estimating the effects of global patent protection in … · 2013. 10. 17. · estimating the...

Estimating the Effects of Global Patent Protection inPharmaceuticals: A Case Study of Quinolones in India

By SHUBHAM CHAUDHURI, PINELOPI K. GOLDBERG, AND PANLE JIA*

Under the Agreement on Trade-Related Intellectual Property Rights, the WorldTrade Organization members are required to enforce product patents for pharma-ceuticals. In this paper we empirically investigate the welfare effects of thisrequirement on developing countries using data for the fluoroquinolones subseg-ment of the systemic anti-bacterials segment of the Indian pharmaceuticals market.Our results suggest that concerns about the potential adverse welfare effects ofTRIPS may have some basis. We estimate that the withdrawal of all domesticproducts in this subsegment is associated with substantial welfare losses to theIndian economy, even in the presence of price regulation. The overwhelming portionof this welfare loss derives from the loss of consumer welfare. (JEL F13, L65, O34)

Under the Agreement on Trade-Related Intel-lectual Property Rights (TRIPS)—finalized duringthe Uruguay round of multilateral trade negotia-tions in 1995—nations must, as a condition ofmembership in the World Trade Organization(WTO), recognize and enforce product patents inall fields of technology, including pharmaceuti-cals. At the time the TRIPS agreement went intoeffect, many low- and middle-income countriesmade an exception for pharmaceuticals, even ifthey recognized product patents in other areas,because low-cost access to life-saving drugs andessential medicines was deemed to be an overrid-ing public policy priority. To meet their obliga-tions under TRIPS, however, these countries had

to introduce or amend their patent legislation toinclude pharmaceutical product patents, with thetransition and least-developed economies havinguntil 2005 to do so.

The negotiations leading up to TRIPS, and inparticular the provisions relating to pharmaceu-ticals, were highly contentious. Though morethan ten years have passed since TRIPS wasfinalized, there continues to be considerablecontroversy and debate regarding its merits. Themain point of contention is the claim made bygovernments of many poor developing econo-mies that unqualified patent protection for phar-maceuticals will result in substantially higherprices for medicines, with adverse conse-quences for the health and well-being of theircitizens. Countering this claim, research-basedglobal pharmaceutical companies, which havepotentially lost billions of dollars because ofpatent infringement by third world firms thathave reverse-engineered their products, arguethat the introduction of product patents is un-likely to raise prices significantly because mostpatented products have many therapeutic sub-stitutes. Moreover, they claim that the absenceof patent protection has served as a disincentiveto engage in research on diseases that dispro-portionately afflict the world’s poor, implyingthat patent protection for pharmaceuticals willactually benefit less-developed economies bystimulating innovation and transfer of technology.

Given the scope of TRIPS and the intensity ofthe accompanying debate, it is remarkable how

* Chaudhuri: World Bank, 1818 H Street, NW, Wash-ington, DC 20433 (e-mail: [email protected]);Goldberg: Department of Economics, Yale University, Box208264, New Haven, CT 06520, National Bureau of Eco-nomic Research, and Bureau for Research in EconomicAnalysis and Development (e-mail: [email protected]); Jia: Department of Economics, MassachusettsInstitute of Technology, 50 Memorial Drive, Cambridge,MA 02142 (e-mail: [email protected]). We would like to thankIsher Ahluwalia, former director, Indian Council for Re-search on International Economic Relations, for providingus with access to the primary data used in this study. Wealso thank Abhijit Banerjee, Steve Berry, Anne Case, AngusDeaton, Phil Haile, Elhanan Helpman, Michael Keane,Jenny Lanjouw, Dick Nelson, Ariel Pakes, Chris Paxson,Fiona Scott-Morton, T. N. Srinivasan, a coeditor, and thereferees for many detailed and constructive comments on anearlier draft, and participants at several seminars in theUnited States and Europe for helpful feedback.

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sparse is the evidence on which these divergentclaims are based.1 Apart from the findings of asmall number of studies we refer to in moredetail below, little is known about the extent towhich pharmaceutical prices in less-developedeconomies might increase with the introductionof product patents, and the magnitude of theassociated welfare losses.2 Past empirical stud-ies on the impact of patents on prices and inno-vative activity in various sectors, includingpharmaceuticals, have focused almost exclu-sively on developed economies. Aside from thefact that none of these studies estimates welfareeffects, the conclusions from these studies arenot directly pertinent to the TRIPS debate, be-cause the structure of demand for pharmaceuti-cals in less-developed economies differs fromthat in developed economies in several criticalrespects.3

Any assessment of the potential price andwelfare effects of TRIPS needs, therefore, to bebased on a better empirically grounded under-standing of the characteristics of demand andthe structure of markets for pharmaceuticals inpoor developing economies. To what extent areconsumers willing to trade off lower prices forolder, possibly less effective, therapies? Howdoes this vary across different therapeutic seg-ments? Are consumers willing to pay a pre-mium for the pedigree and brand reputation ofproducts marketed by subsidiaries of foreignmultinationals? How competitive are pharma-ceutical markets? The welfare of consumers

depends on the pricing strategies and decisionsof pharmaceutical firms. But these, in turn, de-rive from the firms’ assessment of the structureof market demand. If consumers are unwillingto pay substantially more for newer patenteddrugs for which there exist older, possiblyslightly less effective, generic substitutes, theability of patent-holders to charge a premiumwill be limited. As mentioned above, there havebeen a few studies that carefully consideredthese issues and used explicit models of con-sumer and firm behavior to simulate the welfarelosses implied by patent protection.4 Their find-ings are ultimately limited, however, by the factthat the simulations that are used to evaluate thepotential impact of patents are in each instancebased on assumptions about demand character-istics and market structure, rather than on actualestimates of the relevant parameters.

This paper takes a first step toward filling thisgap. We provide the first rigorously derivedestimates of the possible impact of pharmaceu-tical product patents on prices and welfare in adeveloping economy. Using detailed product-level data on monthly pharmaceutical pricesand sales over a two-year period from January1999 to December 2000, we estimate key priceand expenditure elasticities and supply-side pa-rameters for the fluoroquinolone (quinolone,henceforth5) segment of systemic antibacterials(i.e., antibiotics) in the Indian pharmaceuticalsmarket. We chose this segment both because itcontains several products that were still underpatent in the United States during our sampleperiod, and because antibiotics are importantfrom a public health policy point of view (com-pared to, for example, Prozac, Viagra, or otherlifestyle drugs that were also under patent pro-tection in the United States during this period).We then use our estimates to carry out counter-factual simulations of what prices, profits (ofboth domestic firms and subsidiaries of foreignmultinationals), and consumer welfare wouldhave been, had the quinolone molecules westudy been under patent in India as they were in

1 There is a sizeable theoretical literature on the welfareimpact of patent protection that generally finds that theeffects of patents in a multicountry setting are substantiallymore complicated than their respective effects in a singleclosed economy where both innovating firms and innova-tion beneficiaries (i.e., consumers) are of the same nation-ality (see William Nordhaus, 1969; Judith C. Chen andGene M. Grossman, 1990; Ishac Diwan and Dani Rodrick,1991; Alan V. Deardorff, 1992; Elhanan Helpman, 1993;and Grossman and Edwin L.-C. Lai, 2004). Empirical workin this area is, however, still in its infancy.

