ENTRY DECISIONS IN THE GENERIC
`PHARMACEUTICAL INDUSTRY
`
`Fiona M. Scott Morton
`
`Working Paper 6190
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`NBER WORKINGPAPER SERIES
`
`ENTRY DECISIONS IN THE GENERIC
`PHARMACEUTICAL INDUSTRY
`
`Fiona M. Scott Morton
`
`Working Paper 6190
`http://www.nber.org/papers/w6 190
`
`NATIONAL BUREAU OF ECONOMIC RESEARCH
`1050 Massachusetts Avenue
`Cambridge, MA 02138
`September 1997
`
`Thanks are due to Peter Reiss, John Roberts, Garth Saloner, Andrea Shepard, Dimitri Vayanos,
`Matthew White, seminar participants at UBC, Northwestern, University of Chicago, University of
`Michigan,and the NBER 1.0. winter conference. I am also grateful to Merck and Co.for allowing
`me to gather data from their library. All errors are the responsibility of the author. This paperis part
`of NBER’s research program in Industrial Organization. Any opinions expressed are those of the
`author and notthose of the National Bureau of Economic Research.
`
`© 1997 by Fiona M.Scott Morton. All rights reserved. Short sectionsof text, not to exceed two
`paragraphs, may be quoted withoutexplicit permission providedthatfull credit, including © notice,
`is given to the source.
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`Entry Decisions in the Generic Pharmaceutical
`Industry
`Fiona M. Scott Morton
`NBER Working Paper No. 6190
`September 1997
`JEL Nos. L65, L20, L21
`Industrial Organization
`
`ABSTRACT
`
`In this paper I use dataon all generic drug approvals granted from 1984-1994 to examine
`
`whether heterogeneity among potential generic entrants can be used to predict which firmswill
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`chooseto enter a particular market. The findings suggest that a firm’s portfolio characteristics,
`
`namely, its previous experience with a drug or therapy reduces the cost of preparing an ANDA and
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`increasesthe probability of entry. A subsidiary’s parent's experienceis not generally significant in
`
`predicting entry of the subsidiary. Firmsalso prefer entering markets that are similar, in terms of
`
`revenue and sales to hospitals, to markets already in their portfolios. On both scientific and
`
`marketing dimensions, the evidence showsthatfirms are specializing.
`
`I explore severaldifferent
`
`ways of constructing the set of potential entrants and find that the results are not affected by
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`methodologicalvariation. Standard IO theory suggests that profits per entrant will decline in the
`
`numberof entrants. Previousresearch has found that generic prices depend on the numberofgeneric
`
`entrants, and the results presented here show that the total numberof entrants increases with the size
`
`of the market (revenue). These findings imply that generic firms face a negative competition
`
`externality which makestheir expectations about who else might be planning to enter any given
`
`market important in the entry decision. The limited evidence on entrant beliefs supports this
`
`conjecture as do several features of a regulatory upheaval when firms began entering different
`
`markets than they had in the past.
`
`Fiona M. Scott Morton
`Graduate School of Business
`University of Chicago
`1101 East 58th Street
`Chicago, IL 60637
`and NBER
`fionasm@gsb.uchicago.edu
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`I. Introduction
`
`A firm’s decision to enter a particular market is one of the most important economicactionsin a
`
`market economy. The numberoffirmsin a market and distribution of market share have long been
`knownto affect price levels and consumer welfare. Most researchin this area uses the convenient
`
`assumption of symmetric firms, although this is of course not a good representation ofreality. In
`
`contrast, this paper takes explicit accountof heterogeneity among potential entrants to predict which
`firmsare likely to enter which markets. In particular, it examines the entry choices of heterogeneous
`generic pharmaceuticalfirms and findsthat they specialize along both scientific and marketing
`dimensions. Thehistory and experience ofa firm that lead it to enter particular markets can be thought of
`as firm ‘capabilities,’ in the sense that word is used in the business press. This industry providesa setting
`wherea firm’s capabilities can be explicitly measured and the result of using existing capabilities or
`
`developing new ones can be observed.
