insightful suggestions on the third chapter of this dissertation; Dr. Hyunwoo Park, for his great support on the data side in general; Dr.

George Ball, for his excellent help in framing and writing the second chapter of this dissertation.

I am also grateful to Dr. James Hill and Dr. Gokce Esenduran, and to Dr. Andy

Tsay and Dr. Joseph Mahoney. I learned a lot from them while working on the resale channel choice project and on the outsourcing literature review project, respectively.

Finally, my thanks go to my fellow Ph.D. students for their friendship: Keith Skowronski,

Luv Sharma, Yingchao Lan, Myra Pan, Somak Paul, Rahul Pandey, and John Lowrey.

Some work in this dissertation was related to projects funded by the U.S. Food and Drug Administration (FDA). I thank Dr. John Gray and Dr. George Ball for the opportunity to be part of these projects, which involved collaboration with the FDA.

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Vita

January 2008 ...... B.S. Industrial Engineering Hanyang University, Seoul, S. Korea

January 2008 – January 2010 ...... M.S. Industrial and Systems Engineering KAIST, Daejeon, S. Korea

January 2010 – July 2014 ...... Quality Engineer Samsung Electronics, Hwasung, S. Korea

August 2014 – present ...... Ph.D. Candidate Department of Management Sciences The Ohio State University Columbus, Ohio

Publications

Tsay, A.A., J.V. Gray, I.J. Noh, J.T. Mahoney. 2018. A Review of Production and Operations Management Research on Outsourcing in Supply Chains: Implications for the Theory of the Firm. Production and Operations Management, 27(7): 1177-1220.

Esenduran, G., Hill, J.A. and Noh, I.J., 2020. Understanding the Choice of Online Resale Channel for Used Electronics. Production and Operations Management, 29(5): 1188- 1211.

Ko, Y.D., I.J. Noh, H. Hwang. 2012. Cost Benefits from Standardization of the Packaging Glass Bottles. Computers and Industrial Engineering, 62(3): 693-702.

Fields of Study

Major Field: Business Administration (Concentration: Operations Management)

Minor Field: Statistics

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Table of Contents

Abstract ...... ii

Dedication ...... vi

Acknowledgments...... vii

Vita ...... viii

List of Tables ...... xii

List of Figures ...... xv

Chapter 1. Introduction ...... 1

Chapter 2. Are Generic Drugs Safe, Regardless of Production Location? A Drug Level

Analysis...... 11

2.1. Introduction ...... 11

2.2. Hypotheses ...... 16

2.2.1. Generic vs. Original Drug ...... 16

2.2.2. Advanced vs. Non-Advanced Economies...... 18

2.3. Method ...... 21

2.3.1. Sample...... 21 ix

2.3.2. Data Sources and Measures ...... 22

2.3.3. Empirical Strategy ...... 25

2.4. Results ...... 28

2.4.1. Identification, Endogeneity, and Robustness Checks ...... 30

2.4.2. Post-hoc: Moderating effect of learning from experience, learning from

failure, and internal manufacturing ...... 31

2.5. Discussion, Limitations, and Conclusion...... 35

Chapter 3. Vicarious Learning from Warning Letters ...... 38

3.1. Introduction ...... 38

3.2. Theory, Literature, and Hypothesis Development ...... 44

3.2.1. Organizational Learning ...... 44

3.2.2. Learning from Success and Failure Experience...... 45

3.2.3. Vicarious Learning from Failure Experience ...... 46

3.2.4. Compliance Inspection...... 54

3.2.5. Vicarious Learning from FDA Warning Letters ...... 60

3.2.6. Moderating Effects of Geographical Proximity and Manufacturing Process

Similarity...... 62

3.3. Method ...... 64

3.3.1. Research Design, Data, and Measures ...... 64

3.3.2. Empirical Strategy ...... 72

3.4. Results ...... 74

3.4.1. Main Results ...... 75

3.4.1. Robustness Checks...... 77

3.5. Discussion ...... 80

3.5.1. Theoretical Contributions and Policy Implications ...... 82

3.5.2. Limitations and Conclusion ...... 83

Chapter 4. Conclusion ...... 86

Bibliography ...... 90

Appendix: Online Appendix for Chapter 2 ...... 112

A. Descriptive statistics ...... 112

B. Robustness checks ...... 118

B1. Stratum fixed effects, in addition to firm and year fixed effects...... 119

B2. Logistic panel regression ...... 121

B3. All class recalls as DV ...... 124

B4. Exclusion of strata where brand-name drug manufacturer also produces equivalent

generic drugs ...... 126

B5. Sales volume in the previous year ...... 129

List of Tables

Table 1. Drivers of quality and compliance performance examined in the representative studies in the literature ...... 10

Table 2. Variables, data sources, and measurement procedure ...... 23

Table 3. Descriptive statistics and correlations...... 27

Table 4. Negative binomial regression result ...... 28

Table 5. Post-hoc analysis: moderating effect of learning from experience, learning from failure, and internal manufacturing ...... 34

Table 6. Empirical findings from vicarious learning from failure literature ...... 50

Table 7. Empirical findings from compliance inspection literature ...... 56

Table 8. C.F.R. Title 21 Part 211 – Subpart, and its topical area and the corresponding

CFR items ...... 66

Table 9. Variables, data sources, and measurement procedure ...... 68

Table 10. Summary statistics ...... 70

Table 11. Correlation matrix ...... 71

Table 12. Count of citations for each subpart of CFR Part 211 ...... 72

Table 13. Difference-GMM estimation results ...... 76

Table 14. Summary statistics (within vs. outside the focal firm) ...... 78

Table 15. Difference-GMM estimation results (within vs. outside the focal firm) ...... 79

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Table 16. Frequency of firms by number of plants that belong to the firm ...... 80

Table 17. Total number of drugs in the matched sample ...... 112

Table 18. Number of drugs per each matched stratum (# matched strata = 576) ...... 112

Table 19. Number of drugs by panel start years (2009 ~ 2018) ...... 113

Table 20. Number of recalls by class ...... 113

Table 21. Number of recalls (Class 1 and 2) by year...... 114

Table 22. Generic and product age – Sample mean and standard deviation ...... 114

Table 23. Generic and non-advanced economies – Number of drugs in the sample ...... 115

Table 24. Generic and non-advanced economies – Number of drug-year instances involved in recalls (Class I and II) ...... 115

Table 25. Generic and advanced economies (detailed) – Number of drugs ...... 116

Table 26. Generic and non-advanced economies (detailed) – Number of drugs ...... 117

Table 27. Generic and IM – Number of drug-year instances ...... 117

Table 28. Generic and IM – Number of drug-year instances involved in recalls (Class I and II) ...... 117

Table 29. Summary of the results ...... 118

Table 30. Negative binomial panel regression with firm, year, and stratum fixed effects

...... 120

Table 31. Logistic panel regression with firm and year fixed effects ...... 122

Table 32. Logistic panel regression with firm, year, and stratum fixed effects ...... 123

Table 33. All recalls classes as DV; negative binomial panel regression with firm and year fixed effects ...... 124

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Table 34. All recalls classes as DV; logistic panel regression with firm and year fixed effects ...... 125

Table 35. Without 40; negative binomial panel regression with firm and year fixed effects

...... 127

Table 36. Without 40; logistic panel regression with firm and year fixed effects ...... 128

Table 37. Sales volume in the previous year; negative binomial panel regression with firm and year fixed effects ...... 130

Table 38. Sales volume in the previous year; logistic panel regression with firm and year fixed effects ...... 131

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List of Figures

Figure 1. Structure of dissertation ...... 5

Figure 2. Generic_D * Non_Adv_Econ_D interaction plot ...... 29

Chapter 1. Introduction

Pharmaceutical quality and compliance is critical to U.S. public health, as nearly seventy percent of Americans take at least one prescription medicine regularly.1 Yet, consumers and healthcare professionals are unable to easily assess drug quality, which makes effective regulation and policies of central importance to achieve consistent pharmaceutical quality and compliance (Woodcock 2004). In the U.S., the Food and

Drug Administration (FDA) is responsible “to ensure that drugs marketed in this country are safe and effective” (FDA 2019). To fulfill this responsibility, the FDA conducts various regulatory activities throughout the drugs’ life cycle, from R&D to manufacturing and post-marketing: in the drug development stage, new drugs must go through the

FDA’s evaluation process in order to be approved to be sold in the U.S. market; in the manufacturing stage, the FDA conducts current Good Manufacturing Practice (cGMP) compliance inspections on the pharmaceutical manufacturing plants,2 where in recent years the likelihood of a plant being inspected is based partially on a risk-based site- selection model3; in the post-marketing stage, the agency maintains post-market drug

1 https://newsnetwork.mayoclinic.org/discussion/nearly-7-in-10-americans-take-prescription-drugs-mayoclinic-olmsted-medical-center-find/ 2 https://www.fda.gov/drugs/pharmaceutical-quality-resources/current-good-manufacturing-practice-cgmp- regulations 3 https://www.fda.gov/media/116004/download 1 surveillance programs.4 The FDA also works to continuously improve their communication to the public regarding any identified risks associated with drugs, to help people to make more informed decisions about the drugs that they use and thus to penalize poor quality.5 However, despite such efforts, the frequency of pharmaceutical quality and compliance problems, including product (e.g., drug recalls and adverse event reports) and process failures (e.g., violations of cGMP compliance regulations), has risen steadily over time.6

There do exist some notable trends in the pharmaceutical industry that may have been correlated with such increasing failure trend. First, in order to address the ever- growing healthcare costs in the U.S., the government has introduced policies to encourage market competition via generic drugs through, for example, Generic Drug

User Fee Amendment (GDUFA 2012) and Drug Competition Action Plan (DCAP 2017).

The FDA explicitly states this idea in its guidance information regarding DCAP as follows: “FDA is helping remove barriers to generic drug development and market entry in an effort to spur competition so that consumers can get access to the medicines they need at affordable prices.”7 The policy has proven effective in reducing the cost of medicines, as 90 percent of prescription drugs dispensed in the U.S. are generic drugs,8

4 https://www.fda.gov/drugs/surveillance/postmarketing-surveillance-programs 5 https://www.fda.gov/science-research/science-and-research-special-topics/risk-communication 6 https://www.bioprocessonline.com/doc/an-analysis-of-fda-fy-drug-gmp-warning-letters-0003; https://www.uspharmacist.com/article/overview-of-the-fdas-drugrecall-process 7 https://www.fda.gov/drugs/guidance-compliance-regulatory-information/fda-drug-competition-action- plan#:~:text=In%202017%2C%20FDA%20announced%20the,underlying%20our%20generic%20drug%20 program. 8 https://www.fda.gov/drugs/buying-using-medicine-safely/generic-drugs 2 which, according to the FDA, enabled annual savings of $8.8 billion in 2017.9 The intensified market competition, however, implies increased cost pressure on the pharmaceutical manufacturers, which, according to the previous literature, is associated with increased drug recalls (Ball et al. 2018). Second, from a manufacturing strategy perspective (Skinner 1974, Fine and Hax 1985, Hayes and Pisano 1996), pharmaceutical supply chains have become increasingly globalized and complex, possibly in part in response to the raised cost pressure due to generic-induced competition. Janet Woodcock, the director of the Center for Drug Evaluations and Research (CDER) at the FDA, stated in her December 2019 congressional testimony that “Beginning in the 1970s, industry moved away from the mainland United States … to developing nations such as China and

India … [as these nations] provide significant cost savings … [and] have weaker environmental regulations than more developed countries. … As of August 2019, [only]

28 percent of facilities manufacturing active pharmaceutical ingredients (APIs) and 47 percent of the facilities producing finished dosage forms (FDFs) of human drugs for the

U.S. market were located in the United States.” Manufacturing outsourcing has similarly gained rising popularity in the pharmaceutical industry possibly for cost reasons as well,10 so much so that the FDA recently released the guidance for industry for quality assurance under contract manufacturing arrangements.11 Unfortunately, previous research has shown that these supply chain practices – i.e., offshoring or outsourcing – may

9 https://www.fda.gov/media/113500/download 10 https://www.ipqpubs.com/cmo-story/; https://www.outsourcing- pharma.com/Article/2015/10/02/Outsourcing-about-cost-reduction-more-than-anything-else-says-Deloitte 11 https://www.fda.gov/regulatory-information/search-fda-guidance-documents/contract-manufacturing- arrangements-drugs-quality-agreements-guidance-industry 3 adversely affect quality and compliance performance (Gray et al. 2011, Steven and Britto

2016, Anderson et al. 2018). This dissertation extends the literature by examining how the interplay between pharmaceutical policy (e.g., promoting market competition via low-cost generic drugs) and manufacturing strategy (e.g., offshoring and outsourcing) have contributed to the prevalent pharmaceutical quality and compliance failures.

Also examined in this dissertation is the role of organizational learning in potentially alleviating the failure trend in the pharmaceutical industry. The premises of the organizational learning literature is that firms learn from their own and others’ experience – either successes or failures – and subsequently improve their performance

(Nelson and Winter 1982, Levinthal and March 1993).12 This notion of organizational learning has been supported by empirical evidence in various industry contexts such as automobile (Levin 2000, Haunschild and Rhee 2004), airline (Haunschild and Sullivan

2002, Madsen and Desai 2018), railroad (Baum and Dahlin 2007), coal-mining (Madsen

2009), and the medical device industry (Thirumalai and Sinha 2011). In fact, some of these studies found that, in part thanks to organizational learning, the industry-wide failures (e.g., accidents) have significantly decreased over time (e.g., Haunschild and

Sullivan 2002, Baum and Dahlin 2007, Madsen and Desai 2010). Apparently, such decreasing trend of failures in those industries runs counter to that in the pharmaceutical industry. Research on the effect of organizational learning on quality and compliance performance in this highly regulated industry, especially together with important

12 Learning-by-doing, or learning curve, can be considered as part of “learning from own successful experience” (e.g., Yelle 1979, Argote 1999). 4 pharmaceutical policies concerning, for example, market competition and public communication, is scarce. This dissertation also intends to partially fill this gap.

Overall, this dissertation investigates three broad categories of pharmaceutical quality and compliance drivers: pharmaceutical policy (promoting competition through generic drugs, disseminating failure information to industry via warning letters), manufacturing strategy (manufacturing location and outsourcing) and organizational learning (learning from own and others’ experience). More specifically, the dissertation focuses on how the interplays between the pharmaceutical policy and other categories of factors (i.e., manufacturing strategy and organizational learning) affect pharmaceutical quality and compliance performance. Figure 1 below illustrates the conceptual structure of this dissertation. More details on each essay follow. Table 1 at the end of this chapter depicts the gap in the extant literature that this dissertation aims to partially fill.

Figure 1. Structure of dissertation

In Chapter 2, we empirically compare the quality risk of generic drugs, whose approval and use has been promoted heavily by the government to address increasing healthcare costs, against that of their branded counterparts at the drug level. In addition, we examine whether manufacturing strategy (manufacturing in advanced vs. non- advanced economies, and manufacturing outsourcing) and organizational learning

(learning-by-doing and learning from failure) moderate the generic-quality risk relationship. In this first essay, drug quality risk is measured by serious (i.e., Class I and

II) recalls (Thirumalai and Sinha 2011, Ball et al. 2018). Drawing on knowledge (Nelson and Winter 1982, Kogut and Zander 1982) and incentive perspectives (Kranton 2003,

Board and Meyer-ter-Vehn 2013), as well as on institutional theories (Peng 2003,

Marquis and Qian 2014), we make theoretical arguments that a generic drug will be associated with more frequent serious drug recalls compared to its original counterpart, and that this relationship will be strengthened when drugs are manufactured in non- advanced economies. We test these relationships using a dataset constructed from several secondary databases, employing an econometric matching technique for identification.

Our results, based on 2,892 drugs from 224 firms experiencing 367 serious recalls across a 10 year period, indicate that there is no significant difference in serious recalls between generic and the branded counterpart overall, but the recall risk of generic drugs significantly increases compared to that of brand-name drugs when manufactured in non- advanced economies. We also check for other moderating factors – learning from production experience (or learning-by-doing), learning from failure experience, and

6 internal (vs. contract) manufacturing – that may influence generic quality risk. Among these moderators, we find that learning-by-doing, as measured by product age, significantly mitigates the generic drug quality risk, relative to that of the corresponding branded drugs. This essay makes contributions to the literature and policy, by examining the relative quality risk of generic drugs for the first time on a large-scale basis with a clear identification strategy (matching), while taking into account the manufacturing strategies and organizational learning. In particular, the drug manufacturing strategy information – where the drug is made and by whom – is considered as “trade-secret” and, thus, is not disclosed by the FDA even upon Freedom of Information Act (FOIA) requests. Nevertheless, our novel dataset allowed us to access such information and ultimately to provide these important insights. The findings of this essay also provide managerial implications for generic manufacturers who wish to offshore their manufacturing to low-cost regions, as they need to consider that there will be a greater quality risk and/or a need for increased oversight.

Chapter 3 focuses on learning from others’ failures, more specifically learning from the FDA warning letters that are issued to other pharmaceutical manufacturing plants. The FDA warning letters represent one of the agency’s means to disseminate compliance failure information to the regulated industry.13 Thus, while the warning letters are intended to prompt compliance from those who committed violations, the public nature of warning letters also provide those who are not receiving one with opportunities to learn from others’ mistakes. Drawing on the literature on vicarious

13 https://www.fda.gov/media/71878/download 7 learning from failure (e.g., Madsen and Desai 2010, KC et al. 2013), as well as on the distinct characteristics of the FDA warning letters, we argue that the more FDA warning letters issued to other plants citing a specific regulation, the lower the likelihood that the focal plant violates that specific regulation. We further argue that such violation-reducing learning will be stronger when the scope of “other plants” is restricted to 1) those that are located in geographical proximity with the focal plant, or to 2) those that have similar manufacturing process, which is proxied by the FDA’s plant profile codes (Gray et al.

2015), as the focal plant. We test these relationships at the “pharmaceutical manufacturing plant-inspection-regulation (i.e., Code of Federal Regulation, or CFR, subpart)” level, using the data constructed from five publicly attainable FDA databases.

Based on 1,078 pharmaceutical manufacturing plants and the corresponding 28,380 inspection-CFR subpart instances and the 277 warning letters from 2010 to 2016, we find that the vicarious learning from others’ warning letters takes place only among the plants that are located geographically close to each other. Our finding that there is no industry- wide vicarious learning from failure or learning among the plants having similar manufacturing processes is somewhat counterintuitive and against the empirical evidence observed in other industry contexts, yet appears to be aligned with the increasing failure trend in the pharmaceutical industry. This result provides implications to the FDA’s public communication and dissemination policies, as the agency’s intention of publicizing the warning letters is to induce industry-wide learning via warning letters.