2 Even less is known about the other central questionsrelevant to the TRIPS debate, namely the extent to whichpharmaceutical research and product development prioritiesare likely to shift as a result of TRIPS, and how large thewelfare benefits of any therapeutically innovative drugs thatresult from this shift are likely to be. The only paper that hascarefully addressed such questions is Jean O. Lanjouw andIain M. Cockburn (2001).

3 For a representative example of estimation of pharma-ceuticals demand in developed countries, see Sara F. Ellisonet al. (1997).

4 See, for instance, Pablo M. Challu (1991); Carsten Fink(2000); Keith E. Maskus and Denise E. Konan (1994); JulioJ. Nogues (1993); Arvind Subramanian (1995); and Ja-yashree Wattal (2000).

5 Technically, the term “fluoroquinolones” refers to thelatest generation of quinolones. Older quinolones, however(e.g., nalidixic acid), have market shares close to zero.

1478 THE AMERICAN ECONOMIC REVIEW DECEMBER 2006

the United States at the time. The presence ofmany therapeutic substitutes within the antibi-otics segment make this product category idealfor investigating the claim that the presence ofclose substitutes will prevent drug prices fromrising once patent protection is enforced. Ofcourse, to the extent that our estimates refer toantibiotics, they are not directly applicable toother pharmaceutical product categories thatmay have different demand structures. For ex-ample, a finding of large substitution effectstoward nonpatented products would not neces-sarily apply to a market segment with only few,or possibly no, therapeutic substitutes. Still, afinding of limited substitution toward otherdrugs and associated large price increaseswould suggest that the effects of patent enforce-ment in other pharmaceutical segments withfewer therapeutic substitutes might be evenlarger.

India provides a natural setting for our anal-ysis for a number of reasons. It is a leadingexample of a low-income country that did notrecognize pharmaceutical product patents at thetime the TRIPS agreement went into effect. Infact, during the Uruguay round of negotiations,India led the opposition to the TRIPS articlesmandating pharmaceutical product patents. Interms of the structure of demand, India is aprototypical example of a low-income countrywith a large number of poor households which,because health insurance coverage is nonexist-ent, have to meet all medical expenses out ofpocket. Moreover, the disease profile of theIndian population mirrors that of many otherlow-income countries and is considerably dif-ferent from that of most developed economies.Lastly, the domestic Indian pharmaceutical in-dustry, which as of 2002 was the largest pro-ducer of generic drugs in the world in terms ofvolume, is typical of that of many middle-in-come countries with large numbers of small andmedium-sized firms with significant imitativecapabilities producing and marketing drugs do-mestically that are under patent elsewhere.

During the period covered by our data, sev-eral molecules in the quinolone family were stillunder patent in the United States, but productscontaining these molecules were being pro-duced and distributed in India by both a numberof domestic firms and a number of local subsid-iaries of foreign multinationals. We aggregatethese products into a number of mutually exclu-

sive product groups where, within each productgroup all products contain the same quinolonemolecule (e.g., ciprofloxacin or norfloxacin),and are produced by firms with the same do-mestic or foreign status. We then estimate atwo-level demand system employing the Al-most Ideal Demand System (AIDS) specifica-tion of Angus S. Deaton and John Muellbauer(1980b) in both levels. The higher level corre-sponds to the allocation of expenditures tovarious subsegments within the systemic anti-bacterials segment of the market. At the lowerlevel, we estimate the parameters relevant forthe allocation of expenditures within the quin-olone subsegment to the various productgroups within this subsegment (e.g., foreignciprofloxacin, domestic ciprofloxacin, or do-mestic norfloxacin).

With these estimates in hand we turn to thecounterfactuals. The basic counterfactual sce-narios we consider all involve the withdrawal ofone or more of the domestic quinolone productgroups from the market. The idea here is thathad U.S. patents for, say, ciprofloxacin, beenrecognized in India, all domestic products con-taining ciprofloxacin would not be present in themarket. That would leave only the foreign cip-rofloxacin product group in the market. Usingour estimates of the own, cross-price, and ex-penditure elasticities of the various productgroups, as well as estimates for the upper andlower bounds of marginal costs of production,we are able to simulate the prices and marketshares that would obtain under each of the sce-narios. Moreover, using the expenditure func-tion associated with the higher-level AIDSspecification, we are able to calculate the wel-fare loss—measured in terms of the compensat-ing variation, i.e., the additional expenditurethat the representative Indian consumer wouldneed to incur to maintain her utility level inthe face of the domestic product withdraw-al(s) and the accompanying price and marketshare changes—under each of the counterfac-tual scenarios.

Apart from the fact that our counterfactualsimulations are based on estimated rather thanassumed parameter values, this paper buildsupon the earlier studies in two substantive, and(it turns out) empirically important, ways.

First, by accommodating the possibility thatconsumers may differentiate between domesticand foreign products even when these products

1479VOL. 96 NO. 5 CHAUDHURI ET AL.: GLOBAL PATENT PROTECTION IN PHARMACEUTICALS

contain the same patentable molecule, we allowfor an additional channel through which theintroduction of product patents and the conse-quent withdrawal of domestic products may ad-versely affect consumers, that is, through theloss of product variety. In contrast, previousstudies on developing countries assume thatconsumers are indifferent between foreign anddomestic products that contain the same mole-cule. What this implies is that any adverse wel-fare effects are realized only through increasedprices. The difference is most evident if weconsider a scenario under which domestic prod-ucts are forced to withdraw from the marketbecause of the introduction of product patents,but strict price regulations maintain prices atprepatent levels. In our approach, consumerswould still experience a welfare loss, whereas inthe framework adopted in earlier studies, such ascenario would entail no loss of welfare.