`
`The entry decision is complex because the numberof firms in a marketaffects the payoff to any
`one of them from entering that market; each entrant creates a negative externality for the others that can
`
`be severe. Profits earned by an entrantfirm therefore depend on entry decisions of other firms. Entrants
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`sink entry costs simultaneously because firms do not typically announcetheir entry plans and the FDA
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`does not reveal whose application it has received. The timing of the game, combined with research
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`showing generic prices (and presumably profits) depend on the numberofgeneric entrants, implies that
`generic firms face the difficult problem of how to form expectations about where others will enter. Those
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`expectations will affect its own entry decisions.
`
`The question of which firms are expected to enter -- as well as do enter -- which markets in a
`
`simultaneous gameis important. For a generic pharmaceutical manager making entry decisions for his or
`her firm,it is clearly a crucial problem. I discuss and examine how a generic pharmaceutical firm might
`form expectationsofrivals’ actions and what firm equilibrium strategies might be. I argue that repeat
`players may usean entry strategy that provides stability of expectations: specialization. Specialization
`
`based on both scientific and marketing characteristics is natural becauseit reflects lower costs and
`
`provides a well-understood way to form conjectures about where competitors will enter.
`
`It is possible to conduct an empirical study of firm decision-making in the generic
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`pharmaceutical industry because entry regulations create relatively good experiments and the regulatory
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`agency, the Food and Drug Administration (FDA), generates data that are available to researchers. In this
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`paperI use data on all generic drug entries from 1984 to 1994 to examineentry patterns and
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`specialization.In particular, I explore whether generic entrants are choosing markets based on past
`experience as measured by characteristics of their portfolios. I find that a firm’s previous experience with
`a drug or therapyincreases the probability of entry into a similar market. The experience ofa firm’s
`parent on various dimensionsis generally not helpful in predicting entry, above and beyondthe firm’s
`(subsidiary) experience. Marketing similarities between the entry opportunity and characteristics of the
`
`firm’s portfolio such as market revenue and hospital share are also important in explaining entry.
`Additionally, I show that larger markets, those that attract more entry, are markets with more sales to
`
`hospitals and those where the drug treats a chronic condition.
`
`In 1989 a major scandal erupted whenvariousillegal practices were uncovered in the generic
`drug industry. I present results showing that the subsequent regulatory upheaval re-weighted the
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`components of entry cost and disrupted established industry practices, including the pattern of
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`specialization. Firms began to enter markets that looked different, rather than similar, to markets they
`
`were alreadyin.
`
`II. Institutional Framework and Timing
`
`A firm that invents a new drug must get approval from the FDA by showingthe drugis safe and
`effective. A New Drug Application (NDA)reports tests showing safety andefficacy andis typically
`
`expensive to construct and takes many years to be approved. A firm taking this routeis called an
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`innovator and the product is typically promoted undera proprietary brand name. In 1984 the
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`pharmaceutical regulatory regime wassignificantly altered by the Waxman-Hatch Act. Thislegislation,
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`amongother things, allowed generic firms to submit Abbreviated New Drug Applications (ANDAs)for
`drugs approved since 1962. A flood of new ANDAswasfiled in responseto the law. The advantagesof
`the ANDAprocess are summarizedin the quotation below.
`
`“The benefit of the ANDAprocess to generic manufacturersis that it does not require these
`companiesto repeatcostly clinical and animalresearch on active ingredients or finished dosage
`forms already found to be safe and effective. A generic drug must contain the sameactive
`ingredients; be identical in strength, dosage form, and route; be bioequivalent; and be
`manufactured underthe samestrict standards as the brand-name drug to gain FDA approval.”
`(Frost and Sullivan report (1994))
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`Somefirms submit both NDAs and ANDAs,but the vast majority specialize in one or the other.
`The other main type offirm in the industry is a “generic firm”thatsells generic products. Figuring out
`how to manufacture the drug and performing the bioequivalency studies required for the ANDA can take
`from several months to a couple of years depending on the formulation of the drug, the effort and skill of
`the firm, andthe availability of good suppliers. The application process requires factory inspections
`from the FDA and independentlaboratory tests of several preliminary batches of the product. A firm
`must therefore be ready to make the product -- new equipment must be purchased and operational, for
`
`example -- months before the firm is legally permitted to beginsellingit.