Through these two essays, this dissertation enhances the current understanding of how some of the important pharmaceutical policies and their interplay with

8 manufacturing strategy and organizational learning affect pharmaceutical quality and compliance. Our empirical findings, based on large-scale and novel secondary data sources and rigorous econometric methods, offer insights to policy makers and managers in improving pharmaceutical quality and compliance performance and, thus, public health and safety. We make a number of useful policy recommendations to the FDA, including more stringent quality monitoring of pharmaceutical plants producing generic drugs located in non-advanced economies, improved transparency of drug manufacturing plant information, and more efficient and transparent public communication of failure events.

We also suggest that managers of generic pharmaceutical firms be cognizant of greater quality risk in emerging or developing countries, when considering offshoring their manufacturing to those regions.

Table 1. Drivers of quality and compliance performance examined in the representative studies in the literature

Category of quality and compliance drivers

Dependent Industry Unit of Paper Policy Mfr. Learning variable sector analysis strategy Own Vicarious

Levin (2000) Repair rate Auto Car model - - Production - experience Ittner et al. Defect rate Electro- Plant-year - - Production (2001) mechanical experience Haunschild Recall Auto Firm-year - - Production - and Rhee experience; (2004) Previous recall Novak and Quality Auto Firm-year - Outsourcing - - Stern (2008) rating Thirumalai Recall Medical Firm-year - - Previous - and Sinha device recall (2011) Gray et al. cGMP Pharma, Plant- - Offshoring - - (2011) compliance Biologics inspection Gray et al. cGMP Pharma Plant- - Colocation - - (2015) compliance inspection with R&D Steven and Recall Consumer Firm-year - Outsourcing; Britto (2016) goods Offshoring Ball et al. Recall Medical Plant- - - Investigator - (2017) hazard device inspection, experience recall Shah et al. Recall Auto Model-year - Plant variety; - - (2017) Utilization Ball et al. Recall Pharma Firm-year Promoting - - - (2018) competition Maslach et Adverse Medical Firm- Disseminating - - Adverse al. (2018) event device month failures event reports in online database

Chapter 2 Recall Pharma Drug-year Promoting Outsourcing; Production - of this competition Offshoring experience; dissertation Previous recall Chapter 3 cGMP Pharma Plant- Disseminating Similarity in - Warning of this compliance inspection- failures location or letters dissertation CFR process issued to other plants

Chapter 2. Are Generic Drugs Safe, Regardless of Production Location? A Drug Level Analysis

2.1. Introduction

Nearly half of the U.S. population, or over 150 million people, take at least one prescription drug daily, and this number has been increasing yearly.14 Not surprisingly, this places a significant burden on health care costs. In 2018, the per capita annual spending on pharmaceuticals in the U.S. was just over 1,000 USD; total spending was thus over 300 billion USD.15 The primary government response to the increasing use and costs of drugs has been to stimulate competition through generic drugs, including expedited pathways to generic drug approvals (FDA 2019a). The former commissioner of the Food and Drug Administration (FDA) Scott Gottlieb explicitly stated: “As part of the

FDA’s efforts to promote drug competition and patient access, we’ve advanced many policies aimed at making it more efficient to bring generic competition to the market.” 16

These steps have been effective, as evidenced by the explosive growth of the generic drug market (AAM 2018).

14 https://www.medscape.com/viewarticle/500164 15 https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and- reports/nationalhealthexpenddata/nationalhealthaccountshistorical. html 16 https://www.fda.gov/news-events/press-announcements/statement-fda-commissioner-scott-gottlieb-md- 2019-efforts-advance-development-complex-generics 11

Combatting increasing drug costs by promoting generic drugs hinges upon two key assumptions. Specifically that generics are, relative to the original brand-name drug

(hereafter “original” or “original drug”), both 1) lower in cost and 2) equivalent in quality

(FDA 2019b). The FDA reports that generics are, on average, sold at an 85% lower price than the original (FDA 2019b), indicating that the first assumption is true. This is not surprising as firms that make generic drugs are not burdened with high research and development (R&D) expenses required to bring an original drug to market, thanks to an abbreviated drug approval process (Hatch-Waxman Act 1984). And, generic drugs exist in a market with competition—or at least the potential for competition, whereas prior to patent expiration the original drugs enjoy monopoly power (FDA 2019d). The FDA explicitly assures consumers that the second assumption is true, stating on its website

(emphasis added): “Generic drugs are held to a rigorous approval standard and are as safe and effective as brand name drugs. Importantly, generic drugs manufactured outside the

U.S. must meet the same approval standards as those made domestically.”17 This assurance of consistent quality of generic drugs, independent of where the drug is manufactured, explains the explosion in generic drug manufacturing in countries that enjoy significantly reduced costs of manufacture.18 In particular, nearly fifty percent19 of prescription generic drugs sold in the U.S. are manufactured in countries defined by the

17 https://www.fda.gov/news-events/fda-voices-perspectives-fda-leadership-and-experts/safety-efficacy- and-quality-remain-top-priorities-we-continue-our-work-expand-access-cost-saving 18 https://www.wbur.org/npr/723545864/the-generic-drugs-youre-taking-may-not-be-as-safe-or-effective- as-you-think 19 This number is based on our drug labeling data – i.e., Structured Product Labeling (SPL) – that we downloaded from DailyMed in October 2018. The detailed description of this dataset is provided in Section 3, and the descriptive statistics regarding the number of generic and brand-name drugs manufactured in advanced and non-advanced economies are provided in the Appendix. 12

International Monetary Fund (IMF) as “non-advanced economies.”20 Examples of non- advanced economies that manufacture generic drugs sold in the U.S. include India,

China, and Brazil, with the overwhelming majority coming from India.21 In terms of advanced economies, most of the generic drugs sold in the U.S. are made in the U.S.,

Canada, or Europe. This advanced vs. non-advanced economy distinction is vital to explore the quality implications of generic drug manufacturing. Among other differences between advanced and non-advanced economies, drug quality control regulations and enforcement differ significantly.

In the U.S., FDA oversight of manufacturing includes unannounced and stringent plant quality and compliance inspections that are not time constrained (FDA 2012a).

While the FDA conducts inspections of foreign plants, and has increased these inspections in recent years (FDA 2019j), foreign inspections are typically pre-announced, due to logistical and political challenges to conducting unannounced inspections. In advanced economies outside of the U.S., the FDA leverages harmonization agreements with foreign regulatory bodies that align their quality control regulations and enforcement approach with those espoused by the FDA (FDA 2020a), including recognizing the surveillance inspections of all countries in the European Union by the end of 2019 (FDA

2020c). However, in non-advanced economies, where the explosion of generic drug manufacturing is taking place, harmonization agreements are infrequent and the foreign regulatory bodies are relatively weak and poorly resourced (Bloomberg 2012). Such

20 The IMF’s main criteria for this distinction are as follows: (1) per capita income level, (2) export diversification, and (3) degree of integration into the global financial system (WEO 2020). 21 https://www.gbreports.com/article/the-worlds-pharmacy-indias-generic-drug-industry 13 relatively weak regulatory regimes in non-advanced economies may be more detrimental to generic drug safety than to that of brand, as the quality-focused regulations may be more necessary for generic drug manufacturing due to the intense cost pressure generics manufacturers face. Low-quality generics would be a significant health and safety concern for the nearly half of the U.S. population that takes daily medications, as almost

90% of the prescriptions dispensed in the U.S, are generic drugs (FDA 2019h). Thus, we examine the product safety ramifications of generic drugs, and their manufacturing location, addressing two main research questions: 1) Are generic drugs as safe as original drugs? and 2) Is this relationship dependent upon manufacturing location?

While the assumption of equivalent quality between generics and their original drugs, independent of manufacturing location, has been questioned in anecdotal reports

(e.g., HHP 2018, RAPS 2019), it has not been rigorously, econometrically tested until now, due in large part to the difficulty of determining with confidence the country in which a drug is made.22 In this study, we match generic drugs to their original drugs along three dimensions: active pharmaceutical ingredient (API), dosage form (DF), and route of administration (RA). These are the three key dimensions used by the FDA to establish generic drug equivalency to original drugs (FDA 2019b).23 This measurement precision was accomplished by exploiting a previously unused source of drug labeling

22 https://www.healthaffairs.org/do/10.1377/hblog20200203.83247/full/ 23 In addition to these three key criteria, dosage strength is another criterion for pharmaceutical equivalence. As the unit of analysis of our study is approval number(-year), which may involve multiple dosage strengths, we do not use strength as a matching criterion in our study. Some FDA studies have also used the three key criteria we use (i.e., API, DF, RA), not considering strength, for determining equivalent drugs (e.g., FDA 2017a, 2019c). 14 data, the Structured Product Labeling (SPL) dataset.24 SPL provides detailed and difficult-to-collect drug-level data for all drugs approved for sale in the U.S.25 In addition to the detailed drug information, SPL includes manufacturer name and importantly, manufacturing plant identifier,26 allowing us to link a drug to the specific plant in which it was manufactured.

We operationalize drug safety as serious drug recalls. Drug recalls are classified by the FDA into three categories. Class I recalls are associated with life-threatening defects; Class II recalls lead to serious but medically-reversible harm; Class III recalls are associated with defects which do not pose a health or safety risk to the consumer.27 Past

FDA-recall research treats Class I and II recalls as serious (Thirumalai and Sinha 2011) while treating Class III recalls as discretionary and significantly less serious (Ball et al.

2018). Consistent with this precedent, we focus on Class I and II drug recalls as our dependent variable. In sum, we analyze the safety of 2,892 drugs (2,194 generic and 698 original) that experience 367 serious recalls produced by 224 firms across 10 years.28

If generic drugs are in fact equivalent in quality to their original drugs, there should be no difference in recall propensity compared to their original drug counterparts, independent of manufacturing location. Interestingly, our analysis indicates that generic drugs appear to be as safe as their comparative original counterparts overall, but that this

24 SPL is a document markup standard approved by Health Level Seven (HL7), and is adopted by FDA as a mechanism for exchanging product and facility information using extensible markup language (XML) (FDA 2009). 25 https://dailymed.nlm.nih.gov/dailymed/spl-resources-all-drug-labels.cfm 26 The plant identifier provided in SPL is DUNS number that can be linked to the Dun & Bradstreet (D&B) and other FDA databases. 27 https://www.fda.gov/drugs/drug-recalls/fdas-role-drug-recalls 28 By design, our matched sample includes only original drugs for which a generic equivalent(s) exists. 15 is driven by drugs made in advanced economies. In non-advanced economies such as

India, where an ever-increasing number of generic drugs are manufactured, generic drugs are significantly more prone to serious recalls than their original drugs counterparts. In particular, among drugs produced in non-advanced economies we find a 156 percent increase in serious drug recalls for generic drugs relative to their original drug counterparts.

The results of this study have policy implications. These include 1) more frequent unannounced overseas inspections in locations in non-advanced economies, 2) greater generic drug manufacturer location transparency, 3) greater transparency of drug quality, such as plant- and drug-level quality scores, and 4) third-party testing of random samples, particularly for generic drugs made in non-advanced economies. We note that this study is an outgrowth of a multi-million dollar, multi-university FDA grant focused on identifying and reducing drug quality risks. While we have reported our findings to the

FDA, they do not necessarily endorse our policy prescriptions.

2.2. Hypotheses

2.2.1. Generic vs. Original Drug

Our theorizing that generics are less safe than original drugs is grounded in two key constructs: knowledge and incentives. As predicated by the structure of the generic drug industry, generic firms are less knowledgeable about the drug because they are not the firm that invested substantial resources to bring a new drug to market (DiMasi et al.

2016). In combination with less knowledge, generic firms also operate in a market that

16 prioritizes cost over quality, as quality is not visible to buyers (Woodcock and Wosinka

2013). Contrary to this, original drug firms are incentivized by efficacy and safety—that is, by high quality of drugs, given their price premiums and reputational values (Pile

2004).

To make a generic drug, the firm begins with the published API, DF and RA provided by the original drug firm in the publicly available product approval documentation (DailyMed 2019a, FDA 2019f), and then reverse-engineers the manufacturing process (Bansal and Koradia 2005). The organizational learning literature indicates that reverse engineering is a form of mimetic knowledge creation (Nelson and

Winter 1982, Baum and Ingram 1998) which is less effective than the knowledge creation established by the original product creator (Liebeskind 1996, Ryall 2009). Because the manufacturing process used by the original drug firm is typically a trade secret, generic drugs become “an approximation rather than a duplicate of the original” (Eban 2013).

Generic firms also compete on cost (FDA 2017b), not on quality, because equivalent quality is presumed by buyers and is quite difficult for a consumer to ascribe to a drug. While the original drug still enjoys a certain level of product differentiation that translates into higher prices even after patent expiration, generic firms can command no such price differential (FDA 2019h). Two FDA economists explicitly stated that “the heart of the [drug] quality problem is the fact that generic manufacturers compete on price… [because] buyers … consider any given products as perfect substitutes”

(Woodcock and Wosinka 2013, p. 171). In fact, it is often difficult for a consumer to determine the generic manufacturer location, or even name, making quality competition

17 even less likely as is difficult to associate directly with the generic drug (LAT 2018).

Further, it is rarely the consumer that chooses their drugs. Rather, typically doctors identify the medication, the pharmacies choose the manufacturer based on pricing incentives from their wholesaler and the drug manufacturer, and pharmacy benefit managers decide the end price to the patient by determining the reimbursement rate of their medication. These downstream entities are known to aggressively seek price reductions from manufacturers (Fein 2018). Thus, market mechanisms provide limited incentives for generic firms to invest in and maintain consistently high quality (Kranton

2003, Martin 2015). These knowledge and incentive differences lead us to hypothesize:

Hypothesis 1 (H1). A generic drug is associated with a greater number of serious

recalls than its corresponding original drug.

2.2.2. Advanced vs. Non-Advanced Economies

In such an environment, in which the nuanced knowledge of how to make a drug may be insufficient, and the incentives lead generic firms to focus more on cost and less on quality, the role of the regulator to stimulate and ensure product safety is tantamount.

Consequently, we ground Hypothesis 2 in the varying roles played by a regulator in disparate global manufacturing locations. In doing so, we build upon previous research that distinguishes between advanced economies and non-advanced (i.e., developing and/or emerging) economies in terms of regulatory effectiveness (Peng 2003, Young et al. 2008, Marquis and Qian 2014).

In advanced economies, the regulator’s role is one of substantial power and influence (Levitsky 2009). In the U.S., for example, the FDA conducts surprise plant inspections for all domestic manufacturing plants; these inspections are supposed to occur at least once every two years, but not all plants are visited on that frequency (FDA

2014). The unannounced nature of these inspections helps ensure the FDA witnesses the true state of manufacturing quality and compliance (FDA 2015). In advanced economies outside the U.S., drug product regulators operate similarly. The two primary non-U.S. advanced economies that manufacture drugs are Canada and Europe. In the U.K., the

Medicines and Healthcare products Regulatory Agency (MHRA) regulates drugs. In its role, the MHRA works to ensure that drugs marketed in the U.K. meet applicable quality and safety standards, by such means as Good Practice inspections and efficient adverse event reporting systems (MHRA 2020). In the European Union, as well as in Canada, similar regulatory activities are conducted by their regulatory agency (EMA 2020, CFIA

2020). Not only do these regulatory bodies outside of the U.S. maintain significant authority to stimulate and enforce high quality and compliance, but they also have close working relationships with the FDA and have established harmonization agreements with the FDA to ensure similar regulatory approaches across advanced economies (FDA 2016,

FDA 2020a, FDA 2020c, MHRA 2020).

However, this is not necessarily true in non-advanced economies, in particular in the locations where a majority of generic drugs are manufactured, such as India and

China (Pezzola and Sweet 2016). In India, for example, the Central Drugs Standard

Control Organization (CDSCO) regulates drugs. The CDSCO has fostered a questionable

19 reputation in which drugs are approved without sufficient demonstration of safety,29 manufacturers are allowed great leeway in quality and conformance standards,30 and drug manufacturing firm corruption is tolerated.31 The Chinese drug regulator, the National

Medical Products Administration (NMPA) has developed a similar record.32 With such concerning histories, the FDA attempts to take a more proactive role in non-advanced economies that have less rigorous regulators, however their efforts are often stymied by geopolitical hurdles. For example, the FDA is typically expected to notify a plant many weeks in advance before conducting an inspection and faces challenges such as language barriers (GAO 2019). Historically, these inspections have also been less frequent, although more foreign inspections are being conducted every year (FDA 2019j).

Anecdotal evidence indicates, not surprisingly, that less frequent, as well as pre- announced, inspections foster complacency and at times conscious deception leading to low quality and non-compliance practices and potentially unsafe products (Eban 2019).

Hours of congressional testimony in June 2020 articulated similar concerns (COVID-19 and Beyond 2020).

In sum, in the generics market where intense cost-based competition prevails and quality risk is not visible to the customers, effective regulatory regimes are necessary to ensure minimum quality requirements are being achieved (Becker 1968, Kolstad et al.

1990, Cohen 1992). Non-advanced economies, however, have less mature regulatory

29 https://www.moneycontrol.com/news/business/the-dangerous-cocktail-of-substandard-drugs-and-poor- regulatory-oversight-in-india-4369661.html 30 https://cen.acs.org/articles/94/i16/Indian-drug-firms-struggle-quality.html 31 https://www.thehindubusinessline.com/opinion/pharma-industry-has-a-deep-culture-of- corruption/article22984560.ece 32 https://www.china-briefing.com/news/why-corruption-is-inevitable-in-chinas-pharmaceutical-industry/ 20 systems to enforce this minimum level of quality. Thus, while generic drugs may have an inherent potential to experience many serious recalls because of the reduced level of knowledge and cost-focused incentives in any location, this will be aggravated when the generic drug is made in a non-advanced economy. On the other hand, the quality of brand-name drugs will not be as prone to weak regulations in non-advanced economies.

This is because brand-name drug manufacturers face more ex post liability – or what the quality literature would call “external failure costs” – due to their higher margins and reputational values (Cook 1998, Pile 2004). Aligned with this argument, economic theory has formally shown that, when quality is not perfectly observable, reputation incentivizes firms to operate as mistake free as possible and to produce high quality products

(Rogerson 1983, Board and Meyer-ter-Vehn 2013). As such, we hypothesize the moderating effect of non-advanced economies as follows:

Hypothesis 2 (H2). The difference in serious recalls between a generic drug and

its corresponding original drug is greater when the generic drug is manufactured

in a non-advanced economy.

