As of July 2021, I am an Associate Professor of Economics Analysis & Policy and Faculty Co-Director of the Robinson Life Science, Business, and Entrepreneurship program at the Haas School of the University of California, Berkeley. Please visit me there or my personal website www.matthewgrennan.com. I will no longer be updating this page.
Matthew Grennan’s research studies health care markets, products, and organizations using empirical and theoretical models from industrial organization economics. His recent work examines how complex incentives and imperfect information affect how health technologies are adopted, priced, and ultimately deliver value for society. Grennan’s research relates closely to his teaching in health care entrepreneurship, data analytics, and technology strategy. It also informs recent business and public policy debates regarding price transparency, relationships between physicians and industry, regulation of new products, and antitrust concerns about market power in the health care sector.
Grennan has received teaching awards from Wharton, Toronto (Rotman), and Poets & Quants. His research has been published in the top general interest journals in economics, management, and policy, including the American Economic Review, Management Science, and Health Affairs. Grennan’s research has been funded through leading institutions such as the National Science Foundation and National Institute for Health Care Management.
Matthew Grennan (Working), Using Machine Learning Methods to Predict Physician-Hospital Integration.
Abstract: In this document, we propose a new method that combines high-dimensional data with machine learning methods to predict physician-hospital integration. We compare the performance of this method with alter- native approaches used in the healthcare economics literature for a large validated sample, finding that it outperforms previous methods by a substantial margin. We also compare the static predictions of this model to a large validated sample recently made available by the Agency for Healthcare Research and Quality (AHRQ) and again document a high degree of accuracy. Finally, we briefly summarize the implications of our method for the growth in physician-hospital integration over the years 2008-2016.
Matthew Grennan and Mara Lederman (Under Review), Spillovers Across Product Categories in the Medical Device Industry.
Abstract: Theory offers many explanations for why multiproduct firms dominate a variety of industries, but empirically measuring these forces is complicated by the fact that the scale and scope of the firm are typically endogenous decisions. This paper exploits exogenous variation in firm “multiproductness” induced by differing regulatory regimes in the US and EU to empirically measure the size and sources of multiproduct firm advantages in a medical device market. We find that the average multiproduct device firm enjoys spillovers across its product portfolio that increase the value of a new product introduction by 20.1 percent relative to a new single product firm. The available data suggest that at least part of this effect can be explained by: (1) increased information transmission and (2) greater physical design complementarities among products by multiproduct firms. We discuss these findings in the context of recent questions surrounding innovation, barriers to entry, and conflicts of interest in medical devices.
Matthew Grennan and Ashley Swanson (2020), Transparency and Negotiated Prices: The Value of Information in Hospital-Supplier Bargaining, Journal of Political Economy, 128 (4).
Abstract: Using data on hospitals’ purchases across a large number of important product categories, we find that access to information on purchasing by peer hospitals leads to reductions in the prices hospitals negotiate for supplies. These effects are concentrated among hospitals previously paying relatively high prices for brands purchased in large volumes. Evidence from coronary stents suggests that transparency allows hospitals to resolve asymmetric information problems, but savings are limited in part by the stickiness of contracts in business-to-business settings. Savings are largest for physician preference items, where high-price, high-quantity hospital-brand combinations average 3.9% savings, versus 1.6% for commodities.
Matthew Grennan and Robert J. Town (2020), Regulating Innovation with Uncertain Quality: Information, Risk, and Access in Medical Devices, American Economic Review, 103 (110), pp. 120-161.
Abstract: We study the impact of regulating product entry and quality information requirements on an oligopoly equilibrium and consumer welfare. Requiring product testing can reduce consumer uncertainty, but it also increases fixed costs of entry and time to market. Using variation between EU and US medical device regulations, we document patterns consistent with valuable learning from more stringent US requirements. To derive welfare implications, we pair the data with a model of supply, demand, and testing regulation. US policy is indistinguishable from the policy that maximizes total surplus in our estimated model, while the EU could benefit from more pre-market testing. "Post-market surveillance'' could further increase surplus.
Matthew Grennan, Kyle Myers, Ashley Swanson, Aaron Chatterji (Under Review), No Free Lunch? Welfare Analysis of Firms Selling Through Expert Intermediaries.