Empirically, the component of the consumerwelfare loss attributable to the reduction of va-riety from the withdrawal of domestic productsturns out to be significant. We interpret thiscomponent as capturing primarily an “ease ofaccess” effect: due to differences in the market-ing and distribution networks, domestic prod-ucts are more readily available to Indianconsumers than products produced by foreignsubsidiaries. From a policy perspective, thissuggests a possible role for compulsory licens-ing, in addition to or in lieu of price regulation,since the latter, by itself, will not alleviate thewelfare loss due to loss of variety. Alterna-tively, one could argue that—to the extent thatthe loss we attribute to the reduction of productvariety is due to the fact that the current productportfolios and distribution networks of foreignproducers are limited—it is purely a transitionalphenomenon, and should thus not be included inthe welfare calculations. This is a controversialpoint we discuss in detail in the results section.If foreign firms respond to patent enforcementby investing in distribution networks or by us-ing licensing agreements with domestic firms tomake their products more readily available toIndian consumers, the “ease of access” effectwould indeed diminish in importance in thelonger run, though of course it could be signif-icant in the first years after patent enforcement.Whether these investments will materialize,however, is open to debate. If TRIPS is accom-panied by price regulation in order to limit price

increases in poor developing countries, the in-centives of multinationals to invest in marketingand distribution in these countries may dimin-ish. At any rate, to take into account the possi-bility that the welfare loss due to the reductionof variety is a temporary phenomenon, we alsopresent a more conservative welfare loss esti-mate, by subtracting the “product variety” com-ponent from our total loss estimate. This givesus a lower bound estimate that is due to priceincreases alone. Though it is only about a thirdof our upper bound estimate, this lower boundestimate is still very large in absolute termsrepresenting 24 percent of antibiotic sales in2000.

A second, and perhaps even more important,methodological difference between this paperand earlier studies is that we allow for, andflexibly estimate, a range of cross-product-group and cross-molecule substitution effects.In contrast, cross-price effects are ignored inearlier studies. To see why cross-price effectsare likely to alter estimated welfare effects sig-nificantly in this context, imagine a scenariowhere the introduction of patents leads to mo-nopoly pricing in the market for a particularpatentable molecule. If the markets for potentialsubstitutes are imperfectly competitive, then theincrease in price in the original patentable mar-ket will lead to corresponding upward priceadjustments in the related markets, as producersof substitute products reoptimize in the face ofthe increased demand for their products. Themagnitude of any upward adjustments will nat-urally vary with the degree of competition inrelated markets, and with the strength of thecross-price effects. But as long as the cross-price effects are positive, and related marketsare not perfectly competitive, the loss of con-sumer surplus because of monopoly pricing inone market will be multiplied through the rippleeffects of upward price adjustments in relatedmarkets.

If this were just a theoretical possibility, itwould not be of much interest. These multipliereffects, however, turn out to be substantial inour counterfactual scenarios. Most strikingly,the estimated loss of consumer welfare from thesimultaneous withdrawal of all four domesticproduct groups—the scenario that most closelyresembles what is likely to happen underTRIPS—is more than two times the sum of theestimated losses from the four separate scenar-


ios in each of which only one of the domesticproduct groups is withdrawn. What this veryclearly indicates is that past studies that haveestimated the aggregate effects of patent protec-tion by adding up the losses, estimated sepa-rately in each of a number of patentablemarkets, may have substantially underestimatedthe magnitude of the consumer welfare lossesfrom the introduction of pharmaceutical productpatents.

In absolute terms, we estimate that in theabsence of any price regulation, the prices offoreign patented products would rise between100 percent and 400 percent. In the more real-istic case of some form of price regulation thatwould keep drug prices fixed at their pre-TRIPSlevel, the total annual welfare losses to theIndian economy from the withdrawal of all fourdomestic product groups in the quinolone sub-segment would be on the order of Rs. 13.70billion, or about 50 percent of the sales of theentire systemic antibacterials segment in 2000.At the then-prevailing exchange rate, this trans-lates into a figure of US$305 million. Of thisamount, foregone profits of domestic producersconstitute roughly Rs. 2.3 billion or US$50 mil-lion. The overwhelming portion of the totalwelfare loss, therefore, derives from the loss ofconsumer welfare.

The welfare loss we estimate represents onlythe static costs of patent enforcement arisingfrom pricing distortions and reduction in prod-uct variety. Our approach does not address thepotential dynamic benefits of innovations thatmay result from international property rightsprotection. Nevertheless, we believe that esti-mating these static costs is important wheneverthere is a radical change in policy—whichTRIPS represents for a good part of the devel-oping world. Even if there is the potential forlong-term benefits, knowledge of the short-runcosts is important for designing an appropriatepolicy response that will potentially mitigate theadverse short-run impact. Having said that, itis worth noting that, according to our esti-mates, the total profit gains of patent enforce-ment to foreign producers in the absence ofany price regulation would be only aboutUS$53 million per year. With price regulationthat would keep the prices of drugs suppliedby multinational subsidiaries at their pre-TRIPS level, the profit gains become onlyUS$19.6 million per year.

The remainder of this paper is organized asfollows. In the next section we lay out theessential features of the Indian pharmaceuticalsmarket, provide more detail about the segmentsthat we focus on in the empirical analysis, andbriefly describe the primary data we use. Sec-tion II describes the analytic framework and theeconometric strategy we use to estimate therelevant parameters and construct the counter-factual scenarios. We discuss our results in Sec-tion III. Section IV concludes.

I. The Setting and the Data

Between April 1972, when the Indian PatentsAct (1970) became effective, and March 2005,when India’s parliament passed the 3rd Amend-ment of the Patents Act, India did not recognizeproduct patents for pharmaceuticals. The IndianPatents Act, which replaced the inherited Brit-ish colonial law regarding intellectual propertyrights, specifically excluded pharmaceuticalproduct patents and admitted process patentsonly for a period of seven years. In contrast, thelatest amendment recognizes patents on endproducts that under the new regime will remainin force for 20 years.6

The two stated objectives of the 1970 actwere: the development of an indigenous pharma-ceuticals industry; and the provision of low-costaccess to medicines for Indian consumers. Con-sistent with these objectives, and with the broaderleftward tilt in policy, a number of other measureswere introduced—drug price controls, restrictionson capacity expansion, limits on multinational eq-uity shares—that in the years since have, on theone hand, kept pharmaceutical prices low and, onthe other, encouraged the development of the In-dian pharmaceutical industry. Many of these reg-ulations and restrictions have been lifted or easedsince the mid-1980s with marked acceleration inthe pace of liberalization during the 1990s.

Over the last 20 years, the Indian pharmaceu-tical industry has grown rapidly to the pointwhere it is now the world’s largest producer offormulations in terms of volume, and one of the

6 Indian companies that are now producing drugs forwhich patent applications were submitted between the sign-ing of the TRIPS agreement in 1995 and January 1, 2005,will be allowed to continue producing if they pay a royaltyto the patent holder.


world’s largest producers of bulk drugs.7 Thestructure of the industry has also evolved. In1970, the industry was dominated by multina-tional subsidiaries; by 2001, Indian-owned firmswere not just the leading players in the industry,many had also become major exporters.