2.3. Method

2.3.1. Sample

The sampling frame for our study is prescription (Rx) drugs on DailyMed33 which provides the labelling information of all drugs currently marketed in the U.S. (DailyMed

33 This website is the official online repository of the drug labeling information submitted to the FDA. It is maintained by the U.S. National Library of Medicine and provides online browsing as well as bulk 21

2019b). These labels are available as SPL files. SPL contains detailed information about each marketed drug, including indication and usage, as well as drug identification information such as the National Drug Code (NDC), approval number, APIs, DF, and

RA. SPL also includes the drug establishment registration and listing (R&L) data, which contains the name of the drug’s manufacturing plant and its Data Universal Numbering

System (DUNS) number.34 Such information is not subject to disclosure with Freedom of

Information Act (FOIA) requests; yet it is crucial to identify where the drug is made. As the FDA has accepted the R&L data electronically in the SPL format since July 2008

(FDA 2008), our sample begins in 2009.35 The end of the panel is 2018 for all drugs.36

2.3.2. Data Sources and Measures

Beyond the SPL data, we use six other databases to compile our full set of measures, which are described in Table 2.

download of drug labels. The data for our study was retrieved in October 2018; so technically 2018 only includes information on drugs approved by the download date. The recall data include all of 2018. 34 This is thanks to the California code of regulation that requires a Rx drug label to include the manufacturer of the drug: 17 C.C.R. § 10386 (1985) requires that “[t]he labeling and advertising for any prescription drug … must contain the name and place of business of the manufacturer who mixed … encapsulated … or tableted … the finished dosage form.” 35 In case the R&L data is only available after 2009 for a drug, the start of the panel for the drug is set as the earliest year when the R&L data is available. 36 DailyMed removes discontinued drugs. Thus, our study only includes drugs still registered in 2018. Because of this, our two plant-level control variables identified from SPL may be inaccurate prior to 2018. 22

Table 2. Variables, data sources, and measurement procedure

Data Variable† Type Measurement Procedure†† Source(s) Count of Class I and II recalls for each drug- year, merged to SPL using NDC number as FDA via the key. There is a total of 375 serious recalls Recall (Recall_D) DV FOIA across the 10 years of our study, or 37.5 per year on average (see Table A2-1 in the online appendix). Generic (Generic_D) IV SPL Coded as 1 if the drug is generic; 0 if original Step 1. DUNS number of the drug manufacturing plant identified from SPL Step 2. The address of the facility merged Non-advanced DECRS; from Drug Establishments Current Economies Mod. IMF; Registration Sites (DECRS) using DUNS (Non_adv_Econ_D) SPL number as the key Step 3. Coded as 1 if the country is one of the non-advanced economies per International Monetary Fund (IMF)37; 0 otherwise38 Step 1. Drug approval year identified from Product Age Drugs@ Drugs@FDA Ctrl. (Prod_Age_D) FDA Step 2. Year of observation minus the year of approval (Levin 2000) Prior Recall FDA via Number of recalls in the prior year (Ball et al. Experience Ctrl. FOIA 2018)40 (Prev_Rcall_D)39

Continued

37 The IMF classifies 39 countries and regions as advanced economies; see online appendix for the full list of advanced economies. 38 16% of the drugs in our sample are manufactured in multiple manufacturing plants. When a drug is manufactured in multiple manufacturing plants, we considered the drug to be manufactured in advanced economies if all those plants are located in advanced economies. 39 To alleviate the concern regarding dynamic panel bias (Nickell 1981), we also repeated the analyses in Table 3 excluding this control variable, and all our results hold. These analyses are available upon request. 40 In the online appendix, we use the total number of recalls made on each drug in all prior years and find consistent results. 23

Table 2 Continued

Data Variable† Type Measurement Procedure†† Source(s) Step 1. Drug manufacturing plant's name identified from SPL Internal Drugs@ Step 2. Firm who owns the drug rights Manufacturing Ctrl. FDA; identified from Drugs@FDA. (IM_D) SPL Step 3. Coded as 1 if the manufacturing plant belongs to the firm; 0 otherwise (Gray et al. 2017)41 Annual Number of drugs Orange Number of drugs that a firm markets in a marketed by firm Ctrl. Books given year (Thirumalai and Sinha 2011) (Num_Drugs_F) (2009 ~ 2018) Number of plants Number of plants that manufacture the drug in manufacturing drug Ctrl. SPL a given year (Anderson 1999) (Num_Plants_D) Number of drugs that a plant manufactures in Number of drugs a given year (Shah et al. 2017); Average is manufactured in plant Ctrl. SPL used when a drug is manufactured in multiple (Num_Drugs_P) plants Number of dosage Number of dosage forms that a plant forms manufactured manufactures in a given year (Shah et al. Ctrl. SPL in plant 2017); Average is used when a drug is (Num_DFs_P) manufactured in multiple plants Medicaid: Drug sales volume State drug Cumulative number of drug units reimbursed Ctrl. (Volume_D) utilization by states in the last three years data FDA via Dummy variable indicating each recall year Year Ctrl. FOIA 2009 as reference category † _X at the end of variable name indicates the level, where X∈{D, P, F} and D=drug, P=plant, F=firm. †† Natural log is taken for count variables after adding 1 (except for the Recall_D, the dependent variable).

41 William Liu and Marta Wosinska, top economists at the FDA, note in Liu and Wosinska (2017, p. 472) that “[B]ecause all manufacturers face the same cGMP requirements, FDA does not explicitly distinguish in its databases which drugs are contract manufactured and which are made in-house. Given that, we compile our data by comparing the name of the NDA [New Drug Application] or ANDA [Abbreviated New Drug Application] holder with the name of the facility owner.” We follow the same approach. When a drug is manufactured in multiple manufacturing plants, we considered the drug to be internally manufactured if all those plants belong to the application holder. 24

2.3.3. Empirical Strategy

We match each generic drug to the pharmaceutically-equivalent original drug and consider generic as the treatment effect. Matching is a method of preprocessing data which can partially address endogeneity in observational research (Iacus et al. 2012). In particular, we use coarsened exact matching (CEM). CEM can be applied both when matching variables are continuous, and thus need to be “coarsened” into bins, and when they are discrete and do not require coarsening (King and Nielsen 2019), as in our case.

When matched strata include different numbers of treated and control units, the CEM algorithm produces weights to be used in a post-matching estimation stage to compensate for the differences (Iacus et al. 2012).42 Employing CEM weights is in effect equivalent to estimating the treatment effect within each stratum and then averaging those within- stratum treatment effects across all treated units, leading to an estimated average treatment effect on the treated (ATT) in the entire sample (King 2012). CEM has become increasingly popular in various contexts (e.g., Dutt and King 2014, Connelly et al. 2017,

Corritore et al. 2019), including being officially “qualified for scientific use” by the FDA

(Iacus et al. 2019).

To implement CEM, we use three drug-level categorical variables to match generic drugs to original drugs: API, RA, and DF.43 The FDA uses these three variables

42 The CEM weights (W) are calculated as follows: W for treated units = 1; W for control units = 푆 푆 푇 푇 푆 푆 푇 (푚푇/푚퐶)*(푚퐶/푚푇) where 푚푇 and 푚퐶 are the number of treated and control units in a stratum, and 푚퐶 and 푇 푆 푆 푚푇 are the total numbers of control and treated units in the sample, respectively. The first part, 푚푇/푚퐶, represents unnormalized weights, which varies over strata and makes the sum of the control units equal the 푇 푇 number of treated units. The second part, (푚퐶/푚푇), is the normalization factor, which makes the sum of the weights equal to the total number of units in the sample. 43 The CEM algorithm conducted exact matching based on three categorical variables. Thus, our matching approach achieves exact balance between treated and control groups on the matched dimensions, and has 25 to define pharmaceutical equivalence when approving generic drugs (FDA 2019b). There are 576 matched strata, each containing at least one generic and its original counterpart.

The total number of matched drugs across all strata is 2,892, and an average stratum contains 5.04 drugs (3.81 generic and 1.23 original). Table 3 provides the descriptive statistics and bivariate correlations for all variables used in the analysis. More detailed descriptive statistics are provided in the online appendix Section A.

the same logical effect of blocking in experimental research (Imai et al. 2008, Stuert 2010). This makes it unnecessary to include the matching variables in the subsequent regression models (Blackwell et al. 2009). 26

Table 3. Descriptive statistics and correlations

Variables Mean SD Min Max [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [1] Recall_D 0.04 0.23 0 7 1 [2] Num_Drugs_F 135.21 109.30 1 357 0.025* 1 [3] Num_Drugs_P 31.15 27.13 1 117 -0.037* 0.345* 1 [4] Num_DFs_P 5.26 3.60 1 18 -0.041* 0.201* 0.744* 1 [5] Num_Plants_D 1.18 0.56 1 9 0.061* 0.019* -0.128* -0.068* 1 [6] Prod_Age_D 13.21 12.43 1 77 0.007 0.107* -0.112* -0.062* 0.138* 1 [7] Prev_Recall_D 0.05 0.24 0 7 0.065* 0.026* -0.041* -0.046* 0.074* 0.030* 1 [8] Non_Adv_Econ_D 0.30 0.46 0 1 -0.005 -0.040* 0.210* 0.102* -0.033* -0.402* -0.018* 1 [9] IM_D 0.75 0.43 0 1 -0.003 0.198* 0.301* 0.185* -0.237* -0.174* -0.013 0.171* 1 [10] Volume_D 1.4e+07 6.3e+07 0 2.3e+09 0.097* 0.085* 0.046* 0.084* 0.126* 0.229* 0.131* -0.062* 0.047* 1 [11] Generic_D 0.69 0.46 0 1 -0.013 0.128* 0.346* 0.155* -0.251* -0.518* -0.029* 0.370* 0.264* 0.019* 1 Descriptive statistics are based on untransformed variables; * p < 0.05.

2.4. Results

Because recalls are an over-dispersed count measure, we use a negative binomial estimator to test our hypotheses (Thirumalai and Sinha 2011). The results are in Table 4; firm and year fixed effects are included in all models but not reported.

Table 4. Negative binomial regression result

Recall_D (Class I and II) Model 1 Model 2 Model 3

Num_Drugs_F -0.157 -0.180 -0.203 (0.251) (0.252) (0.253) Num_Drugs_P -0.042 -0.054 -0.056 (0.075) (0.076) (0.076) Num_DFs_P -0.104 -0.101 -0.117 (0.126) (0.127) (0.127) Num_Plants_D -0.075 -0.058 0.046 (0.160) (0.160) (0.164) Prod_age_D -0.157* -0.148* -0.130* (0.065) (0.066) (0.066) Prev_Recall_D 0.549** 0.550** 0.528** (0.192) (0.172) (0.168) Non_Adv_Econ_D -0.097 -0.105 -1.194** (0.123) (0.123) (0.417) IM_D -0.182+ -0.183+ -0.190+ (0.104) (0.104) (0.104) Volume_D 0.147** 0.148** 0.146** (0.014) (0.014) (0.014)

Generic_D 0.133 0.070 (0.159) (0.161)

Generic_D*Non_Adv_Econ_D 1.256** (0.436)

Fixed effects Firm Y Y Y Year Y Y Y

Observations 17562 17562 17562 Wald-Chi2 1914.84 1908.44 1919.99 Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

We observe in Model 1 (control variables only) that, while number of recalls made in the prior year (Prev_Recall_D) and number of units sold in the last three years (Volume_D) are positively related to recalls, product age (Prod_Age_D) and internal (vs. contract) manufacturing (IM_D) are negatively associated with recalls, with the latter being marginally significant (p<.1). Moving to Model 2, we observe that the coefficient for

Generic_D is not significant, although positive (훽=0.133; p>.05), indicating that H1 is not supported. In Model 3, the coefficient for Generic_D and Non_Adv_Econ_D is positive and significant (훽=1.125; p<.001), providing strong support for H2. This result, combined with the non-significant first-order effect of Generic_D on serious recalls, suggests that the relationship between generic drugs and the frequency of serious recalls is contingent on where the drug is made. Figure 2 shows this moderating effect of manufacturing location (i.e., Generic_D and Non_Adv_Econ_D interaction).

Setup 10: Firm FE; Cnt Pred; Allvars; sd/sm

. s

0 .

c i

r P

0 Advanced Non-advanced Economy Economy Manufacturing Country

Brand Generic

Figure 2. Generic_D * Non_Adv_Econ_D interaction plot

Consistent with H2, the predicted number of serious recalls made on generic drugs increases when they are manufactured in non-advanced economies. Interestingly for brand-name drugs, the predicted frequency of recalls decreases when manufactured in non-advanced economies. Research has shown that organizational slack resources may lead to improved performance (Bromiley 1991, George 2005), including quality performance (Moreno et al. 2009, Mohr and Young 2012). Thus, a plausible explanation for this observation may be that, with brand reputation being a quality protection mechanism (Board and Meyer-ter-Vehn 2013) and with the competition being based on quality (vs. cost), the extra resources freed up by the reduced costs in non-advanced economies are redirected to further improving quality.

2.4.1. Identification, Endogeneity, and Robustness Checks

The identification of our econometric model is aided by the fact that the original drugs’ loss of patent or market exclusivity protection occurs for all drugs, by law (FDA 2019d).

In addition, by matching generic drugs to their original drugs using the criteria defined by the FDA to determine pharmaceutical equivalence, we contend that this approach, plus the inclusion of several controls in our analysis, accounts for most plausible alternative explanations for our results (Wang et al. 2018, FDA 2019g).

We do perform several robustness checks related to measurement and analysis.

We classify our robustness checks into five categories. First, we repeated the negative binomial regression analysis with firm and year fixed effects with additionally including stratum fixed effects. Second, we tested a logistic panel regression model examining the

30 existence of any recalls (1) or not (0) for our dependent variable, as opposed to a count as in our main analysis. Third, we used all recall classes (including Class III recalls) as an alternative dependent variable. Fourth, we excluded 40 strata where a single firm manufactures both original and equivalent generic drugs, and repeated the analyses. Fifth, we repeated our analyses using drug sales volume in the previous year, as opposed to previous three years in the main analysis. Results of all of these robustness checks are summarized in Section B in the online appendix. The lack of support for H1 and the continued support for H2 (p<.01) remains in all models.

2.4.2. Post-hoc: Moderating effect of learning from experience, learning from failure, and internal manufacturing

We explore additional moderating factors that may enhance the safety of generic drugs, compared to that of the corresponding original drugs. We first examine learning – more specifically, learning from production experience (Argote and Epple 1990, Levitt et al.

2013) and from failure experience (Sitkin 1992, Madsen and Desai 2010). We investigate learning from experience, because the literature has shown that increased experience leads to improved product quality and that such learning is influenced by the timing when the product was introduced – that is, more recently introduced products have faster learning than do products debuted earlier (Levin 2000). We operationalize production experience as the number of years that the drug has been on the market (i.e.,

Prod_Age_D; Levin 2000), and failure experience as past recalls on that drug (i.e.,

Prev_Recall_D; Thirumalai and Sinha 2011). Because we only study original drugs with generic counterparts, the original drug firms in our sample may be systematically further 31 down their learning curve (Yelle 1979) compared to generic drug firms, due to patent protection preventing generic entry for years (FDA 2019d). Considering the typical decreasing rate of learning over time (Epple et al. 1991), generic firms may benefit more from manufacturing learning (i.e., enjoy a steeper learning curve) than their original counterparts in improving drug safety. This may hold whether such learning originates from production experience (Levin 2000) or from failure experience (Thirumalai and

Sinha 2011). We also explore internal manufacturing (vs. contract manufacturing) (i.e.,

IM_D), as contract manufacturing has been shown to have quality implications (Novak and Stern 2008, Gray et al. 2017), yet is an increasingly popular practice in the pharmaceutical industry for both branded and generic drugs (Ernst and Young 2017).

Outsourcing manufacturing inevitably involves knowledge transfer across firm boundaries, which can be challenging to the extent that causal ambiguity is present (Reed and DeFillippi 1990, Kogut and Zander 1992, Gray and Handley 2015, Gray et al. 2015).

Thus, generic firms’ incomplete understanding about the drug and its manufacturing process, if any, may be more pronounced when the drug is contract-manufactured. This implies that generic firms may relatively gain more from internal manufacturing compared to original drug firms.

Table 5 below shows the moderation effect of learning from experience (Model

1), learning from failure (Model 2), and internal manufacturing (Model 3) on the generic- recall relationship. In Model 4, all interactions, including the Generic_D and

Non_Adv_Econ_D interaction, are included. We find that, among the factors that may potentially reduce generic drug recalls relative to the corresponding branded counterparts,

32 only the interaction between Generic_D and learning from experience (Prod_Age_D) is significant (p<.05). The negative and significant coefficient indicates that, on average, the rate of learning-by-doing is significantly higher for generic drugs compared to that of brand-name drugs, consistent with our expectation.

Table 5. Post-hoc analysis: moderating effect of learning from experience, learning from failure, and internal manufacturing

Recall_D (Class I and II) Model 1 Model 2 Model 3 Model 4

Num_Drugs_F -0.150 -0.313 -0.205 -0.285 (0.255) (0.256) (0.253) (0.259) Num_Drugs_P -0.048 -0.050 -0.051 -0.047 (0.076) (0.076) (0.076) (0.076) Num_DFs_P -0.126 -0.097 -0.102 -0.137 (0.127) (0.127) (0.127) (0.127) Num_Plants_D -0.052 -0.023 -0.031 0.112 (0.161) (0.162) (0.163) (0.169) Prod_Age_D 0.057 -0.147* -0.146* 0.093 (0.120) (0.066) (0.066) (0.121) Prev_Recall_D 0.542** 0.524* 0.541** 0.469+ (0.168) (0.243) (0.168) (0.244) Non_Adv_Econ_D -0.121 -0.112 -0.102 -1.250** (0.123) (0.122) (0.122) (0.420) IM_D -0.173+ -0.187+ -0.112 -0.114 (0.104) (0.104) (0.151) (0.149) Volume_D 0.156** 0.150** 0.148** 0.156** (0.014) (0.014) (0.014) (0.014)

Generic_D 0.918* 0.137 0.229 1.012* (0.419) (0.162) (0.217) (0.460)

Generic_D*Prod_Age_D -0.299* -0.324* (0.146) (0.147) Generic_D*Prev_Recall_D -0.022 0.005 (0.323) (0.324) Generic_D*IM_D -0.164 -0.121 (0.218) (0.197) Generic_D*Non_Adv_Econ_D 1.307** (0.439) Fixed effects Firm Y Y Y Y Year Y Y Y Y

Observations 17562 17562 17562 17562 Wald-Chi2 1905.71 1892.13 1907.30 1900.03 Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

2.5. Discussion, Limitations, and Conclusion

Our study examines generic drug safety. We find that, overall, generic drugs are as safe as their brand-name counterparts, as measured by serious recalls. On the surface, this result is assuring, as generic drugs are designed to “have the same risks and benefits as the brand-name medicines” (FDA 2019b). However, this finding is contingent on where the drug is manufactured. More specifically, the generic drugs that are manufactured in non-advanced economies such as India and China are not as safe as the corresponding brand-name drugs. We believe that this is driven by the combination of less stringent regulatory oversight in non-advanced economies and the severe cost competitiveness, and inability to differentiate on quality, in the generics market. From the U.S. drug consumers’ perspective, this result is alarming, considering that generic drugs account for

90% of prescriptions dispensed in the U.S., of which 50% are made in non-advanced economies.