`
`The time between submission of the ANDA andgranting of approval from the FDA averaged
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`about seventeen months over the 1984-94 period, although annual averages have varied greatly overthe
`
`last decade (Scott Morton (1996)). Therefore, a firm must start applying for permission to manufacture a
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`specific drug two to three years before its patent expires if the entrant wishes to be active in the market
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`immediately following patent expiration. Historically, many firms have not applied in a timely fashion
`
`despite the well-knownfact that a firm that is first into a market and the only generic for even a few
`
`monthsearnslarge profits. A useful rule of thumb is that generic markets for most drugsdo notlast very
`long compared to the productlife of a patented and branded drug. Bythetimeall the patents on a drug
`
`have expired,it is relatively old technology and superior therapeutic substitutes are very likely to have
`
`been invented. Demandfor the drug in patient days and revenue beginsto fall after patent expiration;
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`not many drugs remain major markets five years after patent expiration. (See Caveset al. (1991) and
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`Stern (1995).) Thus, we should not see profit-maximizing generic firms wasting any time in entering a
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`market -- once the relevant patents expire -- and the model will not build in an option for delay.!
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`The FDA doesnot reveal what applications it has received from which firms; no one can receive
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`information on pending applications from the FDA. However, firms may announcetheir own intentions
`
`or actions. An (admittedly incomplete) examination of annual reports and interviews suggests that
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`announcingis rare. Industry participants claim they do not wantto reveal that they think a particular
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`marketis a good one to enter. Since all firms have accessto essentially the same information,this claim
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`does not seem to be very convincing. However, another firm’s opinion could function as an additional
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`signal of value for what could be an unknown, but common,value. Additionally, although a firm might
`
`announceit has applied fora particular drug, there is no guarantee approval will be grantedin the time
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`frame anticipated by the applicant. The announcementis notcredible. The firm could still be missing
`
`sometests or have a sloppy application. At the last minute, the FDA could decideatrial did not meet
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`1 Bolton and Farrell (1990) model an option for delay in their entry game.
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`FDAstandards and mustbe redone;or the factory mightfail a late inspection. A year’s delay (which
`could easily happen due to regulatory uncertainty) can cause an application that was submitted and
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`announcedto be approved after that of a competitor responding to the announcement. A firm therefore
`
`cannot precommitto a market with an application announcement. Theresult of this lack of announcingis
`that generic firms are effectively sinking entry costs simultaneously.
`
`The pharmaceuticalindustry is characterized by high fixed costs (invention) and low marginal
`
`costs (production). Although an ANDAandthe research involved are muchless costly than basic
`
`research and NDAs,entry cost is nevertheless significant for a generic drug project wherethereis likely
`
`to be vigorous price competition. The average size of brand markets in the samplethatlater attract one
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`generic entrantis $22 million. Generic products usually capture about half of molecule volume,although
`
`at prices 30-50% lowerthan the brand price. Thus per firm generic revenuesare likely to be less than $10
`
`million per year, perhaps as low as $5 million for a product (perhaps with multiple concentrations). In
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`interviewsI have been told that filing an ANDA costs one quarter of a million dollars, twenty million
`
`dollars and various figures in between. This order of magnitude matches a one-entrant marketsize,
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`although ANDAcosts clearly must vary across drugs and firms. Generic drug industry participants
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`exhibit great concern overexcessentry, falling prices, and the effect on profits. Stories of unexpected
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`failures due to the appearanceof extra entrants are common. If fixed and sunk costs were zero, marginal
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`cost pricing would provide an acceptable rate of return, no one would be willing to charge less than
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`marginal cost, and manufacturers would not worry about making a drug on which they are losing money
`
`overall.
`
`I therefore make the assumptionthat fixed sunk costs are an important componentin the
`
`project’s budget.