Abstract: We study how firms target and influence expert intermediaries, and the welfare impact of banning those relationships. In the case study we investigate, manufacturers of statins, a class of cholesterol-lowering drugs, provide meals and other payments to physicians. Leveraging variation in exposure to spillovers from academic medical centers’ conflict-of-interest policies for identification, we estimate significant heterogeneity in the effects of payments on prescribing, with firms targeting highly responsive physicians. Payments offset the negative effects of oligopoly pricing and other frictions on utilization, but at great expense to consumers and insurers because payments promote high-price branded drugs. To understand the net effects of payments in the presence of various factors that may drive a wedge between physicians’ decisions and patients’ best interests, we introduce a decision error into our framework and explore the assumptions under which payments benefit consumers. We calibrate this decision error using clinical trial results on statin effectiveness for a similar population. This exercise suggests that, in the case of statins, firm payments to physicians benefit consumers due to significant underprescribing at baseline.
Stuart Craig, Matthew Grennan, Ashley Swanson (2017), Mergers and Marginal Costs: New Evidence on Hospital Buyer Power, RAND Journal of Economics, accepted.
Abstract: We estimate the effects of hospital mergers, using detailed data containing medical supply transactions (representing 23 percent of operating costs) from a sample of US hospitals 2009-2015. Pre-merger price variation across hospitals (Gini coefficient 7 percent) suggests significant opportunities for cost decreases. However, we observe limited evidence of actual savings. In this retrospective sample, targets realized 1.9 percent savings; acquirers realize no significant savings. Examining treatment effect heterogeneity to shed light on theories of “buyer power,” we find that savings, when they occur, tend to be local, and potential benefits of savings may be offset by managerial costs of merging.
Matthew Grennan and Ashley Swanson (Working), Diagnosing Price Dispersion: Demand, Bargaining, and Search in Hospital-Supplier Contracting.
Abstract: In a wide range of product markets in which prices are negotiated, price dispersion across buyers for similar (and even identical) products can be driven by heterogeneity in brand preferences, search/contracting costs, and bargaining abilities. We develop a model that allows for each of these mechanisms and estimate it using data on purchases of medical devices/supplies in a broad variety of product categories purchased by over 20 percent of U.S. hospitals 2009-15. While nearly all categories exhibit substantial price dispersion, the drivers vary. Among physician preference items, brand preferences are important drivers of price heterogeneity; among more commodity-like products, bargaining heterogeneity plays a dominant role. Search/contracting costs keep choice sets small, affecting welfare via access to more and better suppliers, and also slightly increasing negotiated markups. We relate these results to expected heterogeneity in the welfare impacts of the growth of conglomerate device suppliers and the entry of information into the marketplace that could facilitate search.
Matthew Grennan (2014), Bargaining Ability and Competitive Advantage: Empirical Evidence from Medical Devices, Management Science, 60, pp. 3011-3025.
Abstract: In markets where buyers and suppliers negotiate, supplier costs, buyer willingness to pay, and competition determine only a range of potential prices, leaving the final price dependent on other factors (e.g., negotiating skill), which I call bargaining ability. I use a model of buyer demand and buyer–supplier bargaining, combined with detailed data on prices and quantities at the buyer–supplier relationship level, to estimate firm bargaining abilities in the context of the coronary stent industry where different hospitals (buyers) pay different prices for the exact same product from the same supplier. I estimate that (1) variation in bargaining abilities explains 79% of this price variation, (2) bargaining ability has a large firm-specific component, and (3) changes in the distribution of bargaining abilities over time suggest learning as an important channel influencing bargaining ability.
Matthew Grennan (2013), Price Discrimination and Bargaining: Empirical Evidence from Medical Devices, American Economic Review, 103.
Abstract: Many important issues in business-to-business markets involve price discrimination and negotiated prices, situations where theoretical predictions are ambiguous. This paper uses new panel data on buyer- supplier transfers and a structural model to empirically analyze bargaining and price discrimination in a medical device market. While many phenomena that restrict different prices to different buyers are suggested as ways to decrease hospital costs (e.g., mergers, group purchasing organizations, and transparency), I find that: (i) more uniform pricing works against hospitals by softening competition; and (ii) results depend ultimately on a previously unexplored bargaining effect.