The results are practically significant. A generic drug manufactured in a non- advanced economy is associated with 156 percent44 more recalls than the corresponding original drug made in a similar location. Whether or not these increased number of serious recalls are a greater social cost than the $8.8 billion annual savings enabled by generic drugs that are in large part manufactured in low-cost, non-advanced economies

(FDA 2019c) is a policy consideration. Rather than abandoning generic manufacturing in

44 The predicted number of generic drug recalls when manufactured in non-advanced economies is 0.034 and that of brand-name drug recalls when manufactured in similar locations is 0.013. Thus, generic drugs are associated with 156% (= (0.034-0.013)/0.013*100) more frequent serious recalls compared to brand- name drugs when they are manufactured in non-advanced economies. 35 non-advanced economies, we recommend practical policy changes to make their quality risk more comparable to original drugs, as explained below.

First, we propose that closer scrutiny of generic manufactures located in non- advanced economies by FDA may help alleviate the effects we uncover. More frequent inspections may help stimulate higher quality of generic drugs made in those locations; indeed, FDA inspections have been shown to act as product quality renewal mechanisms for plants (Anand et al. 2012). The FDA may also need to increase the frequency of routine unannounced inspections by the FDA inspectors who understand the language and culture (Eban 2019, GAO 2019). Additionally, because generic quality is indistinguishable to doctors, pharmacists, and patients until a recall is announced, the

FDA may be able to encourage greater generic drug quality by making quality differences more transparent. For example, the FDA can require a quality risk score, perhaps akin to their plant-level risk scores (FDA 2019g) or drug-level quality scores recently developed by other institutions (Dabestani et al. 2020), as a mandatory item on drug labels. At a minimum, steps should be taken to make the drugs’ manufacturing location information more accessible to patients and providers. Finally, the current practice is that the safety of the vast majority of drugs consumed in the U.S. relies on self-testing by the firms selling the drugs (NYT 2013, FDA 2020e). Thus, the FDA may want to encourage, if not require, random testing of drugs by independent parties, particularly those drugs that are manufactured in non-advanced economies.

Additionally, our post-hoc analysis indicates that generic firms’ learning from experience significantly enhances the safety of generic drugs compared to brand-name

36 drugs, while learning from failure or internal manufacturing does not. Doctors and patients may want to choose a generic drug that has been on the market sufficiently long, especially if all available generic versions are made in non-advanced economies.

However, the step that needs to be taken first is again to improve transparency – that is, to make it easier for patients and providers to know which specific generic drug that they are taking and prescribing (LAT 2018) and when the drug was approved by the FDA, in addition to where the drug is made.

Despite the benefits of comprehensive drug level data and careful empirical strategy, the present research has some limitations. First, recalls are not a perfect measure of drug quality risk, as recalls are in most cases voluntarily made by firms. We partially alleviate this concern by using serious, thus, less discretionary recalls and by including firm fixed effects to control for heterogeneity in firms’ general willingness to make recalls. Second, we used a dichotomous measure of manufacturing location (i.e., advanced vs. non-advanced economies). While this classification is well aligned with our theoretical arguments for the moderating effect in Hypothesis 2, other categorizations of locations – for example, U.S. vs. non-U.S., or continent-based classification – may provide useful insights.

Consumers expect the drugs they take to be safe and effective. The FDA explicitly states that “Consumers must have confidence in the safety and quality of generic medicines … regardless of where they were manufactured” (FDA 2019i). Our study indicates that consumers should not have confidence when the generic is made in a less advanced economy.

Chapter 3. Vicarious Learning from Warning Letters

3.1. Introduction

On February 25th, 2020, the U.S. Food and Drug Administration (FDA) issued a warning letter to Cipla’s manufacturing facility in Goa, India, where the company’s generic sterile injectable drugs are manufactured.45 The letter was issued due to the plant’s serious violations of current Good Manufacturing Practices (cGMP) – including cross- and microbiological contamination – which were observed during a cGMP inspection in the previous year but not sufficiently addressed to the FDA’s satisfaction.46 Consequently, the plant voluntarily suspended the production of the affected drugs, and Cipla’s new drug applications and supplements were subject to being withheld by the FDA until the plant corrected all the violations and was confirmed to be compliant with cGMP.47 The shares of Cipla also fell nearly 6% shortly after the plant received the letter.48

The warning letter issued to the Cipla plant is one of dozens of cGMP-related warning letters that the FDA sends to pharmaceutical entities (e.g., individuals, plants, and firms) in an average year.49 Generally, a warning letter is issued when the FDA

45 https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning- letters/cipla-limited-597511-02252020 46 https://www.fiercepharma.com/manufacturing/cipla-knocked-fda-warning-letter-for-sterile-injectables- plant 47 https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/warning- letters/cipla-limited-597511-02252020 48 https://www.thehindubusinessline.com/markets/stock-markets/cipla-shares-plunge-nearly-6-after-usfda- issues-warning-letter/article30920147.ece 49 https://www.bioprocessonline.com/doc/an-analysis-of-fda-fy-drug-gmp-warning-letters-0003

38 observes significant violations of federal regulations during the inspections or investigations.50 If not promptly and adequately corrected, a warning letter may lead to a more “formal” enforcement action, including seizures, injunctions, and/or civil penalties.51 Besides the pending formal enforcement action, the public nature of warning letters imposes “informal” enforcement actions against the regulated entity, such as loss in investments and sales and reputational damage (Boxhorn 2011), among others. Thus, warning letters serve as an important means for the agency to prompt compliance from the entities in this industry (FDA 2020a).

Warning letters, which are made available to public on the FDA website,52 also have important implications for the industry members who are not receiving one – more specifically, they provide opportunities to learn from others’ mistakes.53 The warning letter that was issued to the Cipla plant, for example, may have given other organizations an idea as to how to avoid the violation of specific regulations that the Cipla plant violated and could not resolve promptly. In fact, publicizing warning letters is part of the

FDA’s risk communication policy which is aimed at helping people to make more informed decisions about the FDA-regulated products and thus to promote public health

(FDA 2016a).

The idea that organizations can improve their performance by observing and examining the failures of other organizations has been studied in the vicarious learning literature (Levitt and March 1988, Levinthal and March 1993, KC et al. 2013). This

50 https://www.fda.gov/media/71878/download 51 The requested time frame is typically 15 working days after the date of receipt of the letter (FDA 2020d). 52 https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/compliance- actions-and-activities/warning-letters 53 While warning letters are available to public, the detailed observations and reports of cGMP inspections (i.e., Form 483, Establishment Inspection Report) are not publicly available.

39 notion of vicarious learning from failures has been empirically supported in several industry contexts, including hotel, bank, railroad, and airline industry (Baum and Ingram

1998, Kim and Miner 2007, Baum and Dahlin 2007, Madsen and Desai 2018).

Haunschild and Sullivan (2002) and Baum and Dahlin (2007) found that, in part thanks to vicarious learning from other firms’ failure experience, the number of accidents has been significantly decreased over time in the U.S. airline and railroad industry, respectively.

Such trends in those industries, however, run counter to that in the pharmaceutical industry where the number of failures, such as drug recalls, has steadily increased over time.54

One important observation on the extant literature is that the industries where the performance-enhancing vicarious learning was found to take place are homogeneous in nature in terms of the products or services that the firms offer to the market (Tremblay

1985, Foster 2008, Alderighi 2010).55 In such homogeneous settings, the industry members are likely to be comparable to each other in such dimensions as competitive environments, strategies, and base technologies (Porac et al. 1989, Kim and Miner 2007).

As such, the organizations may be more likely to be inclined to closely monitor each other and better positioned to extract value and learn from others’ experience (Greve

1998, Darr and Kurtzberg 2000, Baum and Dahlin 2007).

The notion of performance-enhancing vicarious learning from failure has not been the subject of empirical research in a more heterogeneous industry setting such as

54 https://www.uspharmacist.com/article/overview-of-the-fdas-drugrecall-process 55 An important dimension that determines whether an industry is homogeneous or heterogeneous is the extent of “product (or service) substitutability” (Porac et al. 1989, Foster 2008).

40 pharmaceutical industry.56 Specifically, the pharmaceutical industry consists of a highly heterogeneous set of firms, with plants that employ different processes to manufacture different pharmaceutical products for treating different medical conditions (Kremer and

Glennerster 2004, Richman 2017). As product and process designs are highly interdependent in the pharmaceutical industry (Pisano 1996), it is also likely that the organizational knowledge bases and operational routines are heterogeneous across those pharmaceutical entities (Pisano 1994, Anand et al. 2012). Such heterogeneity may hamper the organizations from understanding means-ends relationships residing in others’ failure events (Reed and DeFillippi 1990, Szulanski 1996) and thus may make vicarious learning difficult to occur (Maslach et al. 2018). Nevertheless, considering the potential public safety risk associated with any failures in this industry, as well as the

FDA’s external communication efforts, vicarious learning from failures may still take place, at least under certain conditions, in this industry context.

The literature does not give a clear answer to this question of whether and how the vicarious learning from failure among the industry members would take place in such a heterogeneous industry setting. This study fills this gap in the context of pharmaceutical industry, with the focus on the publicly available warning letters issued to pharmaceutical manufacturing plants as the potential source of knowledge for vicarious learning from failures. While there exists research analyzing the FDA warning letters to glean insights

56 One exception is Maslach et al. (2018) who examined “vicarious learning from a repository” of adverse events in the medical device industry, which is similar to the pharmaceutical industry in terms of heterogeneity. This study measures vicarious learning in a non-conventional way (i.e., correlation in the trajectories of adverse events between the focal and other firms) and, thus, is not intended to examine the performance-enhancing vicarious learning from failure.

41 into the agency’s regulatory practices (e.g., Hoy and Park 2014), no research to date has explicitly examined the vicarious learning implications of warning letters.

Vicarious learning from failure in our context would specifically mean that the focal plant monitors and observes the warning letters that are issued to other pharmaceutical plants, extracts lessons from them and, as a result, improves its quality performance. Thus, we investigate in this study, as our main research question, if more

FDA warning letters issued to other plants citing a specific Code of Federal Regulation

(CFR) lead to a lower likelihood that the focal plant violates that specific CFR, as determined by the FDA in their cGMP inspection. With this main research question, we also study two important moderating factors that may influence vicarious learning from warning letters: 1) manufacturing plant location and 2) manufacturing process, as represented by FDA-classified product categories produced in plants.57 In particular, we examine how the similarity between the focal and other plants in terms of location or manufacturing processes affect the focal plant’s vicarious learning. More specifically regarding geographical proximity, as the FDA’s regulatory function – offices and investigators – takes place on a regional basis,58 the focal plant may be more likely to consider the proximately located plants as learning targets and better motivated to learn from them (Levitt and March 1988, Madsen and Desai 2010). Regarding manufacturing process similarity, as the focus of the FDA’s cGMP regulations are intended to assure the monitoring and control of manufacturing processes,59 the focal plant may be more

57 The FDA categorizes drug products in each plant as belonging to one or more of 36 different categories, such as aerosol dispensed medication, sterile liquid, and modified-release capsules. 58 https://www.fda.gov/about-fda/contact-ora/ora-district-directors 59 https://www.fda.gov/drugs/pharmaceutical-quality-resources/facts-about-current-good-manufacturing- practices- cgmps#:~:text=CGMP%20refers%20to%20the%20Current,of%20manufacturing%20processes%20and%2 0facilities

42 inclined to closely monitor those who experienced failures if their manufacturing process is similar to that of the focal plant (Levinthal and March 1993).

We explore the above relationships at the level of specific violation types (i.e.,

CFR) that are under the cGMP regulations. More specifically, we tested these relationships with 4,308 CFR violations across 1,078 pharmaceutical manufacturing plants and their 28,380 inspection-CFR instances from 2010 to 2016, as well as 277 warning letters issued to the plants that cite 979 specific CFR violations, using first- difference GMM model (Arellano and Bond 1991). Our results show that, when no similarity dimensions are considered, there is no significant vicarious learning among the pharmaceutical manufacturing plants. This implies that the industry-wide vicarious learning from failures does not take place in this heterogeneous industry context, which is in contrast to the empirical findings when examined in, for example, airline (Hausman and Sullivan 2002, Madsen and Desai 2018), railroad (Baum and Dahlin), and orbital launch industry (Madsen and Desai 2010). We do find, however, strong evidence of vicarious learning from failures among the plants that are geographically proximate – that is, the probability that a violation of a specific CFR is found during a cGMP inspection at a focal plant significantly decreases when the other plants located geographically close to the focal plant (i.e., in the same state, if the focal plant is located in the U.S.; in the same country, if located outside the U.S.) receive more warning letters citing the specific CFR prior to the focal plant’s inspection. Based on our analysis, the probability that a focal plant violates a certain CFR decreases by approximately 31% when the FDA issues one more warning letter citing the same CFR to any of the other, proximately located, plants.

Finally, counter to our expectations, we do not find significant learning effects among the

43 plants that are similar in terms of manufacturing processes in our context. Our findings that significant vicarious learning from warning letters takes place only among the plants that are located in geographic proximity are interesting, considering the FDA’s intention of publicizing the failure events (i.e., warning letters) to diffuse the knowledge to all industry members to achieve compliance (Miner and Haunschild 1995).

3.2. Theory, Literature, and Hypothesis Development

We start this section by describing the relevant organizational learning and compliance inspection literature. In doing so, we also provide a summary of the relevant empirical findings and emphasize the points of departure of the current study. Then we present our hypotheses based on the literature as well as on the contextual characteristics of the pharmaceutical industry.

3.2.1. Organizational Learning

Organizational learning is defined as “any modification of an organization’s knowledge occurring as a result of its experience” (Madsen and Desai 2010, p. 453), where organizational knowledge generally refers to an organization’s interpretations of information, beliefs about cause-and-effect relationships, or know-how (Huber 1991).

Organizational knowledge encompasses both codified, explicit knowledge embodied in, for example, standard operating procedures and governmental regulations, and noncodified, tacit knowledge embodied in, for example, organizational members and culture (Nelson and Winter 1982, Kogut and Zander 1996). Such organizational knowledge is believed to reside simultaneously at the individual level and organization level (Simon 1991). As such, it is difficult, if not impossible, to observe “modifications”

44 in organizational knowledge itself and, as a result, the convention in the empirical learning literature is to operationally define learning as improvement in an organization’s performance as a result of its own or others’ experience (Darr et al. 1995, Argote and

Ingram 2000). We also adopt this convention in our study and define learning as the decrease in the likelihood that a pharmaceutical manufacturing plant violates a regulation associated with cGMP.

3.2.2. Learning from Success and Failure Experience

While organizational learning has been found to take place from the aggregated organizational experience – such as cumulative operational experience (Baum and

Ingram 1998), production volume (Argote and Epple 1990), or age of organization

(Haunschild and Sullivan 2002) – the literature has also examined the differential learning implications of prior success vs. failure experience (e.g., Haunschild and

Sullivan 2002, Kim and Miner 2007, Madsen and Desai 2010, Anand et al. 2012, KC et al. 2013, Hora and Klassen 2013). The rationale behind this distinction is based on the behavioral theory of the firm, which suggests that the organizational reactions to success and failure are different, where success (failure) is defined as organizational performance that exceeds (falls below) some internal aspiration level (Cyert and March 1963). On the one hand, successful experience confirms that the organization’s current set of knowledge is appropriate and, thus, induces the organization to reduce the effort of processing new information and encourages “local search” – i.e., refinement of existing organizational assumptions and beliefs (March 1981, Louis and Sutton 1991, Lant 1992). While such local search and the resulting local optima may improve the efficiency of existing

45 organizational routines, it may lead an organization to a “competency trap” (Levinthal and March 1981, Levitt and March 1988). On the other hand, failure experience challenges the organization’s existing knowledge set and status quo (Sitkin 1992) and, thus, encourages the organization to engage in “non-local” or “problemistic search”

(Cyert and March 1963, Argote and Greve 2007). Madsen and Desai (2010) discusses the following two conditions of organizational experience that should be met in order to influence organizational knowledge and, thus, learning (i.e., performance improvement):

1) experience should motivate the organization to engage in knowledge searching activities, and 2) the organization should be able to extract meaningful knowledge from experience. It is argued, and empirically supported, in their study that organizations are both more motivated and better able to extract knowledge from failure than from success experience. This is because failures are often associated with a sense of urgency and saliency (Cameron 1984, Haunschild and Miner 1997) and also reveal where the gap exists in the current organizational knowledge, for which search activities should take place (Levinthal and March 1981, Madsen and Desai 2010).

3.2.3. Vicarious Learning from Failure Experience

Organizations not only learn from their own experience, but also learn vicariously from the experience of other organizations (Levitt and March 1988, Haunschild and Miner

1997). Similar to learning from own experience, vicarious learning can also occur by observing others’ successes60 (e.g., Darr et al. 1995), as well as others’ failures (e.g.,

60 This type of vicarious learning is referred to as “imitation of routines,” which typically takes the form of reverse engineering of successful competitors’ products or benchmarking of management practices (Nelson and Winter 1982, Miner and Haunschild 1995).

46 Madsen 2009, KC et al. 2013). It is also argued, and empirically shown, that a focal organization’s performance improvement is better promoted by other organizations’ experience with failure than with success (Madsen and Desai 2010), because failures of others are typically more salient and visible than successes (Kim and Miner 2007) and because successes are often purposefully concealed (Ingram and Baum 1997). In our study, the type of experience that triggers and enables the focal pharmaceutical manufacturing plant’s learning (i.e., decrease in the probability of violating a cGMP regulation) is the FDA warning letters issued to other manufacturing plants. As receiving a warning letter from the FDA is a type of organizational failure, no matter how low the organization’s aspiration level is (Cyert and March 1963), our study also falls under this

(i.e., vicarious learning from failure experience) category of the organizational learning literature. The extant empirical evidence of vicarious learning from failures in several industry contexts is summarized in Table 6 below.