`
`The FDA imposesno requirement to produce once an ANDAis granted. Thus an unused but
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`approved ANDAis an option, available to be usedif prices rise, rivals exit, or the market becomes more
`
`attractive for any reason. Exit -- formal withdrawal of the ANDA-- is not anattractive alternative unless
`
`price is expected to be below marginalcost for the life of the drug (where marginal cost includes the
`
`opportunity cost of using equipment to make other drugs). There are several categories of ANDA
`
`withdrawal: the firm may discontinue the product, the product can be withdrawn by mutual consent, the
`
`firm might violate an FDA standard so that the FDA withdraws the ANDA,orthe product may be found
`
`not to be efficacious (all NDA and ANDAsforthat product are withdrawn). The last two (and maybe
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`three) reasons are cases where the FDAis forcing the firm to exit, rather than situations where the firm
`
`choosesto leave the market because it is unsatisfactory. An ANDAcanbesold ortransferred easily with
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`the physical productionsite, or if the original manufacturer becomes a contract manufacturer for the new
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`owner. Buying an ANDAwithoutits factory means newtests and inspections mustbe carried out to the
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`FDA’s satisfaction; this is not necessarily faster or cheaper than starting from scratch. Thusentry by a
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`particular firm is a fairly irreversible decision; the costs that can be recovered upon exit from one drug
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`(and not a factory) are close to zero.
`
`In 1989 what became knownasthe “generic scandal” broke out. Investigations by the US
`
`Attorney’s office in Baltimore began in 1988, and by the end of 1989 had uncovered several cases of
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`bribery in the generic drug approval process. Four reviewers at the FDA were found to have been taking
`
`bribes in return for speeding approvalofthe bribing firms’ ANDAs.* Additionally, some firms were
`
`found to have submitted the original branded productas their own in tests designed to compare a
`
`potential generic to the brand. The “generic” drug would passthe test with flying colors.3 In one case a
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`generic firm wasactually selling the recoated branded product.4 Laterin the investigation, violations of
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`manufacturing rules were also uncovered. The fallout from these discoveries greatly affected approvals
`
`and manufacturing in the following several years. The FDA’s Generic Drug Division fired many
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`reviewers and the remaining reviewers proceeded very cautiously and slowly. New reviewers were
`
`hired, ethics standards promulgated, and the division was reorganized. ANDAsgranted from 1989-1993
`
`took years longer than usual to be approved.
`
`Additionally, although this was not a concern of the FDA investigation, it became knownthat
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`industry participants had been bribing employeesofrival firms for information on approvalactivity.
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`Manyfirms were engaging in gossip and inquiry into the entry plansof rival generic firms. The people
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`in the industry included in my informalpoll claim muchof this activity cameto a halt at the time of the
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`FDAscandal and investigation because managers were worried that their behavior would be interpreted
`
`as unethicalor illegal. This left firms with much less information on the entry plans of competitors.
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`The discovery ofirregularities in the approval process led to a review of what are known as
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`“good manufacturing practices.” Many more, andstricter, inspections of manufacturing facilities were
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`carried out than had gone on previously. Manyfirms were forced to withdraw products from the market
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`for a period of months-- years, in somecases -- until their manufacturing satisfied the FDA. During the
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`time the products were off the market the firms were upgrading their manufacturing process, suppliers,
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`or proceduresandtraining. The regulatory changes took place over several years and involved political
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`actors from Congress, Health and HumanServices, the FDA, and law enforcement. The criminal
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`2 The investigation was launched because Mylan becamesuspiciousthat its drugs were not getting approval as
`quickly as competitors and found incriminating evidence in the garbage can of their FDA reviewer. (Chicago
`Tribune, Nov. 22, 1992)
`3 The independentlabs doing the testing noticed when the coating chipped off, the brand logo wasvisible!
`4 This hardly seemslike a profit-maximizing strategy.
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`investigation wasstill going strong in 1992, although most convictions had been secured by then.>
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`Overall, the period from 1988 to at least 1992 was a time of great upheaval and uncertainty in the generic
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`drug industry.