The data we use in this paper are from theretail pharmaceutical audits of ORG-MARG,India’s premier market research and consultingfirm. The audit provides detailed product-levelinformation—estimates of monthly retail salesin each of the four geographic zones of India,price, dosage form, launch date, brand name,chemical name, therapeutic categorization—on

all pharmaceutical products sold in India byabout 300 of the largest firms, representingroughly 90 percent of domestic retail sales ofpharmaceuticals. The coverage of the audit isextensive, reaching a representative panel ofthousands of retail chemists in over 350 citiesand towns. The data collected, which providethe only real source of disaggregate informationon the Indian pharmaceutical market, are usedby both the government of India in formulatingpricing policy and other decisions, and the In-dian pharmaceutical industry in determiningpricing and marketing strategies. We have in-formation at a monthly frequency for the periodof January 1999 to December 2000. Tables 1 to3 provide a set of descriptive statistics that areessential for understanding the focus of ouranalysis and interpreting our results.

As noted earlier, the characteristics of de-mand for pharmaceuticals in India are likely todiffer considerably from those in developedeconomies. With a share of 23 percent, the

7 Bulk drugs are the therapeutically relevant active phar-maceutical ingredients that are combined with a variety ofinactive ingredients to make the formulations ultimatelyconsumed by patients. Firms in the pharmaceutical sectorcan be one of three types: bulk drugs producers, pureformulators, or integrated firms, which produce both bulkdrugs and market formulations.

TABLE 1—THE QUINOLONES SUBSEGMENT

Molecule

Shares (%)of quinolones sales Sales (Rs. mill): 2000

Domesticfirms

Foreignsubsidiaries

Domesticfirms

Foreignsubsidiaries

Ciprofloxacin 53.0 2.7 3,030 156Norfloxacin 11.2 0.1 640 3Ofloxacin 11.6 3.1 665 177Sparfloxacin 10.8 0.1 620 4Lomefloxacin 1.5 � 86 �Pefloxacin 1.3 0.1 72 5Levofloxacin 0.0 � 0 �Nalidixic acid 1.3 � 73 �

TABLE 2—BASIC INFORMATION ABOUT THE FOUR QUINOLONES MOLECULES: 2000

Ciprofloxacin Norfloxacin Ofloxacin Sparfloxacin

U.S. or European patent holder Bayer Merck Ortho-McNeil Rhone-PoulencYear of U.S. patent expiry 2003 1998 2003 2010Year of U.S.-FDA approval 1987 1986 1990 1996Year first introduced in India 1989 1988 1990 1996No. of domestic Indian firms 75 40 17 25No. of foreign subsidiaries 8 2 2 1Sales weighted average per-unit API*

price of products produced by:Domestic Indian firms 11.24 9.05 90.08 78.84Foreign subsidiaries 10.35 5.28 108.47 �

* API: Active pharmaceutical ingredient.


antiinfectives segment ranks second in India,whereas in the world market it is fifth and has ashare of only 9.0 percent. Hence, antiinfectivesare important in India not only from a health

and public policy point of view, but also as asource of firm revenue.

With this in mind, we focus on one partic-ular subsegment of antiinfectives, namely the

TABLE 3—SUMMARY STATISTICS FOR THE QUINOLONES SUBSEGMENT: 1999–2000

North East West South

Annual quinolones expenditure per household (Rs.) 31.25 19.75 27.64 23.59(3.66) (3.67) (4.07) (2.86)

Annual antibiotics expenditure per household (Rs.) 119.88 84.24 110.52 96.24(12.24) (12.24) (9.60) (9.96)

No. of SKUsForeign ciprofloxacin 12.38 11.29 13.08 12.46

(1.50) (1.90) (1.02) (1.06)Foreign norfloxacin 1.83 1.71 2.00 1.58

(0.70) (0.75) (0.88) (0.83)Foreign ofloxacin 3.04 2.96 2.96 3.00

(0.86) (0.86) (0.91) (0.88)Domestic ciprofloxacin 106.21 97.63 103.42 105.50

(5.99) (4.34) (7.22) (4.51)Domestic norfloxacin 38.96 34.96 36.17 39.42

(2.71) (2.68) (2.51) (3.79)Domestic ofloxacin 18.46 16.00 17.25 17.25

(6.80) (6.34) (5.86) (6.35)Domestic sparfloxacin 29.83 28.29 31.21 29.29

(5.57) (6.38) (6.88) (6.57)Price per-unit API* (Rs.)

Foreign ciprofloxacin 9.58 10.90 10.85 10.07(1.28) (0.66) (0.71) (0.58)

Foreign norfloxacin 5.63 5.09 6.05 4.35(0.77) (1.33) (1.39) (1.47)

Foreign ofloxacin 109.46 109.43 108.86 106.12(6.20) (6.64) (7.00) (11.40)

Domestic ciprofloxacin 11.43 10.67 11.31 11.52(0.16) (0.15) (0.17) (0.13)

Domestic norfloxacin 9.51 9.07 8.88 8.73(0.24) (0.35) (0.37) (0.20)

Domestic ofloxacin 91.63 89.64 85.65 93.41(16.15) (15.65) (14.22) (14.07)

Domestic sparfloxacin 79.72 78.49 76.88 80.28(9.76) (10.14) (11.85) (10.37)

Annual sales (Rs. mill)Foreign ciprofloxacin 41.79 24.31 45.20 29.47

(15.34) (8.16) (12.73) (6.48)Foreign norfloxacin 1.28 1.00 0.58 0.73

(1.01) (0.82) (0.44) (0.57)Foreign ofloxacin 54.46 31.84 35.22 31.11

(13.99) (9.33) (9.06) (7.03)Domestic ciprofloxacin 962.29 585.91 678.74 703.81

(106.26) (130.26) (122.26) (87.40)Domestic norfloxacin 222.55 119.71 149.18 158.29

(38.84) (19.45) (26.91) (16.26)Domestic ofloxacin 125.02 96.21 149.36 112.05

(44.34) (30.11) (52.82) (42.59)Domestic sparfloxacin 156.17 121.75 161.30 98.11

(31.41) (25.76) (46.74) (34.20)

Note: Standard deviations in parentheses.* API: Active pharmaceutical ingredient.


quinolone subsegment. Quinolones fall into thesystemic antibiotics and antibacterials segmentof the Indian pharmaceuticals market, whichgenerates over three-quarters of the revenues inthe antiinfectives segment.8 The systemic anti-bacterials segment includes all of the originalmiracle drugs that first sparked the growth ofthe global research-based pharmaceutical indus-try in the post–World War II period, as well aslater generations of molecules that have beenintroduced in the last four decades.