We describe some important facets of the extant literature and discuss key differences of our study from the previous works. First, as described earlier, the industry contexts studied in the literature are homogeneous in terms of the products or services that the industry members produce and offer to the market - for example, railroad (Baum and Dahlin 2007), coal mining (Madsen 2009), airline industry (Madsen 2018), natural gas (Mani and Muthulingam 2019), nursing home (Chuang and Baum 2003), and the banking industry (Kim and Miner 2007). As organizations are more likely to be comparable in those settings in terms of key organizational dimensions such as market and/or technological distance (Porac et al. 1989), it may be easier for the entities to extract, as well as appropriately interpret and apply, the lessons from the adverse

47 experience of others (Fligstein 1991, Greve 1999, Darr and Kurtzberg 2000). The pharmaceutical industry is qualitatively different, in the sense that the products that the industry members produce are heterogeneous (Kremer and Glennerster 2004, Richman

2017). For example, according to the FDA Orange Book, which shows all the drug products that are approved by the FDA and marketed in the U.S.,61 there exist over 4,000 distinct types of drugs – i.e., distinct combinations of active pharmaceutical ingredient, dosage form, and route of administration – that may be used to treat different medical conditions. Such heterogeneous nature of this industry may hamper the industry-wide vicarious learning from failure, because industry members are less likely to be comparable (Porac et al. 1989, Darr and Kurtzberg 2000) and also because extractability, interpretability, and applicability of the knowledge of others may be restricted (Greve

1999). Nevertheless, given the FDA’s strong emphasis on the communication of risk to the public (FDA 2017) and thus its deliberate role as a central knowledge diffusion mechanism (Levitt and March 1988), the failure events are highly salient and the entities may still be able to learn from others’ failure experience in this highly regulated industry.

Second, in the extant studies, the type or types of failure experience of other organizations and the performance metrics of the focal organization are not closely aligned, which may potentially lead to an erroneous conclusion that vicarious learning is

(or is not) occurring. For example, while Baum and Dahlin (2007) showed that the focal railroad’s accident cost decreased as the number of accidents of other railroads increased, it is not clear how a focal railroad’s accident due to, say, “an obstacle on the rail track” was alleviated by observing another railroad’s accident due to, say, “train signal failure.”

61 https://www.fda.gov/drugs/drug-approvals-and-databases/approved-drug-products-therapeutic- equivalence-evaluations-orange-book

48 We overcome this inherent limitation regarding mismatch between learning cause and effect in the empirical learning literature (Haunschild and Sullivan 2002) by adopting a granular unit of analysis. By doing so, the current study increases the precision of learning model specifications and provides more reliable evidence of vicarious learning from failure.

49 Table 6. Empirical findings from vicarious learning from failure literature

Paper Industry Unit of analysis/ IV (failure DV Relevant results Model experience) (performance) Chuang and Baum Nursing home Nursing home Own/others’ Chain’s increased Others' commonly named nursing home (2003) industry chain-year/ random- commonly- and use of common (vs. failures increases the focal chain’s use of effects GLS model locally-named local) naming common naming strategy (opposite to the comp. failures strategy of its prediction: focal chains may be likely to view * comp = chain (weighted by # of comp. others' difficulties as a chance to gain component beds in the failed competitive advantage; this may be better comp.); explained by competitive dynamics than by concentration of vicarious learning) locally-named comps. in the prior year Baum and Dahlin US railroad Firm (railroad)- Own and others’ Direct accident- Others’ accident experience (and own opr. (2007) industry year/random-effects cumulative prior related costs per experience) decreases the focal firm’s accident panel data GLS with opr. and accident operating mile cost per mile; aspiration * experience robust standard experience interactions – i.e., firms do more exploration errors (learn from others) when deviations from aspiration level are high.

Continued

50 Table 6 Continued

Paper Industry Unit of analysis/ IV (failure DV Relevant results Model experience) (performance) Kim and Miner US banking Firm-year/ Other banks’ Bank survival Industry-wide learning from others’ (near) (2007) Hazard model (near) failure failures is not significant, but only the in-state vicarious learning is. Madsen (2009) U.S. coal mining Firm (mine)-year/ # of own and # of disasters Coal mines learn from their own and other industry panel Poisson other mines’ experienced by a mines disaster experiences, and such learning regression disaster/minor coal mine per year effect depreciates with age (time). accident experience in the last 5 years Madsen and Desai Global orbital Orbital launch Other firms’ Orbital launch Learning from others’ failure is significant (but (2010) launch vehicle attempt/ orbital launch success learning from own failures is more significant); industry Logistic regression success and firms with more prior failure experience learn failures more from others’ failure

Continued

51 Table 6 Continued

Paper Industry Unit of analysis/ IV (failure DV Relevant results Model experience) (performance) KC et al. (2013) Hospital (cardiac Surgeon-patient- Cum. # of own Patient death Surgeons learn more from their own success surgery) surgery/logistic and other than failure, and more from others’ failure than regression with surgeons’ success; positive interaction between own surgeon, patient, successful and success and failure experience; positive hospital, and time failed surgery interaction between own failure and others’ fixed effects failure experience. Hora and Klassen Vignette-based Firm (cross- Operational Knowledge The observing (focal) firm learns more when (2013) field experiment sectional)/ANOVA similarity and acquisition the incident firm is a market leader or is similar (based on risk and OLS market leadership. intention (e.g., how to the focal firm. Market leadership * similarity managers in the likely are you…) interaction is negative and significant – that is, financial services similarity is a critical key criterion triggering and chemical knowledge acquisition, regardless of market industries) leadership.

Continued

52 Table 6 Continued

Paper Industry Unit of analysis/ IV (failure DV Relevant results Model experience) (performance) Madsen and Desai Airline industry Firm (airline)- # of accidents in # of airline Others’ failure (within the same country) (2018) year/Poisson and other firms in the accidents in a experience reduces the focal firm’s accident NB regression last 3 years (with given year rate; this vicarious learning is strengthened discounting by when the focal country’s regulatory strength is year); Regulation high. strength Maslach et al. Medical device Firm- Correlation in the # adverse events in Correlation is negatively associated with the (2018) industry month/negative historical failure a given month DV; but the conventional measure of binomial regression trends between the experience (i.e., # of adverse events of other focal and other firms) is not significantly associated with the firms focal firm’s subsequent # adverse events. Mani and Unconventional Inspection/ Cum. # of No violation Learning from others’ failure (but not success) Muthulingam well (natural gas) heckprobit inspections with detected from an is significant; Penalties associated with others’ (2019) industry in violation EPA inspection failures do not augment the above result. Pennsylvania (un)detected

53 3.2.4. Compliance Inspection

Various factors have been shown to drive conformance quality performance at manufacturing plants, as measured by inspection outcomes or violations, in the operations management and other relevant literatures. We summarize the empirical findings in the literatures in Table 7 below, and discuss those findings by the levels of the attributes studied. At the plant-level, plant location, especially relative to the location of the headquarter or R&D, and its knowledge transfer implications due to, for example, language and cultural difference, was found to affect the plant compliance inspection outcome (Gray et al. 2011, Gray and Massimino, Gray et al. 2015b). Other plant-level factors, such as plant size and age (Abualfaraj et al. 2016), participation in the industry association (King and Lenox 2000), ISO-9000 certification (Gray et al. 2015a), and

M&As (Anand et al. 2012), were also found to influence compliance inspection outcome.

At the inspection-level, previous inspection outcomes and time elapsed since last inspection affect plant inspection outcome (Macher et al. 2011, Anand et al. 2012). The inspection literature has also examined and found that inspector (or investigator) characteristics affect inspection outcomes – for example, investigators’ previous experience and level of training (Macher et al. 2011) and gender (Short et al. 2015).

Relatedly, Ball et al. (2017) found that adverse inspection outcomes at medical device plants are predictive of future recalls, and the predictive power becomes stronger when the FDA investigators who inspected the plant had more plant-specific inspection experience. As can be observed in this review, while various plant-, inspection-, and inspector-level attributes have been shown to affect plant conformance quality

54 performance, the effect of FDA warning letters issued to other plants on a focal plant’s conformance quality performance has not been studied.

55 Table 7. Empirical findings from compliance inspection literature

Paper Industry Unit of analysis/ IV DV Relevant results Model King and Lenox Chemical industry Plant-year/ robust Participation in Emission from Performance improvement rate increased in the (2000) generalized least Chemical EPA’s Toxics entire chemical industry following the inception squares regression Manufacturers Release Inventory of RCP; performance improvement rate of RCP Association (TRI), participants is lower than that of non-participants (CMA)'s standardized by Responsible Care plant size Program (RCP) Macher et al. Pharmaceutical cGMP inspection/ Inspection, and Probability of Inspection characteristics: compliance (vs. (2011) industry logistic regression investigator receiving adverse surveillance or consumer complaint) inspection, characteristics inspection days since last inspection increases P(OAI), no outcome (OAI) violation found in the last two inspections increases P(OAI) Inspector characteristics: experience (cum. # of inspections), training (# completed core/supplemental courses) decreases P(OAI);

Continued

56 Table 7 Continued

Paper Industry Unit of analysis/ IV DV Relevant results Model Anand et al. (2011) Pharmaceutical cGMP inspection/ Days since last Probability of Days since last inspection increases P(OAI); Plant industry logistic regression inspection, plant receiving adverse being merged increases P(OAI); Plant being merger or inspection acquired decreases P(OAI) acquisition outcome (OAI) Gray et al. (2011) Pharmaceutical cGMP inspection/ Plant location Quality risk score Plant location being in Puerto Rico increases industry OLS (Puerto Rico); (based on cGMP quality risk score; geographic distance between HQ-plant distance inspection HQ and plant decreases quality risk score outcome) Gray and Pharmaceutical cGMP inspection/ Language Level of Language difference between HQ and plant Massimino (2014) industry ordered probit difference conformance decreases plant conformance quality regression between HQ and quality performance; Uncertainty Avoidance cultural plant; Cultural performance dimension at HQ decreases conformance quality dimensions at HQ (based on cGMP performance and plant inspection outcome)

Continued

57 Table 7 Continued

Paper Industry Unit of analysis/ IV DV Relevant results Model Gray et al. (2015a) Pharmaceutical cGMP inspection/ ISO-9000 Quality risk score ISO 9000-certification decreases quality risk, but industry OLS Certification (based on cGMP more for the plants that are certified early than inspection those that are certified late; quality risk increases outcome) as time passes after ISO 9000 certification. Gray et al. (2015b) Pharmaceutical cGMP inspection/ Colocation Probability of Colocation between R&D and manufacturing industry probit regression between R&D and receiving adverse decreases P(OAI); effect of colocation becomes manufacturing; inspection more significant when process knowledge is tacit, tacitness of outcome (OAI) and firm size is large process; firm size; information/ communication technology

Continued

58 Table 7 Continued

Paper Industry Unit of analysis/ IV DV Relevant results Model Short et al. (2015) Various industries, Supplier audit/ Auditor/auditing Number of Auditors found fewer violations when individual including team violations found in auditors have audited the factory before, when accessories, characteristics audit audit teams are less experienced or less trained, chemicals and when audit teams are all male, and when audits plastics, and are paid for by the audited supplier others Abualfaraj et al. Natural gas Pennsylvania DEP Plant (operator) Probability that a Larger operators, unconventional wells, spud year (2016) industry Compliance characteristics violation is found (newer plants) led to lower odds of having a inspection/logistic during inspection violation; first/second year since spud year led to regression higher odds of having a violation Ball et al. (2017) Medical device Plant (cGMP cGMP inspection Recall Adverse inspection outcome increases future industry inspections, recalls) outcome; recall hazard; inspection outcome-recall hazard investigator relationship becomes stronger when investigator’s experience plant-specific inspection experience increases

59 3.2.5. Vicarious Learning from FDA Warning Letters

We have argued that the pharmaceutical industry is qualitatively different from the industry contexts studied in the vicarious learning from failure literature, in that the pharmaceutical entities produce the products that are heterogeneous (Kremer and

Glennerster 2004, Richman 2017, FDA 2020b). While vicarious learning in this heterogeneous industry may be difficult in general, we argue that industry-wide vicarious learning from failure via warning letters may still take place in the pharmaceutical industry, thanks to the following specific characteristics of the FDA warning letters.

First, the FDA warning letters are salient events. This is first because the warning letters are visible to the public, as the FDA intentionally posts them on its website as part of its risk communication policy. In the language of the vicarious learning literature, the

FDA’s posting of the letters would be classified as the “broadcast transmission” mechanism of vicarious learning, where “a single source is responsible for diffusing a new routine, practice, or structure across a population of organizations” (Miner and

Haunschild 1995).62 Another reason for such saliency is due to the negative consequences associated with the warning letters, both to the organization that the letter is given to and, more importantly, to the public health and safety (FDA 2019). As a result, warning letters may draw attention from direct and indirect stakeholders of the letter recipients as well as from public media, further promoting their visibility (Boxhorn 2011). Such visibility and saliency of failures may sufficiently motivate the focal plant to closely monitor the

62 In addition to the broadcast transmission mechanism, Miner and Haunschild (1995) introduce two other mechanisms that enable vicarious learning among organizations: first, “mimetic learning” refers to selective copying of routines from other organizations (e.g., reverse engineering, benchmarking); second, “contact learning” refers to transmission of routines through personal and formal ties between organizations and their members.

60 warning letters issued to other plants and to be engaged in search and learning activities

(Baum and Dahlin 2007), even when those others are not comparable or direct competitors of the focal plant.

Second, warning letters present detailed information about the non-compliance identified during the FDA’s plant cGMP inspections. According to the FDA regulatory procedure manual, warning letters should include descriptions of the violative conditions or practices,63 including the citation of specific regulatory references for each violation found, and should also give the recipient directions for corrective actions (FDA 2020a).

Such details and directions may allow outsiders to extract and interpret information and gain lessons from the warning letters (Madsen and Desai 2010).

Taken together, warning letters are salient events publicized by the regulatory agency, for which the details of failures – e.g., specific violations as well as the regulatory agency’s expectations – are available. Thus, even if the products and the associated manufacturing processes are heterogeneous from those of the letter recipients, we hypothesize that a focal plant may still be able to learn from those warning letters and improve its compliance performance in this context.

An operations manager at an FDA-regulated pharmaceutical manufacturing plant indicated that their plant had weekly meetings specifically to review the warning letters that the FDA newly posted on their website. If they find some citations in the letters that may also be a potential risk factor which may be picked up by the FDA during its cGMP

63 The original warning letters issued to the recipients also include the information regarding the drug products that are affected by the violation, if any. However, such product information is redacted in the public version, as it is considered as “confidential commercial information” that the FDA cannot reveal to the public (FDA 2020f).

61 inspection at their plant, they try to come up with, and deploy, a plan to remove that risk factor from their manufacturing process. Thus, we hypothesize as follows:

Hypothesis 1 (H1). Pharmaceutical manufacturing plants will learn from the

warning letters issued to other plants, such that the more FDA warning letters

issued to other plants citing a specific CFR, the lower the likelihood that the focal

plant violates that specific CFR, as determined by the FDA in its cGMP

inspection of the focal plant.

3.2.6. Moderating Effects of Geographical Proximity and Manufacturing Process Similarity

We assert that any vicarious learning effect of warning letters is likely to become greater among the manufacturing plants that are located in geographical proximity or that have similar manufacturing processes.

Regarding the role of geographical proximity, the regulatory function of the FDA

(i.e., Office of Regulatory Affairs) is divided into regional “districts” based on geography, each of which covers specific states and/or regions.64 The implication of this geography-based distinction of the regulatory function is that the likelihood that the same, or similar, set of investigators who identified the violations at, and issued the warning letters to, the other geographically proximate plants may also conduct the cGMP inspection at the focal plant.65 Thus, such warning letters may be deemed as looming threats to the focal, geographically proximate, plant and may stimulate the focal plant to

64 https://www.fda.gov/about-fda/contact-ora/ora-district-directors 65 https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/foreign- inspections/chapter-3-establishment-inspections-340-390

62 search for ways to avoid a similar failure (Levitt and March 1988, Madsen and Desai

2010).

In addition to increasing the level of motivation to engage in search activities, geographical proximity may also facilitate the transfer of knowledge, especially when the knowledge involves tacit components (Jaffe et al. 1993, Alcacer and Chung 2007, Gray et al. 2015). The mechanisms of local knowledge spillover include informal means such as common suppliers, chance encounters, and employees switching jobs (Marshall 1920).

These arguments imply that:

Hypothesis 2 (H2). Geographical proximity will positively moderate the

relationship in Hypothesis 1, such that the effect of violation-reducing vicarious

learning from warning letters will be stronger when the scope of “other” plants is

restricted to those that are located in the same physically proximate geographic

location as the focal plant.

We now discuss the potential role of manufacturing process similarity in vicarious learning from warning letters. The focus of the FDA’s cGMP – the main regulatory standard for ensuring pharmaceutical quality – is on the manufacturing process (vs. product), as the drug quality is generally not visible and population testing of drugs is difficult and expensive (Woodcock and Wosinska 2013, FDA 2018). Thus, the focal plant may be motivated to pay attention to the cGMP violations found in the similar manufacturing processes, as those may be relevant to the focal plant (Levinthal and

63 March 1993) and may signal the FDA’s regulatory emphasis and trend associated with the specific type of manufacturing processes.66

Moreover, manufacturing process similarity may also help the focal plant better understand the tacit component of the knowledge residing in the others’ warning letters – e.g., how to resolve those citations – and reduce causal ambiguity that may hamper knowledge transfer (Lane and Lubatkin 1998, King 2007). Thus, to the extent that the tacitness of knowledge prevails in the warning letters issued to others, at least in part due to the heterogeneous nature of the industry, manufacturing process similarity should facilitate vicarious learning (Reed and DeFillippi 1990). Thus, we hypothesize as follows:

Hypothesis 3 (H3). Manufacturing process similarity will positively moderate the

relationship in Hypothesis 1, such that the effect of violation-reducing vicarious

learning from warning letters will be stronger when the scope of “other” plants is

restricted to those that have similar manufacturing process as the focal plant.

3.3. Method

3.3.1. Research Design, Data, and Measures

The unit of analysis of this study is “pharmaceutical manufacturing plant67-inspection-

Code of Federal Regulation (CFR).” To test the hypothesized relationships at this granular unit of analysis, we collected our data from the following five databases that are

66 https://www.pharmaceuticalonline.com/doc/analyzing-fda-warning-letters-citing-process-validation- supplier-controls-otc-manufacture-0001 67 “FDA Establishment Identifier (FEI)” identifies distinct plants.