`
`III. Model
`
`The entry gameandthe role of competition and fixed costs has been addressed by a numberof
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`researchers (Bresnahan and Reiss (1988, 1991b), for example). These models predict the total number of
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`entrants in a market, partly because this piece of information is of great interest to 1.0. economists and
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`policy makers. However, the issue of exactly which potential entrant ends up entering is undetermined in
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`many gametheoretic models of entry. Typically, entrants are modeled as drawn from a limitless pool of
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`identical potential entrants, probably an unrealistic assumption for most markets. Often it is difficult to
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`measure and model heterogeneity among potential entrants; additionally, heterogeneity has a strong
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`impact on form of the entry game. Berry (1992) addresses the question of which entrants enter which
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`markets, as well as how manyentrants enter a market, in his study of the airline industry. However, the
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`generic pharmaceutical industry is characterized by simultaneous entry, rather than sequential, as in the
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`Berry model. The following simple modelillustrates its main features.
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`The gamehas two periods. In the first period firms choose whetheror not to enter specific
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`markets by sinking their fixed costs. In the second period production and sales determine net profits.
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`Profits will depend on the numberofentrants in two ways. First, equilibrium price will decline in the
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`numberofentrants. (Price will hereafter mean generic price.) Despite the fact that the product is very
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`homogeneousandthe industry lookslike it should exhibit Bertrand pricing, several studies have
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`documented that generic pricesfall steadily with the numberof generic firms (Caveset al. (1991), Frank
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`and Salkever (1995), Wiggins and Maness(1995)).
`
`Secondly, an increasing numberof firms means smaller quantity sold for each,all else equal.I
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`assume a molecule’s marketsize is fixed because productsare relatively old by the time their patents
`
`expire; the brand has expanded the market as muchas it can. The drop in price due to the introduction of
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`generics does not increase total quantity sold of the molecule, probably dueto the price-insensitivity of
`doctors (see CWH (1991)).” However, lower generic prices will increase generic quantity at the expense
`
`of brand quantity. If total generic quantity sold is Q, and 7 is the numberof generic firms, I assume Q/n
`
`falls with n, despite the expansion in Q due to lower prices. The important feature of the modelis that
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`5 Chicago Tribune, Nov. 22, 1992 p 13A
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`profits decline with an additional entrant. This will hold, and the coordination problem will be
`
`particularly important, when both price and quantity sold per firm decline with additional entry. The
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`expression below representstotal profits for firm i earned in marketj.
`
`Ty = (pit) — ca)qu(n) — Fa
`
`whereprice (p) and quantity (q) depend on the numberoffirms (n) in the manner described. cjj and Fi
`denote the marginal cost and fixed entry cost respectively that are specific to firm i and marketj.
`
`Theidea hereis that there is heterogeneity across firms and it shows upin firm costs. The model
`
`will produce the sameentry rule whetherfirms differ in marginal cost, fixed cost, or both.” For the
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`purposesofdiscussion, for the rest of the paper I will assumethat it is the fixed cost of entry that differs
`
`across firms and markets, while marginal costs are constantacrossfirms.’ Firms differ in the skills of
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`their research group, suppliers, equipment, etc. which will affect ANDAcost. Drug markets also vary in
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`howdifficult a drug is to formulate and test, and therefore how muchanentrant will spend to enter. The
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`basic implications of this sort of modelare that in larger markets more firms will find it profitable to
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`enter, each additional entrant will lower the net profit of all other entrants, and lowercost firms should
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`enter where higher cost firms should not. The analysis in the paper will test these implications.
`
`In equilibrium firms choose markets to enter based on differences in past experience. Each firm
`
`has a fixed cost F; that is observable and common knowledge. Strategies are enter if F;<F* and stay outif
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`F>F*, F* is defined such that firms with costs higher in the distribution than F* earn negative profit
`
`whenall firms with F,<F* enter. No mistakes occur under this scheme because each firm knows whereit
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`lies in the realized distribution of fixed costs for the market and will not enter if there is not “room” for
`
`it. The postulated strategies form a Nash equilibrium because the low cost entrants will earn non-
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`negative profit when the high cost firms stay out and therefore prefer to enter, while the high cost firms
`
`© The elasticity of demandis clearly not zero atall price levels. However, in the observedprice range,elasticity of
`demand for a molecule is very low.