Among systemic antibacterials, quinolonesare the latest generation molecules available inIndia. We focus our analysis on quinolones forseveral reasons. First, quinolones are the drug ofchoice for a large number of bacterial infec-tions, some of which are also treated by alter-native drugs (see Table A1 in the Appendix,which outlines the spectrum of activity for eachmolecule family within the antibacterials seg-ment). Hence, if there were one product groupfor which we would expect to have many sub-stitutes readily available, this would be quino-lones. Second, with a share of 20 percent in thesales of systemic antibacterials, quinolones rep-resent one of the largest subsegments within thistherapeutic category. Finally, several moleculeswithin the quinolone subsegment were still un-der patent in the United States at the time of ourinvestigation. Table 2 details the basic informa-tion about the four quinolone molecules that arethe focus of our analysis. The first row showsthe year of U.S. patent expiry: this ranges from1998 for norfloxacin to 2010 for sparfloxacin.Quinolones include, in principle, four moremolecules that are listed at the bottom of Ta-ble 1; the market shares of these molecules arenegligible, however, so that we exclude thesemolecules from our analysis.

Table 2 reveals several other interesting factsabout competition in the quinolone market inIndia. First, note the large number of firmsoperating in this subsegment. The large numberof domestic firms is perhaps not that surprising,given that pharmaceutical product patents werenot recognized in India.9 What is more surpris-ing is the number of foreign firms selling pat-ented products (e.g., ciprofloxacin); the fact that

multiple foreign firms sell a patented productindicates that such firms often “infringe”10

patent laws in India, while complying with themin developed world countries. The last two rowsof Table 2 further indicate that domestic prod-ucts often sell at a premium. With the exceptionof ofloxacin, the average prices of products of-fered by Indian firms are higher than the pricesof products offered by foreign subsidiaries. Thispreliminary evidence suggests that Indian con-sumers do not place a premium on the brandname and reputation of big multinational phar-maceutical concerns. Moreover, the higherprice of domestic products does not seem toprevent domestic companies from capturing alarge market share. This is most evident in thecase of ciprofloxacin, where domestic firmshave, with 53 percent, the largest share in thetotal sales of quinolones—this despite the factthat the average price of these products is 10percent higher than the price of foreign productscontaining the same molecule.

Table 3 provides additional summary statis-tics for our data, broken down by region. Thefirst two rows of the table report the averageannual household expenditure on quinolonesand antibiotics, respectively. Note that in bothcases the average expenditure is higher in thenorth and west; these regions include states withhigher per capita incomes, and tend to be moreindustrialized and urbanized than those in theeast and south. Pharmaceutical products areavailable in multiple presentations, that is, com-binations of dosage forms (e.g., capsule, tablet,syrup), strength (e.g., 100 milligrams, 500 mil-ligrams), and packet sizes (e.g., 50 capsule bot-tle, 100 tablet bottle). The various presentationsin which a product is available are often referredto as stock-keeping units, or SKUs.11 The num-ber of SKUs for each product group withinquinolones is reported at the top of Table 3. Aswith the more aggregate numbers on firms and

8 In addition to anti-bacterials, this segment also containsantivirals.

9 Accordingly, the common distinction between “branded”and “generic” products is irrelevant here.

10 We emphasize here that the word “infringe” belongsin quotes: because patent laws do not currently exist inIndia, infringement in the legal sense is not possible. It is,however, striking that the same firms that accuse Indianproducers of “piracy” sell in India products that are patentedin the United States, and for which the patent is held by adifferent multinational corporation.

11 For instance, a 100 capsule bottle of 100 milligramcapsules of a particular branded drug, and a 50 capsulebottle of 100 milligram capsules of the same branded drugwould be identified as two separate SKUs.


products reported in Table 2, the differencebetween domestic and foreign products isstriking. The number of SKUs offered byIndian firms is consistently larger than thenumber offered by subsidiaries of foreignmultinationals. The number of SKUs variesslightly across regions, but, more importantly,it varies across time, as some SKUs disap-pear, while new ones get introduced duringour sample period.

Many pharmaceutical products in India aresubject to price controls.12 While the specificsof the price regulation are too complex for anyeconomic model to capture adequately, themain concern for the empirical analysis is thatprice controls may lead to a lack of price vari-ation over time, so that the demand functioncannot be identified. Prices at the most disag-gregate, SKU, level are relatively stable overtime; there are variations due to occasionalchanges in the estimated cost (due, for example,to changes in exchange rates that affect the costof imported materials or bulk drugs), but suchvariations tend to be infrequent and small inmagnitude. The degree of time variation is,however, substantially larger once one aggre-gates to the product level. This variability stemsnot only from the fact that the SKUs over whichwe aggregate may experience changes in theirrespective prices at different points in time, butalso from the fact that the range of SKUs of-fered in the market does not remain constantover time. The entry and exit of presentationswithin the same product group that have differ-

ent prices effectively affects the price that con-sumers face for this drug in each period.

The middle portion of Table 3 reports themean price and standard deviation for eachproduct group by region. Prices vary by region,though there is no clear pattern emerging fromthe table with regard to the cross-regional vari-ation (in the sense of some regions being sys-tematically more expensive than others). Toexamine what portion of the total price variationis due to time versus regional variation, weconducted an analysis of variance of prices thatwe report in Table 4. The table is based onseparate regressions for each product group(pooling data across groups with big differencesin their average prices is not particularly infor-mative, as most of the price variation is ac-counted for by product group dummies). Thelast two columns of the table show the fractionof price variation accounted for by region andtime dummies, respectively. As evident fromthe table, a significant fraction of the total vari-ance in prices can be attributed to time varia-tion. In the demand estimation, we include a fullset of product group–specific regional dummies,so that the price parameters are identified en-tirely based on this time variation within eachproduct group. The time variation of productgroup prices is driven primarily by composi-tional changes within each group: the revenueshares of the individual SKUs that compriseeach product group change over time (see therelated discussion in Section IIC), while in eachperiod, there is entry and exit of SKUs into thesample. To check whether this pattern reflectsgenuine entry and exit, as opposed to samplingvariation, we examined the revenue shares ofthe SKUs that leave the sample relative to theones that remain during the entire period. Theresults are reported in Table 5. While the SKUs

12 The details of the procedures for price fixation canbe found in the official government Web site: http://www.nppaindia.nic.in/index1.html, under the link “Drug Price Con-trol Order 1995.” A new pharmaceutical policy was introducedin 2002, but our data were collected before that year.

TABLE 4—ANALYSIS OF PRODUCT PRICE VARIANCE

Product groupPartial SS

timePartial SS

region Total SSPercentage

explained by timePercentage

explained by region

Foreign ciprofloxacin 0.296 0.306 1.047 28.3% 29.2%Foreign norfloxacin 1.036 1.002 4.179 24.8% 24.0%Foreign ofloxacin 0.429 0.021 0.571 75.2% 3.6%Domestic ciprofloxacin 0.005 0.088 0.104 5.0% 84.1%Domestic norfloxacin 0.059 0.098 0.198 29.6% 49.4%Domestic ofloxacin 2.858 0.104 3.056 93.5% 3.4%Domestic sparfloxacin 1.754 0.032 1.860 94.3% 1.7%


that exit tend to be smaller (their average shareis 1 percent as opposed to 3.4 percent for thoseSKUs that are present during the entire sampleperiod), the shares of the two groups do notseem orders of magnitude apart.13 In addition,our data cover only the 300 largest firms sellingin the Indian market, so that firms with verysmall shares are not included in our sample.