64 directly provided by the FDA via Freedom of Information Act (FOIA) or downloaded/scraped from the FDA’s public websites for a six-year period from 2010 to

2016 for 1,078 pharmaceutical manufacturing plants.

First, the cGMP inspection database, which we obtained from the FDA via FOIA, provided us with the population of pharmaceutical manufacturing plants and their addresses, as well as the corresponding cGMP inspection records. To be included in our sample, pharmaceutical manufacturing plants had to have three or more inspection records during the study time frame, for the identification purpose that we describe below. Second, we used the inspection citations database68 to obtain the information regarding which specific CFR items were found to be violated during a cGMP inspection at each plant. While a wide range of CFR Parts and their items appear to be violated during cGMP inspections in the citations database (e.g., CFR Title 21 Part 117, Part 210,

Part 211), this study focuses on “Part 211: Current Good Manufacturing Practice for

Finished Pharmaceuticals.” The reason for this choice is that this category comprises nearly 93% of the total number of citations found in the inspection citations database.

The CFR Title 21 Part 211 consists of 11 Subparts (i.e., Subpart A ~ K), where the classification is made based on the topical areas that the Part 211 covers. The violations in the citations database fall under 10 of these 11 subparts (i.e., Subpart B ~ K, except

Subpart A which is “General Provisions”), which comprises the unit of analysis of this study. Thus, our dependent variable is whether a plant was found to violate a specific Part

68 https://datadashboard.fda.gov/ora/cd/inspections.htm

65 211 CFR Subpart (coded as 1) or not (coded as 0). Table 8 below describes each subpart and the corresponding topical area, as well as specific CFR items.69

Table 8. C.F.R. Title 21 Part 211 – Subpart, and its topical area and the corresponding CFR items

Subpart Topical Area CFR items A General Provisions §211.1, §211.3 B Organization and Personnel §211.22, §211.25, §211.28, §211.34 C Buildings and Facilities §211.42, §211.44, §211.46, §211.48, §211.50, §211.52, §211.56, §211.58 D Equipment §211.63, §211.65, §211.67, §211.68, §211.72 E Control of Components and Drug Product §211.80, §211.82, §211.84, §211.86, §211.87, Containers and Closures §211.89, §211.94 F Production and Process Controls §211.100, §211.101, §211.103, §211.105, §211.110, §211.111, §211.113, §211.115 G Packaging and Labeling Control §211.122, §211.125, §211.130, §211.132, §211.134, §211.137 H Holding and Distribution §211.142, §211.150 I Laboratory Controls §211.160, §211.165, §211.166, §211.167, §211.170, §211.173, §211.176 J Records and Reports §211.180, §211.182, §211.184, §211.186, §211.188, §211.192, §211.194, §211.196, §211.198 K Returned and Salvaged Drug Products §211.204, §211.208

Third, the warning letter data, including the plant identifier (i.e., FDA

Establishment Identifier or FEI) that received the letter and the date of issuance, was obtained from the FDA’s compliance actions database. Because the compliance actions database does not contain the warning letter texts (i.e., actual letter contents), we separately scraped and merged the warning letter texts from the FDA’s warning letter

69 The details of each CFR item are available here: https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?CFRPart=211

66 database70 to the warning letter records obtained from the compliance actions database.71

We then extracted from the text of each warning letter the citations referencing the CFR

Part 211 items described in Table 3 above, if any.72 For each extracted CFR Part 211 item, the corresponding subpart was then identified and merged. The resulting warning letter dataset, including all the fields described above, formed the basis of the measure of our independent variable. Finally, the plant profile codes information, which is our proxy for plant manufacturing process (Gray et al. 2015), was obtained from the FDA’s firm population database via FOIA. We used this time-invariant database when considering the similarity between plants in terms of manufacturing processes. The data sources and the measurement steps for each variable are described in Table 9 below.

70 https://www.fda.gov/inspections-compliance-enforcement-and-criminal-investigations/compliance- actions-and-activities/warning-letters 71 The warning letter database does not have the FDA establishment identifier (FEI). Thus, we manually and carefully merged each warning letter to the corresponding plant (i.e., FEI) in the compliance actions database based on the plant name and the warning letter issuance date. 72 We used “selenium” and “re” package in Python for web-scraping of and text extraction from the warning letters, respectively.

67 Table 9. Variables, data sources, and measurement procedure

Variable Type (level) Data sources Measurement procedure Viol Dependent Citations DB; 1 if the focal plant-inspection-CFR subpart was variable (CFR) Inspection DB violated; 0 O/W

OthWL† Independent Citations DB; Number of warning letters mentioning the focal variable (CFR) Compliance actions CFR subpart that are issued to other plants since DB; the previous and until the next (current) inspection Warning letter DB;

OthWL_LC† Independent Citations DB; Number of warning letters mentioning the focal variable, Compliance actions CFR subpart that are issued to other plants that are interacted with DB; located in the same state (if focal firm is located in geographical Warning letter DB; the US) or same country (if focal firm is located proximity (CFR) Inspection DB; outside the US) since the previous and until the next (current) inspection

OthWL_PF† Independent Citations DB; Number of warning letters mentioning the focal variable, Compliance actions CFR subpart that are issued to other plants that interacted with DB; have similar manufacturing process (i.e., have at manufacturing Warning letter DB; least one common profile code) since the previous process Firm population and until the next (current) inspection similarity (CFR) DB;

OwnWL† Control (CFR) Citations DB; Number of warning letters mentioning the focal Compliance actions CFR subpart that is issued to the focal plant since DB; the previous and until the next (current) inspection Warning letter DB; Inspection DB;

Continued

68 Table 9 Continued

Variable Type (level) Data sources Measurement procedure

OthViolVAI† Control (CFR) Citations DB; Number of violations of the focal CFR subpart Inspection DB found in other plants’ VAI inspections since the previous and until the next (current) inspection; this variable controls for the FDA’s regulatory focus on the focal CFR item; the measurement of this variable is aligned with that of OthWL (e.g., when location similarity is considered, this variable is the number of violations of the focal CFR subpart found in VAI inspections at other plants located in the same state or country) Compliance Control Inspection DB 1 if the current inspection is a compliance (Inspection) inspection; 0 O/W (Macher et al. 2011) Complaint Control Inspection DB 1 if the current inspection is a consumer complaint (Inspection) inspection; 0 O/W (Macher et al. 2011)

Foreign†† Control (Plant) Inspection DB 1 if the plant is located in a foreign country; 0 O/W (Gray et al. 2011)

InspFreq†, †† Control (Plant) Inspections DB Total number of inspections at the plant/number of years a plant appears in the data TimeSinceLast Control Inspection DB Elapsed time from the previous inspection (Anand (Inspection) et al. 2012) Year Control Inspection DB Inspection year; fixed effects (Inspection) u Control (Plant) - Plant dummy; fixed effects

† In the analysis (including the correlation matrix), these variables are logged after adding 1. †† These are time-invariant plant-level factors and, thus, are not estimable in our statistical model (GMM)

We provide the summary statistics and bivariate correlation matrix for the variables described above in Table 10 and 11 below, respectively, based on the 1,078 pharmaceutical plants and the corresponding 28,380 inspection-CFR subpart instances and based on the 277 warning letters from 2010 to 2016. We also provide in Table 12 the

69 number of citations of each subpart of CFR Part 211 from the inspection citations database, as well as from the warning letter database.

Table 10. Summary statistics

Variable† # Obs. Mean SD Min Max Viol 28380 0.152 0.36 0.00 1.00 PrevViol 28380 0.156 0.36 0.00 1.00 OthWL 28380 19.744 21.39 0.00 173.00 OthWL_LC 28380 0.635 1.37 0.00 17.00

OthWL_PF†† 23570 3.344 5.13 0.00 64.00

OthWL_LC_PF†† 23570 0.143 0.55 0.00 13.00 OwnWL 28380 0.005 0.07 0.00 1.00 OthViolVAI 28380 124.950 120.09 0.00 969.00 OthViolVAI_LC 28380 5.481 8.90 0.00 102.00

OthViolVAI_PF†† 23570 38.835 50.06 0.00 562.00

OthViolVAI_LC_PF†† 23570 1.862 3.97 0.00 64.00 Compliance 28380 0.140 0.35 0.00 1.00 Complaint 28380 0.023 0.15 0.00 1.00 Foreign 28380 0.149 0.36 0.00 1.00 InspFreq 28380 0.876 0.43 0.38 8.62 TimeSinceLast 28380 689.238 396.09 5.00 2583.00 † The numbers are based on original (non-logged) variables †† The plants for which the product profile code data is not available are dropped.

70 Table 11. Correlation matrix

Variable† [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]

[1] Viol 1

[2] PrevViol 0.31* 1

[3] OthWL 0.18* 0.18* 1

[4] OthWL_LC 0.12* 0.12* 0.39* 1

[5] OthWL_PF 0.20* 0.18* 0.59* 0.32* 1

[6] OthWL_LC_PF 0.12* 0.09* 0.20* 0.62* 0.38* 1

[7] OwnWL 0.05* 0.11* 0.06* 0.07* 0.10* 0.08* 1

[8] OthViolVAI 0.21* 0.20* 0.85* 0.38* 0.66* 0.22* 0.08* 1

[9] OthViolVAI_LC 0.16* 0.16* 0.40* 0.62* 0.31* 0.32* 0.07* 0.47* 1

[10] OthViolVAI_PF 0.23* 0.22* 0.61* 0.32* 0.83* 0.33* 0.09* 0.71* 0.38* 1

[11] OthViolVAI_LC_PF 0.18* 0.17* 0.31* 0.49* 0.42* 0.42* 0.06* 0.37* 0.78* 0.54* 1

[12] Compliance 0.08* 0.13* -0.11* -0.04* -0.03* -0.01 0.08* -0.10* 0.04* -0.05* 0.06* 1

[13] Complaint -0.01 -0.01 -0.04* -0.01 0.00 0.00 0.00 -0.04* -0.02* -0.01 0.00 -0.06* 1

[14] Foreign -0.06* -0.07* 0.09* -0.02* 0.10* 0.11* -0.01 0.10* -0.20* 0.05* -0.12* -0.09* -0.07* 1

[15] InspFreq 0.05* 0.05* -0.25* -0.08* -0.11* -0.03* 0.01 -0.26* -0.05* -0.16* -0.04* 0.23* 0.06* -0.19* 1

[16] TimeSinceLast 0.01 0.00 0.53* 0.17* 0.28* 0.07* 0.03* 0.57* 0.20* 0.39* 0.15* -0.19* -0.07* 0.18* -0.46* 1

71 Table 12. Count of citations for each subpart of CFR Part 211

# citations in Warning Letter Subpart # citations in citations database database B 136 610 C 119 247 D 74 555 E 62 304 F 202 725 G 27 159 H 3 78 I 190 723 J 165 888 K 1 19 Total 979 4308

3.3.2. Empirical Strategy

According to the compliance inspection literature, the outcome of a previous inspection may have an impact on the plants’ current inspection outcome (Macher et al. 2011,

Anand et al. 2012). Applied in our setting where the unit of analysis is inspection-CFR subpart, it means that the violation of a certain CFR subpart found in the previous inspection may affect the likelihood that the violation of the same CFR subpart is identified in the current inspection. Moreover, a focal plant’s violation of a CFR subpart may influence the FDA’s focus and attention to that CFR subpart, which in turn may affect the propensity that the FDA detects the violation of the same CFR subpart and even issues warning letters associated with that CFR subpart. To account for this potential endogeneity issue with respect to the violation of a CFR subpart in the previous inspection, we use the Arellano-Bond (AB) estimator (Arellano and Bond 1991) in our main analysis.

72 The AB estimator, also called difference generalized-method-of-moments (GMM) approach, is widely used to estimate dynamic panel data models73 in situations with: 1)

“small T, large N” panels – i.e., few time periods and many panels, 2) a linear functional relationship, 3) one left-hand variable that is dynamic, meaning that it depends on its own past realizations, 4) right-hand variables that are not strictly exogenous, for which

“external” instruments are not available, 5) fixed panel effects, implying unobserved heterogeneity, 6) heteroskedasticity and autocorrelation within panel units’ errors, but not across them (Roodman 2009). In such situations, with which our sample characteristics are aligned, the AB estimator provides unbiased and consistent estimates of the coefficients of the first-differenced regressors using previous lags of endogenous variables as instruments.

Specifically, the first-differenced equation that is estimated for the CFR subpart 푐 at the 푡푡ℎ inspection at plant 푝 is as follows:

∆푉푖표푙푐,푡(푝) = 훼∆푉푖표푙푐,푡−1(푝) + 훽1∆푋푐,푡(푝) + 훽2∆푊푐,푡(푝) + ∆푣푐,푡(푝)

where 푋푐,푡(푝) is the endogenous variable (i.e., OthWL, OthWL_LC, OthWL_PF ), 푊푐,푡(푝) is a vector of exogenous variables, and 푣푐,푡(푝) is the observation-specific error term (note that the plant-level fixed effect is included – and thus controlled for – but disappeared by first differencing). The first-differenced endogenous variables (i.e., 푉푖표푙푐,푡−1(푝), 푋푐,푡(푝))

73 Our data is a dynamic unbalanced panel with gaps, because there is a lagged dependent variable on the right-hand side (hence dynamic), number of inspections differ across FEIs (hence unbalanced), and time intervals between inspections are different within/across FEIs (hence gaps). 73 are instrumented by their two-period or earlier lagged values. This approach implies that this model requires at least three observations per unit of analysis to generate instruments based on lagged variables and test for endogeneity (Blundell and Bond 1998).

3.4. Results

The use of the difference-GMM estimator and the corresponding model specification is supported by multiple statistics. First, for all models, the coefficient for lagged dependent variable has an absolute value below unity (i.e., |α|<1), ensuring that the process converges (Blundell and Bond 1998). Second, the Arellano–Bond test for AR(2) in first differences is not significant (p > 0.10), indicating that there is no second-order serial correlation in residuals in differences and thus supporting the validity of using lags 2 and longer for the differences equation as GMM instruments (Arellano and Bond 1991).

Third, the Hansen test of overidentifying restrictions (Hansen 1982) fails to reject the null hypothesis of joint validity of the instruments (p > 0.10), thus offering further support for our model specification. Fourth, the coefficient of the lagged dependent variable from the difference GMM analysis falls between the coefficient from the simple OLS regression

(i.e., upper bound) and that from the plant fixed-effects regression (i.e., lower bound) in all models (e.g., for Model 1, the upper and lower bound is 0.20 and -0.29, respectively),74 further supporting the validity of our use of difference-GMM models

(Bond 2002).

74 In the OLS regression, the lagged dependent variable is positively correlated with the error, which would bias its coefficient estimate upward. On the other hand, in the fixed-effects regression, the lagged dependent variable is negatively related with the error, biasing its coefficient downward (Roodman 2009). 74 3.4.1. Main Results

Table 13 below shows the estimation results based on the difference GMM approach. In

Model 1, the coefficient of OthWL is negative but non-significant (훽 = -0.02, p > 0.10), indicating that the likelihood of violating a CFR subpart does not significantly decrease even if the number of warning letters mentioning the CFR subpart that other plants received increases. This result indicates that H1 is not supported. In Model 2 where the geographical proximity of the manufacturing plants are taken into account in counting the warning letters issued to other plants, the coefficient of OthWL (i.e., OthWL_LC) is negative and significant (훽 = -0.2275, p < 0.05), indicating that plants are less likely to violate a CFR subpart, if other plants that are located in the same state (if focal plant is located in the U.S.) or in the same country (if focal plant is located outside the U.S.) receive more warning letters mentioning the CFR subpart. Thus, this result supports H2.

In Model 3 where the similarity of plants in terms of their manufacturing process are taken into account, the coefficient of OthWL (i.e., OthWL_PF) is negative but not significant (훽 = -0.02, p > 0.10). This indicates that H3 is not supported. Although not hypothesized, we also tested the variable that captures the warning letters issued to other plants that are located in the same state (or country if foreign) and have similar manufacturing process as the focal plant (OthWL_LC_PF). The result in Model 4 indicates that this variable is also not significant (훽 = -0.10, p > 0.10).

75 As the difference-GMM estimator in our mode specification is essentially a linear probability model, this coefficient implies that 100% more warning letters (i.e., 0.64 in Table 5, as we log-transformed this variable) citing a specific CFR subpart issued to any of the other plants that are located close to the focal plant will reduce the probability that the focal plant violates the same CFR subpart by 0.22. Thus, 1 more such warning letter will reduce the probability by about 0.31 (i.e., 31%). 75 Table 13. Difference-GMM estimation results

DV: Viol (binary) Model 1 Model 2 Model 3 Model 4

OthWL -0.02 (0.05) OthWL_LC -0.22* (0.09) OthWL_PF -0.02 (0.05) OthWL_LC_PF -0.10 (0.22) OwnWL -0.25+ -0.21 -0.26+ -0.24 (0.14) (0.14) (0.14) (0.16) OthViolVAI 0.02 (0.01) OthViolVAI_LC 0.02 (0.01) OthViolVAI_PF 0.01 (0.02) OthViolVAI_LC_PF 0.01 (0.01) Compliance 0.03* 0.04** 0.01 0.01 (0.01) (0.01) (0.01) (0.01) Complaint -0.03 -0.01 -0.03 -0.03 (0.02) (0.03) (0.03) (0.02) TimeSinceLast 0.01 0.04*** 0.02 0.01+ (0.03) (0.01) (0.01) (0.01)

Violt-1 (lagged DV) 0.02 0.03 0.02 0.02 (0.02) (0.02) (0.02) (0.02)

Year dummies Yes Yes Yes Yes Lagged regressors Yes Yes Yes Yes Observations 18,050 18,050 15,150 15,150 FEI-CFR 10,280 10,280 8,410 8,410 2 휒 27.33* 32.89** 21.39+ 21.21+ AR(1) test (p-value) (0.00) (0.00) (0.00) (0.00) AR(2) test (p-value) (0.98) (0.34) (0.66) (0.87) Hansen overid. test (p-value) (0.59) (0.69) (0.92) (0.45)

Notes: Standard errors in parentheses unless stated differently; Standard errors are corrected for heteroscedasticity and clustered at the FEI-CFR level; AR(1) and AR(2) test for first- and second- order serial correlation in the first-differenced residuals, under the null of no serial correlation. Non-significance for AR(2) indicates that lags 2 and longer for the differences equation and lags 1 and longer for the levels equation are valid GMM instruments. Non-significance for the Hansen test of overidentifying restrictions supports joint validity of the instruments used. + p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

76 3.4.1. Robustness Checks

We conducted several additional analyses to ensure that our results are robust to alternative model specifications. First, we repeated the difference-GMM analyses after excluding the variables that appear to be highly correlated with other independent variables and, thus, may pose multicollinearity issues. More specifically, we estimated the difference-GMM model after removing OthViolVAI from the model,76 as it is highly correlated with the independent variable of interest (OthWL; r = 0.85). The results remained the same. We also tested the model without including TimeSinceLast, as this variable is also highly correlated with OthWL (r = 0.53) because of the way we constructed OthWL. That is, the number of warning letters issued to other plants since last inspection until the current inspection will always increase as the time between the previous and current inspection increases. The results regarding the coefficient of OthWL remained robust even if the TimeSinceLast is removed from the model. Second, we repeated our analysis after removing the inspection-CFR subpart instances for which

OwnWL is greater than 0, considering that the warning letter issued to the focal plant may be the direct consequence of the violation in the previous inspection (i.e., lagged dependent variable). The results remained consistent. Third, we tested the model after dropping the CFR Part 211 Subpart H and K, considering their small representations in the number of citations as shown in Table 12 above. The qualitative results remained the same. Fourth, it may be that the warning letters issued to other plants that are under the same firm as the focal plant have greater violation-reducing effect on the focal plant than

76 All the discussions in this section also apply to the models where we take into account geographic proximity and/or manufacturing process similarity (i.e., Model 2 ~ Model 4). 77 those issued to other plants that are under a different firm than the focal plant (Inkpen and

Tsang 2005, Mani and Muthulingam 2018). To make sure that our results are not driven by the warning letters issued to other plants within the same firm, we differentiated the warning letters into 1) those issued to other plants that belong to the same firm as the focal plant (i.e., OthWL_within), and 2) those issued to other plants that belong to a different firm from the focal plant (i.e., OthWL_outside), and repeated our analysis using difference-GMM models. The summary statistics and the estimation results are presented in Table 14 and 15 below, respectively.