In healthcare or anywhere else across science, or business, or sports, the importance of data and analytics is virtually unquestioned. That, however, does not mean that it needs no elucidation. In this course, we begin with a fundamental understanding of the state of data and analytics in healthcare and then move onto examples of its use in converting from business questions to implemented solutions. We "sidestep" into the world of algorithms/machine-learning/AI and causal inference, but our focus is on business applications of these tools to the available data in the healthcare industry. As we discuss examples, we always seek to show how human creativity needs to be at the heart of the questions being probed. We highlight today's data universe in healthcare, the level of integration we have achieved, and the immensity of the remaining task, all with an eye to the business opportunities that exist now. We end with a showcasing of the art of the possible - in 2020 - and with (hopefully) a clear look ahead at what remains to be achieved. At the end of this course, students will: 1. Know the health care data landscape; 2. Understand the "loop" that drives modern evidence-based businesses; 3. Dive into real health care data analytics problems, developing a first-hand familiarity with basic tools and concepts; 4. Anticipate the business opportunities evolving in health care data and analytics. Other experience in data science can serve as a substitute for the prerequisites. Knowledge of basic statistics is a must. Coding experience is a plus. Experience coding to solve data/statistics problems is ideal. Further training in data science is a plus, and we welcome those with more advanced preparation.
Delivering basic health care advances worldwide and continuing to increase lifespan and quality (in an affordable manner) represent some of the major societal challenges of our time. Addressing these challenges will require innovation in both medical technology and the ways in which health services are delivered. Through readings, cases, guest lectures, and your own entrepreneurial work outside of class, we will examine the environment facing prospective health care entrepreneurs: (1) sources of health care innovation; (2) the many "customers" in health care: patients, doctors, hospitals, insurers, and regulators; (3) the powerful established firms with developed clinical and sales expertise; (4) the investing community. Along the way we will develop a framework for thinking about what is different (and what is not) about the challenges of health care entrepreneurship.
Successful medical devices are an amalgamation of creative and innovative thinking, clinical expertise, and engineering know-how that endures intense regulatory and reimbursement scrutiny. This course will provide a foundation for understanding the nuances of the medical device industry. It will cover topics ranging from device design and discovery, regulatory issues, marketing, reimbursement, management, and strategy. Classroom activities will be supplemented with optional tours of hospitals, research and manufacturing facilities, and hands-on demonstrations of devices. Though the course is intended primarily for MBA students, it will be open to medical and engineering students as well as to hospital house staff.
In healthcare or anywhere else across science, or business, or sports, the importance of data and analytics is virtually unquestioned. That, however, does not mean that it needs no elucidation. In this course, we begin with a fundamental understanding of the state of data and analytics in healthcare and then move onto examples of its use in converting from business questions to implemented solutions. We "sidestep" into the world of algorithms/machine-learning/AI and causal inference, but our focus is on business applications of these tools to the available data in the healthcare industry. As we discuss examples, we always seek to show how human creativity needs to be at the heart of the questions being probed. We highlight today's data universe in healthcare, the level of integration we have achieved, and the immensity of the remaining task, all with an eye to the business opportunities that exist now. We end with a showcasing of the art of the possible - in 2020 - and with (hopefully) a clear look ahead at what remains to be achieved. At the end of this course, students will: 1. Know the health care data landscape; 2. Understand the "loop" that drives modern evidence-based businesses; 3. Dive into real health care data analytics problems, developing a first-hand familiarity with basic tools and concepts; 4. Anticipate the business opportunities evolving in health care dataand analytics. Experience in data science if prerequisite not met. Other experience in data science can serve as a substitute for the prerequisites. Knowledge of basic statistics is a must. Coding experience is a plus. Experience coding to solve data/statistics problems is ideal. Further training in data science is a plus, and we welcome those with more advanced preparation.
Arranged with members of the Faculty of the Health Care Systems Department. For further information contact the Department office, Room 204, Colonial Penn Center, 3641 Locust Walk, 898-6861.
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