II. The Analytic Framework and EstimationApproach

Patent enforcement in the Indian pharmaceu-tical market will have the effect of eliminatingdomestic products whose active pharmaceuticalingredients are protected by (foreign) patents.Thus, assessing the effects of patent enforce-ment is tantamount to assessing the effects ofwithdrawing domestic products from the mar-ket. This task is the converse of evaluating newproduct introduction; accordingly, the concep-tual framework we use to address the questionsof interest is similar to the one developed in theliterature for the valuation of new goods.14

We start by estimating demand for quino-lones. Given that the market is characterized byimperfect competition, the counterfactual anal-ysis requires that we also model the supply side,as removal of one product will affect the pricesof other products, especially those that are closecompetitors. The existence of price regulationin the Indian pharmaceutical market imposespotential constraints on firms’ maximizationproblem. Given these constraints and the com-

plexity of the price regulation process, the typ-ical approach of deriving estimates of actualmarginal costs and markups by exploiting thefirst-order conditions of profit maximizing firmsdoes not seem particularly promising. Instead,we use our demand estimates to place upper andlower bounds on marginal costs and markups.

With demand elasticities and upper and lowerbounds for marginal costs in place, we thenconduct counterfactual simulations. We con-sider several alternative scenarios, dependingon the number of domestic products affected bypatent enforcement. For each scenario, we com-pute the counterfactual prices, and use them toassess the effects of domestic product with-drawal on consumer welfare (as measured bythe compensating variation), firm profits, andsocial welfare. As with the valuation of newproducts, the big conceptual problem facing thispart of the analysis is that we need to extrapo-late from the region of the data to the point atwhich demand for the products that exit themarket becomes zero. This conceptual issue ispresent in any attempt to evaluate a major pol-icy change for which no historical precedentexists, like the enforcement of patent laws inIndia. One advantage of the present study is thatwe have a limited set of price data for Pakistan,a country with similar demographics to India,but with a market structure that resembles theone that would emerge in India under patentenforcement (monopoly of multinational sub-sidiaries). By comparing the prices of productsoffered by multinationals in Pakistan to thosewe compute in our counterfactual simulationsfor the products that would be offered by mul-tinationals in India if patent laws were enforced,we can get a sense of how plausible our coun-terfactual estimates are.

A. Demand

The demand modelling is based on the multi-stage budgeting approach. Our primary motiva-tion for adopting this approach is a practical one.In the multistage budgeting approach, the depen-dent variable is defined as a revenue share, whichis appealing, given that the products we include inthe analysis contain different molecules (i.e., ac-tive pharmaceutical ingredients, or APIs). Eventhough we do have data on the quantity of therelevant API (e.g., 100 milligrams of ciprofloxa-cin) contained in each product, converting the

13 While we cannot completely rule out the possibilitythat some of the exit is due to sampling variation, note thatthe latter should be reflected in low precision of the demandparameter estimates. The demand parameters, however, areprecisely estimated.

14 See Manuel Trajtenberg (1989), Jerry A. Hausman(1994), and Timothy F. Bresnahan (1997) for representativeexamples and a discussion of the relevant issues.

TABLE 5—REVENUE SHARES OF THE EXITING SKUS

Revenue share ofexiting SKUs

Revenue shares ofall SKUs

Full sample 1.0% 3.4%Northern region 0.8% 3.3%Eastern region 1.2% 3.6%Western region 1.0% 3.4%Southern region 1.2% 3.3%


revenue shares to physical shares is extremelydifficult, if not infeasible, in the case of antibiotics.Because such drugs are “systemic” by nature, theyare used to treat a large number of infections, andthe dosage of each drug depends on the particularinfection it is supposed to address (for example thedosage will differ depending on whether the anti-biotic is used to combat an ear infection or tuber-culosis). This particular feature of antibioticscomplicates the conversion of revenue to physicalshares.

The basic idea of the multistage budgetingapproach is to use the therapeutic classificationof a product—i.e., the therapeutic segment andsubsegment the product belongs to—to orga-nize all products in the systemic antibacterialssegment into a hierarchical taxonomy, consist-ing of two levels. At the higher level are thevarious subsegments of systemic antibacterials.The first stage of budgeting corresponds to theallocation of expenditures across the subseg-ments in this upper level of the taxonomy.

In the second stage of the budgeting process,corresponding to the lower level of the taxon-omy, a flexible functional form is adopted tomodel how the expenditures allocated to eachsubsegment are distributed across the productswithin that subsegment. In particular, to modeldemand at the second stage we employ theAIDS specification proposed by Deaton andMuellbauer (1980b).15

While the two-stage demand estimation ap-proach offers functional form flexibility, its ap-plication to the Indian systemic antibioticsmarket poses a few problems. The first is thatdue to entry and exit, many SKUs and evenproducts in our sample are not present in everyperiod. AIDS does not have a good way ofdealing with a varying number of products, as itwas developed with broad commodity catego-ries in mind, which are consumed by all con-sumers every period. To solve this problem, weaggregate within each subsegment (e.g., quino-lones) SKUs into product groups, where withineach product group, all SKUs contain the samemolecule and are produced by firms with thesame domestic/foreign status. Specifically, let aSKU k be indexed by its molecule (or API) M, its

domestic/foreign status DF indicating whether itis produced by a domestic (Indian) or a subsidiaryof a foreign (multinational) firm, a particular pre-sentation s, and the particular firm f that producesit. We aggregate SKUs over presentations andfirms to obtain a newly defined product group i,which is indexed only by molecule M and domes-tic/foreign status DF, and has revenue Ri � ¥f,sRk, with i � (M, DF), k � (M, DF, f, s), and pricepi � ¥f,s �kpk, where �k denotes the conditional(on M and DF) revenue share of this particularproduct, i.e.:

(1) �k �Rk

Ri.

In most cases, the resulting product groups arebroad enough to be present every period.16

The usual concern with this aggregation pro-cedure is that it may lead us to overstate firms’market power, as we ignore competition amongfirms with the same domestic/foreign status,producing the same molecule. In the presentapplication, however, this concern is unlikely tobe of great importance, as the effect of patentenforcement is to wipe out all domestic compe-tition at once, while granting foreign firms mo-nopoly power; hence, competition among firmsfor patented molecules becomes irrelevant. Theaggregation according to the domestic/foreignstatus (within a particular molecule) thus corre-sponds to the scope of our analysis and theparticular questions of interest.