`? Later in the paper, I use drug experiencevariables to proxy for fixed cost differences across firms. Experience
`variables will also pick up any experience-based differences in marginal cost. Other marginalcost differences come
`from the batch production nature of pharmaceutical manufacturing. The marginal cost of a batch (rather than a pill)
`includes the opportunity cost of capital. A firm with strong demand for more drugs than it can produce at one time
`has an attractive outside option, production of one ofits other drugs, for any given piece of capital. Since the quality
`of the outside option will vary across firms and over time, the marginal cost of a batch will also vary. The problem
`of opportunity cost of a batch is an important factor in the firm’s decision to enter a market, and it is impossible to
`measure without detailed plant-level data. Finally, there will be some marginal cost differences are due to
`characteristics of the production process that I cannot measure or control for. These final two sources of marginal
`cost differences are unaccountedfor in this study.
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`will only lose moneyif they enter and the low cost firmsalso enter, so they prefer to stay out. A firm’s
`fixed cost level will help predict entry in this game,as will the cutoff level for the market, F*.
`All firms can work outthe cutoff point in the fixed cost distribution. The firm enters if its own
`
`fixed cost falls below that cutoff level.
`
`pr(entry 4) = pr( Fi < Fj*)
`
`where Fj* representsthe cutoff fixed cost level for marketj.
`The model mostclosely related to this problem is that of Dixit and Shapiro (1986). They
`describe a case of N identical potential entrants vying for spots in a market that “holds” only M firms,
`and build a model based on mixedstrategies.? There is a probability of entry from which each firm can
`calculate its expected profits from entering or not (using a binomialprobability density). The equilibrium
`probability of entry is derived from the indifference of a firm to choosing enteror not enter.!9 Dixit and
`Shapiro point out that choosing a pure strategy equilibrium in which onlyMfirmsplay “enter”is
`artificial unless the reason for choosing those M firmsis incorporated into the game. The modelandthe
`empirics below do incorporate reasonsfor particular firms to enter, allowing the firmsto do better than
`just flipping a coin, and allowing the researcher (and presumably competitors) to predict entry, although
`not perfectly.
`
`Firmsbear considerable risk due to simultaneousentry in the standard model of competition
`described above.“Over-entry”results in a lower than expected market price, volume, andprofits, and the
`ex post desire not to have entered the marketin the first place. Price might stay above marginal cost, and
`yet belowalevel that yields zero profit on the project, without firms havingthe incentiveto exit the
`industry. “Under-entry” of course, produces economicprofits until additional firms are approved to make
`the drug (which normally would occur within two to three years). At any moment, each firm is trying to
`enter only “good” markets. Eachis trying to be in a market with the equilibrium, or fewer than
`equilibrium, numberoffirms.
`
`SSSSSSSSSSSSSSSSSSSSSSSSSSeeSSSSSeeeeeeEEEee
`
`8 The correct assumption undoubtedly varies by market.
`9 Large markets cannot be served by only one firm because each firm has an upward sloping supply curve due to the
`opportunity cost of capital (using all its machinesto increase output of one drug) andthe risk associated with
`dedicatinga large part of the firm’s capital to one drug.
`10 The probability of entry times the number of potential entrants can be greater or less than the numberoffirms
`which can earn positive profits, which direction the inequality goes depends onthe shapeofthe profit function.
`Dixit and Shapiro showthat for convex profit functions (Cournot, for example) on averagethere will be over-entry.
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`Thisraises the difficult problem of how generic firms make entry decisions in conjunction with
`
`other firms whenthey cannot explicitly coordinate. Bolton and Farrell (1990) examinetheclassic
`
`problem of two firms deciding whetheror not to enter a natural monopoly market. They are primarily
`
`interested in when a central planner can do better than the decentralized case of each firm deciding based
`
`on its own private information. The randomnessinherentin not coordinating will result in “mistakes.”If
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`firms could avoid those mistakes by using the services of a central planner, welfare would be increased.