Table 14. Summary statistics (within vs. outside the focal firm)

Variable count mean sd min max Viol 28380 0.152 0.36 0.00 1.00 PrevViol 28380 0.156 0.36 0.00 1.00 OthWL 28380 19.744 21.39 0.00 173.00 OthWL_within 28380 0.013 0.15 0.00 3.00 OthWL_outside 28380 19.731 21.38 0.00 173.00 OthWL_LC 28380 0.635 1.37 0.00 17.00 OthWL_LC_within 28380 0.001 0.04 0.00 1.00 OthWL_LC_outside 28380 0.634 1.37 0.00 17.00 OthWL_PF 23570 3.344 5.13 0.00 64.00 OthWL_PF_within 23570 0.008 0.12 0.00 3.00 OthWL_PF_outside 23570 3.336 5.11 0.00 64.00 OthWL_LC_PF 23570 0.143 0.55 0.00 13.00 OthWL_LC_PF_within 23570 0.001 0.03 0.00 1.00 OthWL_LC_PF_outside 23570 0.142 0.55 0.00 13.00 OwnWL 28380 0.005 0.07 0.00 1.00 OthViolVAI 28380 124.950 120.09 0.00 969.00 OthViolVAI_LC 28380 5.481 8.90 0.00 102.00 OthViolVAI_PF 23570 38.835 50.06 0.00 562.00 OthViolVAI_LC_PF 23570 1.862 3.97 0.00 64.00 Compliance 28380 0.140 0.35 0.00 1.00 Complaint 28380 0.023 0.15 0.00 1.00 Foreign 28380 0.149 0.36 0.00 1.00 InspFreq 28380 0.876 0.43 0.38 8.62 TimeSinceLast 28380 689.238 396.09 5.00 2583.00 † The numbers are based on original (non-logged) variables †† The plants for which the product profile code data is not available are dropped.

78 Table 15. Difference-GMM estimation results (within vs. outside the focal firm)

DV: Viol (binary) Model 1 Model 2 Model 3 Model 4

OthWL_within -0.37 (0.32) OthWL_outside -0.03 (0.05) OthWL_LC_within 3.24 (3.39) OthWL_LC_outside -0.39*** (0.09) OthWL_PF_within -0.24 (0.34) OthWL_PF_outside -0.02 (0.05) OthWL_LC_PF_within -3.20 (5.44) OthWL_LC_PF_outside -0.16 (0.22)

OwnWL -0.16 -0.81 -0.20 0.36 (0.16) (0.71) (0.16) (0.88) OthViolVAI 0.02 (0.01) OthViolVAI_LC 0.02 (0.01) OthViolVAI_PF 0.00 (0.02) OthViolVAI_LC_PF 0.01 (0.01) Compliance 0.03* 0.06** 0.02 0.00 (0.01) (0.02) (0.01) (0.02) Complaint -0.03 -0.00 -0.03 -0.01 (0.02) (0.03) (0.03) (0.04) TimeSinceLast 0.02 0.04*** 0.02 0.02+ (0.03) (0.01) (0.01) (0.01)

Violt-1 (lagged DV) 0.02 0.02 0.02 0.03 (0.02) (0.03) (0.02) (0.03)

Year dummies Yes Yes Yes Yes Lagged regressors Yes Yes Yes Yes Observations 18,050 18,050 15,150 15,150 FEI-CFR 10,280 10,280 8,410 8,410 2 휒 28.91* 39.53** 22.10+ 18.39+ AR(1) test (p-value) (0.00) (0.00) (0.00) (0.00) AR(2) test (p-value) (0.89) (0.19) (0.58) (0.87) Hansen overid. test (p- (0.64) (0.55) (0.90) (0.25) value)

+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001

79 While the coefficients of OthWL_X_outside remain consistent with those of OthWL_X in

Table 13 (where X ∈ {LC, PF, LC_PF}), supporting the robustness of our main results, the coefficients of OthWL_X_within are not significant. This result may be due to the fact that most of the firms in our sample are single-plant firms (86.3%, see Table 16 below) and, thus, OthWL_X_within did not have enough variance. This is also evidenced in

Table 14, which shows that the mean values of the OthWL_X_within variables are very small, compared to the OthWL_X_outside variables.

Table 16. Frequency of firms by number of plants that belong to the firm

Num. Plants Frequency of firms Percentage 1 779 86.3% 2 69 7.6% 3 22 2.4% 4 14 1.6% 5 1 0.1% 6 2 0.2% 7 5 0.6% 8 6 0.7% 9 2 0.2% 10 1 0.1% 12 2 0.2% Total 903

3.5. Discussion

This study examines if and under what conditions pharmaceutical manufacturing plants learn from the warning letters issued to other manufacturing plants in the industry and improve their manufacturing conformance quality performance. The pharmaceutical industry is one of the unique settings where the warning letters are deliberately made available to the public by the FDA and, thus, the vicarious learning implications of 80 warning letters can be studied. Exploring and understanding such learning is important, as the failure to meet quality and compliance standards in this industry can lead to severe public health and safety consequences.

Our results show that, despite the FDA’s effort as a centralized information diffusion mechanism (FDA 2012b) and the salient nature of warning letters, significant industry-wide compliance-improving vicarious learning from warning letters does not exist in the pharmaceutical industry. It may be that the FDA’s risk communication efforts, combined with the nature of warning letters, are not sufficient to overcome the learning hurdles associated with inherent heterogeneity in the pharmaceutical industry – more specifically, lack of search motivation and/or knowledge extractability, which are both necessary to achieve vicarious learning (Porac et al. 1989, Reed and DeFillippi

1989, Szulanski 1996, Madsen and Desai 2010). One related observation is that, while the warning letters specify the facts around the violations in detail and the directions for corrective actions provided by the FDA, they do not specify exactly how to address those failures (FDA 2020a). In fact, the FDA “allow[s] each manufacturer to decide individually how to best implement the necessary controls by using scientifically sound design, processing methods, and testing procedures” (FDA 2018). Thus, the knowledge associated with how to address the failures identified in the warning letters may be more like “know-how” than “know-what” and, thus, tacit (Polanyi 1966, Nonaka 1991). In addition, some important yet non-public information, including the violative product or equipment names, if any, is redacted from public-facing warning letters, which may exacerbate the tacitness and thus hamper learning. Our result also indicates that similarity in manufacturing process between focal and other plants does not appear to help 81 overcome the issues with respect to motivation and/or knowledge transfer. We do find, however, that the geographical proximity between the focal and other plants significantly promote vicarious learning from warning letters. The increased motivation and/or added layers of knowledge diffusion channels, enabled by geographical proximity combined with the FDA regulatory practices, may have contributed to this significant vicarious learning from warning letters.

3.5.1. Theoretical Contributions and Policy Implications

This study makes several contributions to the literature and to the policy makers. First, we contribute to the organizational learning literature in terms of setting the boundary conditions of the existing theory, by testing the notion of vicarious learning from failure in a qualitatively different industry context and by examining important moderating factors. In addition, our fine-grained data enables us to adopt a more granular unit of analysis – i.e., plant-inspection-CFR subpart – compared to those in much of the empirical studies in the learning literature. This precision allowed us to achieve a close match between learning cause and effect, providing more precise and realistic evidence of vicarious learning, something that the literature has not been able to provide (Baum and Dahlin 2007).

Second, while various firm-, plant-, inspection-, and investigator-related attributes have been studied and found to affect inspection outcomes in the literature (e.g., Macher et al. 2011, Anand et al. 2011, Gray and Massimino 2014, Ball et al. 2017), the learning implications of public warning letters on manufacturing conformance quality have not been studied until now. Thus, we contribute to the compliance inspection literature by 82 identifying and empirically examining warning letters as a novel factor that enables pharmaceutical manufacturing plants to vicariously learn from others’ failure events and thus to improve conformance quality performance.

Third, we examine which factors contribute more to vicarious learning and to reducing cGMP-related violations. We find that only the geographical proximity among plants matters in facilitating vicarious learning, which supports the notion of “localized knowledge” (Alcacer and Chung 2003) in our context, where the knowledge required to conform to regulations involves tacit components (Zollo and Winter 2002, Anand et al.

2012). Considering the FDA’s clear intention to diffuse the failure information to the industry members, our non-significant findings regarding the industry-wide learning and learning among the plants having similar manufacturing processes provide important policy implications to the FDA. More specifically, we propose that improved public communication policies and practices may facilitate vicarious learning from warning letters. The details of the improvement strategy are beyond the scope of this study.

Nevertheless, enhanced transparency in the warning letter contents – especially the information that is considered as “trade secret” or “confidential commercial information” and thus redacted per 5 U.S.C. § 552 (FDA 2020a) – may help promote understanding of causal relationships around others’ failure events and, thus, provide opportunities to vicariously learn from warning letters.

3.5.2. Limitations and Conclusion

This study has several limitations. First, like most other organizational learning literature, we do not observe pharmaceutical manufacturing plants’ search and learning behavior 83 directly, but instead operationally define learning as a reduction in a plant’s likelihood of violation. Second, the large-scale secondary data approach that we employed in our study does not allow us to examine the underlying mechanisms driving the significant (or non- significant) effects of vicarious learning from warning letters. For example, our findings do not offer insights as to specifically which mechanism(s) enabled vicarious learning from failure among the plants that are located in geographical proximity. Third, some of our variables are imperfectly measured. For instance, the product profile codes classified by the FDA are neither a direct nor precise measure of manufacturing process, and are measured at a single point in time. Also, geographical proximity and manufacturing process similarity between plants was measured in a binary manner. Fourth, because the product-plant linkage – that is, in which manufacturing plant each drug is manufactured – is not publicly available for most plants, we could not study the moderating effect of similarity between plants in terms of the drug products that they manufacture. Taking into account the manufactured product types would be an important future research topic, as it is possible that the focal plant may consider the other plants that manufacturing same (or similar) drug products as direct competitors and thus is more likely to allocate greater monitoring and learning efforts to them (Porac et al. 1989, Greve 1999). In the similar vein, although we controlled for time-invariant plant heterogeneity by including plant fixed effects in the model specification, we did not include any time-variant plant-level or firm-level characteristics as control variables in our model. It is possible that this may have caused some omitted variables bias in our estimation.

Despite the above limitations, this study provides novel perspectives on the vicarious learning from the warning letters in the pharmaceutical industry. Given the 84 significant public health and safety implications of manufacturing conformance quality, our study offers important insights for the policy makers who wish to facilitate compliance-enhancing vicarious learning via warning letters.

85 Chapter 4. Conclusion

There is a dearth of research that investigates the effect of some of the important pharmaceutical policies and their interplay with manufacturing strategy and organizational learning on quality and compliance performance. This dissertation provides useful policy and managerial implications to promote public health and safety.

Below is a brief summary of our findings, as well as policy and managerial suggestions, from each essay.

In Chapter 2, we find that there is no significant difference between generic drugs and their original branded counterparts in terms of serious recalls overall, but that generic drugs manufactured in non-advanced economies are less safe than their original counterparts that are made in similar locations. This finding is alarming as many of the generic drugs that are taken by Americans are in fact manufactured in those low-cost regions. We also find that the drug age, the length of time that the drug has been on the market, significantly mitigates the generic drug quality risk, relative to the branded counterparts. Given the inherent inability for the market to observe and assess drug quality, the FDA should improve the transparency of the drug information, including where the drug is made and when the drug was approved by the FDA. Managers of generic drug firms who wish to offshore manufacturing to emerging or developing countries for cost savings should consider that it may incur a greater cost for quality and compliance oversight. 86 In Chapter 3, we find that violation-reducing learning from the FDA warning letters does not take place industry-wide or among the plants that have similar manufacturing processes. We do find, however, evidence of significant vicarious learning from warning letters among the plants that are located in geographical proximity. This significant location effect makes sense, considering the FDA’s geography-based regulatory practices and local knowledge spillover mechanisms. However, given the

FDA’s intention to induce industry-wide learning by publicizing the warning letters, these results are somewhat disappointing. Improved public communication and dissemination policies may facilitate vicarious learning from warning letters. Managers of pharmaceutical manufacturing plants may also want to pay closer attention to the FDA warning letters issued to other plants regardless of their location, as those letters may provide valuable compliance-enhancing learning opportunities.

Although limitations were discussed in each essay, here we discuss broad limitations and the research directions that could partially address those. First, although we used novel, large-scale secondary data sources and conducted the analyses at very granular levels in both essays, the nature of this approach does not allow us to directly investigate the mechanisms that underlie the observed results. Augmenting our large- scale empirical analyses with primary and qualitative data – for example, structured case studies or field experiments – would be helpful in better understanding the mechanisms that lead to improved quality and compliance performance. Second, the samples in both essays are not completely random, but rather restricted by the availability of the data (i.e.,

SPL database in Chapter 2, and Citations database in Chapter 3). In the similar vein, some potentially important variables were not included in the statistical model, although 87 we control for time-invariant variables by including fixed effects whenever possible.

Thus, the possibilities of sample selection and omitted variables bias are not entirely ruled out in both essays. Third, while our dichotomous classification of drug manufacturing locations in Chapter 2 provides valuable insights, it may be worth exploring other ways to operationalize the location variable. For example, the World

Development Indicators (WDI) database provides quarterly development indices for each country worldwide on various dimensions, including agriculture and food security, economic growth, education, financial sector development, health, and social development, among others.77 It may be insightful to operationalize manufacturing location using such country-level continuous variables and examine which of those dimensions are causally associated with generic and/or brand-name drug quality risk and how. Fourth, as recall is an incomplete measure of drug quality risk, it may be helpful to check the robustness of the results in Chapter 2 using alternative measures of quality risk, such as drug adverse event reports from the Field Adverse Events Reporting System

(FAERS) database. Fifth, in Chapter 3, we did not completely control for the endogeneity associated with the investigators’ focus on specific regulations (i.e., CFRs) at the time of plant inspection, which may have masked or amplified the learning that we observed in our analysis. It may be necessary, and also interesting, to directly capture the investigator data – i.e., which investigator inspected which plant and when – and examine the investigator-induced vicarious learning, if any. Another possible approach to alleviate this concern would be to conduct the analysis at the higher level of unit of analysis (e.g.,

77 https://datacatalog.worldbank.org/dataset/world-development-indicators 88 plant-inspection level), although we would then lose the benefits of employing our granular unit of analysis. Finally, the industry context of this dissertation is pharmaceutical industry, and, thus, it may be worthwhile to investigate if our findings hold (or do not hold) in other industry contexts. Examining the vicarious learning implications of warning letters in food or medical device industries, where the product quality is relatively more visible to consumers compared to pharmaceutical industry, would also be fruitful. Limitations aside, the novel findings from the essays in this dissertation help better understand some of the important pharmaceutical quality and compliance drivers that have not been examined in the literature thus far.

This dissertation is a starting point in the research agenda that will include other important pharmaceutical policies whose quality and compliance implications have not been econometrically evaluated. For example, Generic Drug User Fee Amendment

(GDUFA) or Prescription Drug User Fee Amendment (PDUFA), which have shortened drug review cycles to approve more new drugs faster, should be important candidates to be empirically investigated from the drug quality and safety perspective. Also, it may be worth assessing how the Drug Supply Chain Security Act (DSCSA), which is intended to improve the transparency of how prescription drugs move through the pharmaceutical supply chain and has been implemented in phases since 2015, have affected pharmaceutical quality and compliance. The effect of these policies may also be contingent on various organizational and operational characteristics, including manufacturing strategy and learning, among others.

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111 Appendix: Online Appendix for Chapter 2

A. Descriptive statistics

A1. Matched sample

Table 17. Total number of drugs in the matched sample

# Drugs Percent Brand 698 24.14% Generic 2,194 75.86% Total 2,892 100%

Table 18. Number of drugs per each matched stratum (# matched strata = 576)

Mean Std. Dev. Min Max Brand 1.23 0.67 1 8 Generic 3.81 3.09 1 21 Total 5.04 3.21 2 23

112 Table 19. Number of drugs by panel start years (2009 ~ 2018)

Panel Start Year # Drugs 2009 426 2010 412 2011 335 2012 301 2013 207 2014 198 2015 188 2016 294 2017 331 2018 200 Total 2,892

A2. Recalls

Table 20. Number of recalls by class

# Drug-year # Recalls instances # Generics # Brand % Generic involved† Class 1 51 65 34 31 52.31 Class 2 316 674 453 221 67.21 Class 3 199 208 154 54 74.04 Total 566 970 641 306 66.08 † One recall may involve multiple drugs.