The second problem is that, for our approachto be useful in welfare analysis, the allocation oftotal expenditures to group expenditures at thehigher stage has to be modelled in a way con-sistent with utility maximization. In general, thesolution of this allocation problem requiresknowledge of all individual product prices.

15 Representative applications of the multistage budget-ing approach include Sara F. Ellison et al. (1997), Hausman(1994), and Hausman and Gregory K. Leonard (2002).

16 We are missing only four observations (i.e., month/region combinations), all for the drug group of ForeignNorfloxacin: August 1999 in the south, May 2000 in thewest, October 2000 in the south, and November 2000 in theeast. In these cases, we set the revenue shares of ForeignNorfloxacin equal to zero. In general, with 0.1 percent ofquinolone sales (see Table 1, row 2), Foreign Norfloxacinhas a very small share of the market. This probably explainswhy the results pertaining to this drug group are unreliable:it is the only drug group for which we do not obtain asignificant price elasticity of demand, while its cross-priceelasticities with other foreign drug groups often have thewrong sign.


From an empirical point of view, this is notparticularly useful, as it eliminates all compu-tational advantages of the two-stage approach.To address this problem, we adopt an approxi-mate solution to model the higher-level expen-diture allocation along the lines suggested byDeaton and Muellbauer (1980b, pp. 131–32).This gives rise to a two-level AIDS specification.

Consider the lower-level estimation first,which refers to the allocation of a particularsubsegment’s expenditure to the product groupswithin the subsegment. In our application, therelevant subsegment is quinolones, which weindex with Q. Let the product groups within thissubsegment be indexed by i � 1, ... N; pi be theprice of product group i (where, as noted above,i refers to a particular molecule and domestic/foreign status combination); and XQ be the totalexpenditure on the quinolone segment. The rev-enue share of each product group is given by:

(2) �i � �i � �j

�ijln pj � �iln�XQ

PQ�,

where �i, the revenue share of product group i,is defined as:

(3) �i �pi qi

¥j

pj qj�

xi

XQ, with i, j � Q,

XQ is the overall expenditure on the quinolonesubsegment, and PQ is a price index given by

(4) ln PQ � a�p� � �0 � �i

�iln pi

�1

2 �i

�j

�̃ijln piln pj .

With a limited number of product groups and asufficiently large number of time-series observa-tions, the flexibility implied by the AIDS modeldoes not impose too many demands on the data. Inthe present application, however, where the num-ber of observations is limited, the AIDS model isnot estimable in this general form. To reduce thenumber of parameters that need to be estimated,we impose two sets of restrictions.

The first set of restrictions is implied by thetheory of utility maximization. Specifically,these restrictions are:

● Adding-up: ¥k �k � 1; ¥k �k � 0; ¥k �̃kj �0, @j.

● Homogeneity: ¥k �̃jk � 0, @j.● Symmetry: �ij � 1⁄2 [�̃ij � �̃ji] � �ji. This last

restriction by itself reduces the number of �parameters to [N(N � 1)]/2.

The second set of restrictions we impose aimsat further reducing the number of � parameters tobe estimated by exploiting our knowledge of thisparticular market. Specifically, for each productgroup i, we allow one �ij parameter for all productgroups j that have different molecules from prod-uct group i and are produced by foreign firms, andone �ij for all product groups j with differentmolecules produced by domestic firms. We don’timpose any restrictions on the �ij parameter whenproduct group j has the same molecule as productgroup i. (By construction, product groups i and jcontain products produced by firms with differentdomestic/foreign status.)

To better illustrate the nature of the restric-tions we impose on the patterns of substitutionacross products, some additional notation isneeded. Let d(i, j) be an indicator of the degreeof similarity (or difference) between productgroup i and product group j, along the dimen-sions we are able to observe (molecule M anddomestic/foreign status DF). For any two prod-uct groups, i and j, d(i, j) can take on one of thefollowing three values17:

(5)

d�i, j� � � (1, 0) if Mi � Mj , DFi � DFj

(0, 1) if Mi � Mj , DFi � DFj

(0, 0) if Mi � Mj , DFi � DFj

.

Let

(6) Diab � �j : d�i, j� � �a, b��.

17 The sequence (1, 1) is not possible for two differentproducts; in this case, the � parameter corresponds to theproduct’s own-price effect, that is, �ii.


The equation at the lower level becomes:

(7) �i � �i � �iiln pi � �i,10ln pj, j � Di10

� �j � Di

01

��i,01ln pj�

� �j � Di

00

��i,00ln pj� � �iln�XQ

PQ�.

Note that:

● The parameter �ii captures a product group’sown-price effect (there will be as many �iiparameters as the number of product groups).

● The parameter �i,10 captures the cross-priceeffect of the product group containing prod-ucts with the same molecule but produced byfirms of different nationality.

● The parameter �i,01 captures the cross-priceeffects of product groups containing productswith different molecules but produced byfirms with the same nationality.

● The parameter �i,00 captures the cross-priceeffects of product groups containing productswith different molecules produced by firms ofdifferent nationality.

Before we take the demand equation to thedata, we make two modifications. The first oneis to let the product-specific effects �i vary byregion r. The resulting product-specific regionaleffects �ir have two interpretations: first, theycontrol for the “quality” of each drug, withquality differences being allowed to vary acrossregions; second, they proxy for demographicsand other demand shifters, which vary by re-gion, and may affect the demand of each prod-uct group differently.18 Note that by includingproduct-specific regional effects in the demandspecification, we estimate the price parametersbased on the within-product-group variation ofprices in each region. In an earlier version of thepaper, we also estimated the demand systemwithout regional dummies and obtained similarresults.

The second modification that allows us to gofrom a deterministic to a stochastic specificationof the demand equation is to include an additiveerror term in (7). The latter takes into accountthe fact that (7) is not expected to fit the dataexactly. The error term �irt accounts for mea-surement error (due to the fact that the productgroup prices pjrt we employ in the estimationare not exact price indices, but approximationsthereof) and (potentially region-specific) de-mand shocks that may affect the demand for aproduct in a particular period. Examples of suchshocks include an advertising campaign for aparticular product, which temporarily increasesthe demand for this product; or the outbreak ofa (potentially region-specific) epidemic, whichcalls for the use of a particular drug. We discussthe interpretation and properties of this errorterm in more detail in the next section.

The final form of the equation we estimate atthe lower level becomes (with subscript t de-noting month, and subscript r denoting region):

(8) �irt � �i � �ir � �iiln pirt � �i,10ln pjrt, j �Di10

� �j � Di

01

��i,01ln pjrt�

� �j � Di

00

��i,00ln pjrt�

� �iln�XQrt

PQrt� � �irt .