`
`The increase in social welfare in their model comes from the reduction in “duplication” (both firms
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`enter) and “delay” (both firms wait for the other to enter). These are analogousto the generic
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`pharmaceutical industry where producer surplusis savedif the fixed cost of entry is not paid twice and
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`consumersurplusis increased if the entry occurs quickly so that prices fall. However, Bolton and Farrell
`
`show thatif private information, perhaps about the entry costs of the twofirms,is sufficiently important,
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`central planning will result in lowertotal surplus.
`
`It is difficult to isolate evidence of the negative externality problem from the ‘low-cost firms
`
`should enter’ story. If it is the case that an entrant’s profits depend on the numberofother entrants in the
`
`market, then an entry decision cannot depend on a firm’s own characteristics alone. Because of oligopoly
`
`or competitive interaction, the entrant’s beliefs about the actions of other entrants will affect its entry
`
`decision.!! Each firm has an interest in notentering if it believes too many otherfirms will enter. Beliefs
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`about other players’ strategies determines which equilibrium (of many)is selected. Anyset of beliefs
`
`that coordinated firms’ actions would work: geographic, alphabetic, phases of the moon, etc.. However,
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`beliefs are difficult to measure and I will be unable to use them in the empirical analysis.
`
`The cost story, on the other hand, reflects a situation where there are firms with sufficiently high
`
`fixed costs that they cannot earn positive profit even if given an entry slot; beliefs about whoelse will
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`enter do not affect entry decisions. However, if any firm with costs below a certain threshold can earn
`
`positive profits in the market, then market size should not be a helpful predictor of entry. The reason
`
`more firms enter larger markets is because they can fit - despite the business stealing efforts of their
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`rivals. Thus the significance of market size measures in predicting entry will provide some evidence for
`
`the existence of negative competitive externalities in the industry. ] use observable characteristics of the
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`firm andits portfolio to proxy for firm fixed costs of entry. Estimation relies on those firm characteristics
`
`as well as knowledge of actual and potential entry events and measures of market size such as revenue.
`
`11 Notice that beliefs matter only among firmsthat could makea profit if they were given an entry slot. For
`example,it is easy to construct an example of a natural monopoly where any firm in the cost distribution could earn
`non-negative profits as a monopolist, but none could earn non-negative profits in a duopoly.
`
`11
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`000012
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`IV. Data
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`The data I use to examine entry behavior are ANDAapprovals granted from 1984-1994. A firm
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`becomespart of the datasetif it is granted an ANDAinthe time period; a particular drug becomespart of
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`the datasetif it is applied for in an ANDA approved during this time period. Each approved ANDAis an
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`observation. The FDA provides the submission date, the approval date, the applicant nameas well as
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`characteristics of the drug such as ingredients, form, route (into the body), and strength.
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`I recode the
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`FDA’s detailed form and route descriptions into five basic categories according to the type of machinery
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`needed to manufacture a drug with certain characteristics and the cleanliness standards required in the
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`manufacturing facility. The first category is oral solid, which formsthe bulk of the observations in the
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`data. The second groupis injectable drugs, third is topical preparations, followed by oral liquids, and
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`then ocular drugs.'!2 Note that the ANDAs making upthe dataset are not a sample, but the complete
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`universe of ANDAsapproved in the United States during the time period 1984-94.
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`I have calculated or collected information on additional variables such as the firm's primary type
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`of product (brand or generic) and the parent of the applicant(if it exists). A drug’s therapeutic classis
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`assigned according to the description under“indications” in the 1993 volume of Physician’s GenRx, a
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`comprehensive pharmaceutical reference. There are approximately thirty therapeutic categories in the
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`dataset.
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`I also include the date legal restrictions on entry into the drug expire. Simply including thelast
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`patent expiration date is not a good method because,first ofall, there are usually several patents in force
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`and it is not obvious to the uninformed observer which oneis the binding patent. Also, the FDA may
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`have extended exclusivity rights due to excessive approval times, or may have granted an exclusivity
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`period for a new route or dosage form, among other innovations.
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`I resolved cases where the published
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`dates for these restrictions did not match the entry pattern by telephoning patent lawyers at the firm in
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`question. Although time-consuming, this method was quite successful; I was forced to exclude only a
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