113 Table 21. Number of recalls (Class 1 and 2) by year

# Drug-year Year # Recalls instances # Generic # Brand % Generic involved† 2009 7 9 5 4 55.56 2010 39 41 27 14 65.85 2011 50 287 181 106 63.07 2012 24 24 20 4 83.33 2013 33 124 80 44 64.52 2014 54 61 34 27 55.74 2015 31 45 27 18 60.00 2016 27 30 22 8 73.33 2017 44 50 37 13 74.00 2018 58 68 54 14 79.41 Total 367 739 487 252 65.90 † One recall may involve multiple drugs.

A3. Cross-tabulations

A3-1. Product age

Table 22. Generic and product age – Sample mean and standard deviation

Mean Std. Dev. # Drugs Brand 26.77 14.83 698 Generic 9.60 8.35 2,194 Total 13.74 12.64 2,892

114 A3-2. Manufacturing plant location – Advanced vs. Non-advanced Economies

The list of advanced economies per IMF’s classification78 is as follows:

Australia, Austria, Belgium, Canada, Cyprus, Czech Republic, Denmark, Estonia,

Finland, France, Germany, Greece, Hong Kong SAR, Iceland, Ireland, Israel,

Italy, Japan, South Korea, Latvia, Lithuania, Luxembourg, Macao SAR, Malta,

Netherlands, New Zealand, Norway, Portugal, Puerto Rico (United States), San

Marino, Singapore, Slovak Republic, Slovenia, Spain, Sweden, Switzerland,

Taiwan Province of China, United Kingdom, and United States.

See Table 25 for all advanced economies with manufacturing locations in our data set; and Table 26 for all non-advanced economies with manufacturing locations in our data set.

Table 23. Generic and non-advanced economies – Number of drugs in the sample

Adv_Econ Non_Adv_Econ Total Brand 650 48 698 Generic 1,130 1,064 2,194 Total 1,780 1,112 2,892

Table 24. Generic and non-advanced economies – Number of drug-year instances involved in recalls (Class I and II)

Adv_Econ Non_Adv_Econ Total Brand 223 29 252 Generic 272 215 487 Total 495 244 739

78 https://www.imf.org/external/pubs/ft/weo/2019/01/weodata/groups.htm 115 Table 25. Generic and advanced economies (detailed) – Number of drugs

Brand Generic Total† United States 476 913 1,389 Canada 98 102 200 Germany 62 8 70 Italy 39 26 65 Ireland 3 37 40 Israel 34 3 37 France 28 4 32 United Kingdom 30 2 32 Switzerland 18 11 29 Belgium 18 0 18 Taiwan 1 16 17 Austria 4 12 16 Sweden 13 0 13 Japan 12 0 12 Australia 6 3 9 Norway 5 4 9 Portugal 0 8 8 Denmark 2 5 7 Spain 4 2 6 Singapore 0 5 5 Malta 4 1 5 Netherlands 1 4 5 Slovenia 3 0 3 Czech Republic 0 3 3 Finland 3 0 3 New Zealand 0 2 2 Total 864 1,171 2,035 † The table is ordered based on this column. The total number of instances is greater than the number of drugs manufactured in advanced economies (i.e., 1,780), because some drugs are manufactured in multiple manufacturing plants in different countries.

116 Table 26. Generic and non-advanced economies (detailed) – Number of drugs

Brand Generic Total† India 29 988 1,017 China 3 47 50 Mexico 11 0 11 Jordan 0 10 10 Romania 2 7 9 Hungary 4 3 7 Turkey 0 3 3 Brazil 0 2 2 Croatia 2 0 2 Bangladesh 0 1 1 Colombia 0 1 1 Oman 0 1 1 Saudi Arabia 0 1 1 Total 51 1,064 1,115 † The table is ordered based on this column. The total number of instances is greater than the number of drugs (i.e., 1,112), because some drugs are manufactured in multiple plants in different countries.

A3-3. Manufacturing mode – Internal manufacturing

Table 27. Generic and IM – Number of drug-year instances

IM CM Total Brand 346 352 698 Generic 1,749 445 2,194 Total 2,095 797 2,892

Table 28. Generic and IM – Number of drug-year instances involved in recalls (Class I and II)

IM CM Total Brand 138 114 252 Generic 356 131 487 Total 494 245 739

117 B. Robustness checks

In this section, we report the detailed analysis results of all robustness checks. Below table summarizes the estimation results of the relevant first-order and interaction terms, in terms of the sign and significance of the coefficient.

Table 29. Summary of the results

Fixed effect Sign†† (Significance†††) Test Model† Generic_D * Year Firm Stratum Generic_D Non_Adv_Econ_D Main Analysis in NB Y Y (+) ** Paper 1. Additional fixed NB Y Y Y (+) ** effects included 2. Logistic regression Y Y (+) ** LGT with fixed effects Y Y Y (+) ** NB Y Y (+) ** 3. All class recalls LGT Y Y (+) ** 4. Strata where brand NB Y Y (+) ** and generic are produced by different LGT Y Y (+) ** firms 5. Sales volume in the NB Y Y (+) ** previous year LGT Y Y (+) ** † NB: negative binomial panel regression (xtnbreg, DV is number of recalls); LGT: logistic panel regression (xtlogit, DV is recall yes or no) †† A positive sign indicates greater risk. ††† + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

118 B1. Stratum fixed effects, in addition to firm and year fixed effects

The use of CEM weights in a regression model is in effect equivalent to estimating the treatment effect within each stratum and then averaging those within-stratum treatment effects across all treated units, which leads to a single estimated average treatment effect on the treated (ATT) in the entire sample (King 2012); this is a common approach in the literature (e.g., Groves and Rogers 2011), and the one we follow in our main analysis.

Another approach is to explicitly model variation in the dependent variable within each stratum (e.g., Corritore et al. 2019) by including stratum fixed effects. Thus, we re-run our models additionally including stratum fixed effects in this robustness check; results remain consistent.

119 Table 30. Negative binomial panel regression with firm, year, and stratum fixed effects

Recall_D (Class I and II) Model 1 Model 2 Model 3

Num_Drugs_F 0.010 -0.038 -0.043 (0.253) (0.256) (0.256) Num_Drugs_P -0.138 -0.136 -0.137 (0.086) (0.088) (0.088) Num_DFs_P 0.083 0.072 0.035 (0.148) (0.149) (0.149) Num_Plants_D 0.038 0.035 0.136 (0.175) (0.176) (0.181) Prod_Age_D -0.160+ -0.103 -0.101 (0.087) (0.102) (0.102) Prev_Recall_D -0.335* -0.356* -0.384* (0.163) (0.163) (0.164) Non_Adv_Econ_D -0.075 -0.071 -1.189** (0.131) (0.131) (0.421) IM_D -0.080 -0.081 -0.091 (0.117) (0.116) (0.116) Volume_D 0.138** 0.136** 0.135** (0.017) (0.017) (0.017)

Generic_D 0.193 0.110 (0.201) (0.204)

Generic_D*Non_Adv_Econ_D 1.290** (0.443) Fixed effects Year Y Y Y Firm Y Y Y Stratum Y Y Y

N 17562 17562 17562 Wald-Chi2 1914.84*** 1908.44*** 1919.99*** Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

120 B2. Logistic panel regression

To verify if we find similar results when our DV is any recall, instead of the count, we coded Recall_D as 1 if the count of Class I and II recalls for a drug-year is greater than 0, and 0 otherwise. The previous recall experience (Prev_Recall_D) is also re-calculated based on the binary recall variable. We first re-run our main model with the 1-0 dependent variable; then we perform robustness tests with additionally including stratum fixed effects as the prior section.

B2-1. Logistic; firm and year fixed effects

We find that results remain consistent.

121 Table 31. Logistic panel regression with firm and year fixed effects

Recall_D (Class I and II; binary) Model 1 Model 2 Model 3

Num_Drugs_F -0.305 -0.304 -0.324 (0.296) (0.297) (0.297) Num_Drugs_P -0.021 -0.040 -0.045 (0.088) (0.090) (0.090) Num_DFs_P -0.151 -0.137 -0.157 (0.148) (0.149) (0.150) Num_Plants_D -0.064 -0.045 0.126 (0.208) (0.208) (0.214) Prod_Age_D -0.214** -0.200** -0.178* (0.076) (0.077) (0.078) Prev_Recall_D 0.431 0.433 0.404 (0.287) (0.287) (0.288) Non_Adv_Econ_D -0.173 -0.186 -1.716** (0.149) (0.149) (0.490) IM_D -0.206 -0.209+ -0.211+ (0.127) (0.126) (0.126) Volume_D 0.169** 0.169** 0.167** (0.016) (0.016) (0.016)

Generic_D 0.173 0.086 (0.192) (0.194)

Generic_D*Non_Adv_Econ_D 1.777** (0.513) Fixed effects Year Y Y Y Firm Y Y Y Stratum N N N

N 17562 17562 17562 Wald-Chi2 1035.29*** 1036.66*** 1035.51*** Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

122 B2-2. Logistic; firm, year, and stratum fixed effects

We find that results remain consistent.

Table 32. Logistic panel regression with firm, year, and stratum fixed effects

Recall_D (Class I and II; binary) Model 1 Model 2 Model 3

Num_Drugs_F -0.135 -0.181 -0.180 (0.300) (0.302) (0.302) Num_Drugs_P -0.115 -0.137 -0.136 (0.101) (0.102) (0.102) Num_DFs_P 0.029 0.046 -0.000 (0.169) (0.170) (0.171) Num_Plants_D -0.014 0.009 0.167 (0.222) (0.224) (0.230) Prod_Age_D -0.187+ -0.079 -0.076 (0.099) (0.117) (0.117) Prev_Recall_D -0.184 -0.201 -0.229 (0.259) (0.259) (0.260) Non_Adv_Econ_D -0.075 -0.092 -1.670** (0.155) (0.155) (0.513) IM_D -0.100 -0.098 -0.116 (0.138) (0.137) (0.137) Volume_D 0.156** 0.152** 0.151** (0.019) (0.019) (0.019)

Generic_D 0.346 0.239 (0.230) (0.233)

Generic_D*Non_Adv_Econ_D 1.810** (0.537) Fixed effects Year Y Y Y Firm Y Y Y Stratum Y Y Y

N 17562 17562 17562 Wald-Chi2 1352.70*** 1353.14*** 1357.37*** Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

123 B3. All class recalls as DV

To see if adding more minor, Class III, recalls from our analysis changes our results, we repeat our analyses using all recall classes, instead of just the severe (i.e., Class I and II) recalls, as an alternative dependent variable. Note that, in this analysis, previous recall experience (Prev_Recall_D) is re-calculated, reflecting Class III recalls. We find that results remain consistent.

Table 33. All recalls classes as DV; negative binomial panel regression with firm and year fixed effects

Recall_D (Class I, II, and III) Model 1 Model 2 Model 3

Generic_D 0.346 0.239 (0.230) (0.233)

Generic_D*Non_Adv_Econ_D 1.810** (0.537) Fixed effects Year Y Y Y Firm Y Y Y Stratum N N N

N 17562 17562 17562 Wald-Chi2 1400.18*** 1403.11*** 1414.17*** Standard errors in parentheses; CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

124 Table 34. All recalls classes as DV; logistic panel regression with firm and year fixed effects

Recall_D (Class I, II, and III; binary) Model 1 Model 2 Model 3

Num_Drugs_F -0.084 -0.083 -0.095 (0.264) (0.264) (0.264) Num_Drugs_P -0.069 -0.079 -0.081 (0.079) (0.081) (0.081) Num_DFs_P -0.028 -0.020 -0.038 (0.133) (0.134) (0.135) Num_Plants_D 0.072 0.084 0.189 (0.192) (0.192) (0.196) Prod_Age_D -0.225** -0.216** -0.201** (0.068) (0.069) (0.069) Prev_Recall_D 0.186 0.190 0.175 (0.236) (0.236) (0.237) Non_Adv_Econ_D -0.235+ -0.242+ -1.195** (0.135) (0.135) (0.386) IM_D -0.033 -0.034 -0.037 (0.116) (0.116) (0.115) Volume_D 0.160** 0.159** 0.158** (0.014) (0.014) (0.014)

Generic_D 0.105 0.050 (0.173) (0.175)

Generic_D*Non_Adv_Econ_D 1.117** (0.410) Fixed effects Year Y Y Y Firm Y Y Y Stratum N N N

N 17562 17562 17562 Wald-Chi2 1048.99*** 1050.95*** 1053.30*** Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

125 B4. Exclusion of strata where brand-name drug manufacturer also produces equivalent generic drugs

Among the 576 matched strata in our dataset, there are 40 strata where the brand-name drug manufacturer also produces equivalent generic drug(s). As an example of such strata, Pfizer markets a brand-name drug, Zithromax (NDA050733), and Hospira, a subsidiary of Pfizer, markets the equivalent generic version, Azithromycin

(ANDA065511). These two drugs are the equivalent antibacterial injection drugs. As it is not as clear the extent to which our knowledge- and incentive-based arguments that generic drugs are less safe than brand-name drugs apply to these 40 strata, we exclude these strata and repeat our analysis. We find that the results remain consistent.

126 Table 35. Without 40; negative binomial panel regression with firm and year fixed effects

Recall_D (Class I and II) Model 1 Model 2 Model 3

Num_Drugs_F -0.024 -0.057 -0.084 (0.257) (0.258) (0.259) Num_Drugs_P -0.092 -0.094 -0.098 (0.077) (0.079) (0.080) Num_DFs_P -0.110 -0.117 -0.131 (0.132) (0.133) (0.134) Num_Plants_D -0.071 -0.058 0.047 (0.165) (0.165) (0.169) Prod_Age_D -0.154* -0.152* -0.132+ (0.067) (0.069) (0.069) Prev_Recall_D 0.553** 0.543** 0.519** (0.174) (0.174) (0.175) Non_Adv_Econ_D -0.125 -0.129 -1.301** (0.127) (0.126) (0.450) IM_D -0.118 -0.117 -0.123 (0.109) (0.109) (0.108) Volume_D 0.152** 0.154** 0.151** (0.014) (0.014) (0.014)

Generic_D 0.061 -0.003 (0.193) (0.195)

Generic_D*Non_Adv_Econ_D 1.345** (0.469) Fixed effects Year Y Y Y Firm Y Y Y Stratum N N N

N 15798 15798 15798 Wald-Chi2 1434.58*** 1434.31*** 1446.81*** Standard errors in parentheses.; CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

127 Table 36. Without 40; logistic panel regression with firm and year fixed effects

Recall_D (Class I and II; binary) Model 1 Model 2 Model 3

Num_Drugs_F -0.146 -0.155 -0.177 (0.308) (0.308) (0.309) Num_Drugs_P -0.086 -0.093 -0.104 (0.094) (0.096) (0.097) Num_DFs_P -0.167 -0.165 -0.182 (0.158) (0.160) (0.161) Num_Plants_D -0.059 -0.047 0.140 (0.219) (0.219) (0.226) Prod_Age_D -0.233** -0.227** -0.199* (0.081) (0.083) (0.083) Prev_Recall_D 0.375 0.380 0.332 (0.303) (0.303) (0.304) Non_Adv_Econ_D -0.214 -0.218 -1.961** (0.158) (0.158) (0.540) IM_D -0.120 -0.121 -0.121 (0.135) (0.134) (0.135) Volume_D 0.180** 0.180** 0.178** (0.017) (0.017) (0.017)

Generic_D 0.074 -0.009 (0.234) (0.238)

Generic_D*Non_Adv_Econ_D 2.017** (0.565) Fixed effects Year Y Y Y Firm Y Y Y Stratum N N N

N 15798 15798 15798 Wald-Chi2 972.01*** 974.06*** 968.92*** Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

128 B5. Sales volume in the previous year

We repeated our analyses using the drug sales volume in the previous year, instead of the cumulative volume in the last three years. This is because labeled drug expiration dates are typically one year from the date of fill, although drug stability may be much longer than that (Lyon et al. 2006). Thus, drug recalls may be more directly associated with the drugs that are sold more recently (i.e., drugs sold in the previous year). We find that the results remain consistent.

129 Table 37. Sales volume in the previous year; negative binomial panel regression with firm and year fixed effects

Recall_D (Class I and II) Model 1 Model 2 Model 3

Num_Drugs_F -0.167 -0.192 -0.214 (0.251) (0.252) (0.252) Num_Drugs_P -0.050 -0.056 -0.058 (0.075) (0.076) (0.076) Num_DFs_P -0.108 -0.110 -0.127 (0.127) (0.128) (0.128) Num_Plants_D -0.075 -0.060 0.033 (0.160) (0.160) (0.163) Prod_Age_D -0.114+ -0.109+ -0.092 (0.063) (0.064) (0.065) Prev_Recall_D 0.514** 0.518** 0.502** (0.193) (0.190) (0.167) Non_Adv_Econ_D -0.108 -0.115 -1.185** (0.124) (0.123) (0.416) IM_D -0.182+ -0.182+ -0.189+ (0.104) (0.104) (0.103) Volume_D 0.164** 0.165** 0.163** (0.014) (0.014) (0.014)

Generic_D 0.089 0.028 (0.159) (0.161)

Generic_D*Non_Adv_Econ_D 1.235** (0.435) Fixed effects Year Y Y Y Firm Y Y Y Stratum N N N

N 17562 17562 17562 Wald-Chi2 1486.55*** 1493.17*** 1515.50*** Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

130 Table 38. Sales volume in the previous year; logistic panel regression with firm and year fixed effects

Recall_D (Class I and II; binary) Model 1 Model 2 Model 3

Num_Drugs_F -0.343 -0.345 -0.365 (0.298) (0.299) (0.299) Num_Drugs_P -0.027 -0.039 -0.046 (0.089) (0.091) (0.091) Num_DFs_P -0.162 -0.153 -0.173 (0.150) (0.151) (0.152) Num_Plants_D -0.062 -0.046 0.118 (0.209) (0.210) (0.215) Prod_Age_D -0.169* -0.159* -0.138+ (0.075) (0.076) (0.076) Prev_Recall_D 0.356 0.360 0.333 (0.289) (0.289) (0.291) Non_Adv_Econ_D -0.191 -0.200 -1.726** (0.150) (0.150) (0.492) IM_D -0.210+ -0.211+ -0.214+ (0.128) (0.127) (0.127) Volume_D 0.192** 0.192** 0.190** (0.016) (0.016) (0.016)

Generic_D 0.118 0.033 (0.194) (0.196)

Generic_D*Non_Adv_Econ_D 1.772** (0.516) Fixed effects Year Y Y Y Firm Y Y Y Stratum N N N

N 17562 17562 17562 Wald-Chi2 1025.33*** 1027.09*** 1025.54*** Standard errors in parentheses. CEM weights are included in all models. + p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 (two-tailed)

131