The analysis so far has conditioned on the ex-penditure allocated to the quinolone subseg-ment XQ. The upper level of the estimationconsiders the problem of allocating total expen-diture across the different systemic antibioticssubsegments, one of which is quinolones. Theupper-level demand function is given by:

(9) �G � �G � �H

�GHln PH � �Gln�X

P�,

where all variables denoted by capital letters aredefined as before, but now refer to subsegments(G, H, ...) rather than product groups within asubsegment, and the total expenditures on sys-temic antibiotics X are deflated by the Stone priceindex log P � ¥H �Hlog PH. When estimating thesystem above, we impose all the restrictions

18 Given the short time span of our sample, typicaldemand shifters, such as age distribution, income distribu-tion, and education, hardly change over our sample period.Such shifters are therefore absorbed by the region-specificproduct fixed effects �ir.


implied by utility maximization, as we do withthe estimation of the lower-level AIDS. We donot, however, impose any additional restrictionson the substitution patterns at this stage, so thatthe cross-price effects across segments remainrelatively unconstrained.

Estimation of the higher-level AIDS allowsus to obtain the unconditional own- and cross-price elasticities that are used in the formulationof the supply problem and welfare analysis.These are given by the formula

(10) �ij � �ij�XQ�XQ�

ln qi

ln XQ

ln XQ

ln PQ

ln PQ

ln pj,

with the conditional cross price elasticitiesgiven by

�ij�XQ�XQ� ��ij �i��j �jln�XQ

PQ�

�i.

As in the lower stage, we include subsegment-specific regional dummies in the specificationof the upper-stage demand system, so that thefinal form of the estimating equation at theupper stage becomes:

(11) �Grt � �G � �Gr � �H

�GHln PHrt

� �Gln�Xrt

Prt� � �Grt .

In sum, the demand system we take to thedata is represented by equations (8) and (11),and the associated parameter restrictions im-plied by economic theory.

B. Modelling the Supply Side of the Market

Counterfactual simulations concerning theeffects of domestic product withdrawal requireknowledge of the marginal costs of pharmaceu-tical firms operating in the Indian market. Theseare unobservable. The usual approach in theNew Empirical Industrial Organization litera-ture has been to exploit the firm equilibriumconditions to infer marginal cost. For example,it is often assumed that the marginal cost ci isconstant and that the industry is an oligopoly

engaging in Bertrand competition with differen-tiated products. Assuming that firms myopicallymaximize profits each period, one can then de-rive the firms’ first-order conditions that corre-spond to the assumptions above about costs,market structure, and firm behavior.

We deviate from this procedure, as the presenceof price regulation renders the assumption of un-constrained period-by-period maximization un-tenable. Ideally, one would like to explicitlyincorporate the price and other administrative con-trols into the firm’s optimization problem andderive the first-order conditions under the assump-tion of constrained maximization. The complexityof the price regulation, however, makes this ap-proach infeasible. Therefore, we adopt an alterna-tive approach that does not rely on modelling theprice-setting process, but is instead based on de-riving upper and lower bounds for marginal costsand markups.

In particular, an upper bound for marginalcosts and a lower bound for the markups (zero)can be derived under the assumption of perfectcompetition. While this assumption is clearlyunrealistic in the pharmaceuticals market, it isuseful in providing an upper bound for costs ci

U,which will be given by

(12) ciU � pi .

On the other hand, a lower bound for marginalcosts ci

L (and upper bound for markups) can bederived by assuming that there is perfect collu-sion within each product group i (where i refershere to a molecule/domestic-foreign combina-tion)19 and ignoring price controls, so thatprices are determined by the first-order condi-tion of the jointly profit-maximizing firmswithin product group i. Solving this first-ordercondition for the (lower bound of) marginal costthen gives

(13) ciL � pi � �1 �

1

�ii�pi,pj �� ,

19 The reason that this assumption will lead to an under-statement of marginal costs (and hence overstatement ofmarket power) is that it assumes away competition amongfirms within each drug group.


where �ii(pi,pj), the own-price elasticity of de-

mand for product i, will depend on the product’sown-price pi, and all other products’ prices pj,with i j.

Once we have obtained the demand elastic-ities through estimation of the demand sys-tem, we can calculate the upper and lowerbounds for marginal costs and correspondingmarkups according to (12) or (13). These willthen be employed in the counterfactual sim-ulations. While our counterfactuals use boththe lower and upper bounds for costs, most ofour discussion will be based on using thelower bounds for costs, since this is the moreinteresting case in the policy analysis: it givesus the largest possible profits for pharmaceuticalfirms, and hence corresponds to the worst pos-sible scenario facing Indian firms and the bestpossible scenario facing multinationals underTRIPS.

C. Identification Assumptions and EstimationApproach

The discussion of the demand system has sofar abstracted from the issue of price endogene-ity. The usual premise in the industrial organi-zation literature is that correlation of prices withthe error term in the demand equation arises byvirtue of the first-order conditions of profit-maximizing firms.20 As we discuss in this sec-tion, this source of simultaneity bias is unlikelyto be of major concern in the present contextbecause of the existence of price regulation. Ourprimary concern is, instead, with the simultane-ity bias that is implied by the particular wayprices are constructed.

To understand the sources of the simultaneitybias, note first that the price of product group piwe employ in the demand estimation should bethought of as a proxy for an exact price indexthat we do not observe. As such, pi contains, bydefinition, measurement error, and it will becorrelated with the error term of the demandequation. Specifically, the demand equation weare interested in estimating can be written insimplified form (suppressing the subscripts rand t, and ignoring parameter restrictions forconvenience) as

�i � �i � �j

�ijln pjT � �iln�XQ

PQ� � �i ,

where pjT denotes the true (exact) price index for

product group i, and �i captures unobservedvariables that may affect demand in a particularperiod (e.g., an advertising campaign or an ep-idemic). The price index pj

T depends on theSKU prices pk. Note that in the presence ofSKU entry and exit into the sample, the priceindex pj

T will vary over time, even if the pricesof the individual SKUs remain stable, as theproducts (SKUs) that comprise each productgroup change each period.

Ideally, we would like to compute the exactprice index pj

T for each group and employ theseindices in the demand estimation. Under theassumption of predetermined SKU prices eachperiod, the exact price indices would be uncor-related with the error term, and the demandparameters could be estimated by simple OLS.Unfortunately, this strategy is not feasible in thecurrent context.

To derive an exact price index for each prod-uct group, it is necessary to model consumer’schoice among SKUs conditional on the choiceof the group. To this end, it is necessary toexplicitly introduce a third stage in the demandspecification and estimate the parameters asso-ciated with the choice at that stage. However,any specification based on three-stage budget-ing (that would require directly specifying ademand function associated with the SKUchoice at the third stage and imposing additiveseparability at the higher stages) would be in-feasible to estimate for three main reasons.First, there is a large number of SKUs withinsome groups, so that the demand parameters can-not be estimated even if one imposes parameterrestrictions; second, the SKU prices exhibit littlevariation over time; lastly, the choice sets at thislowest stage vary over time, rendering estimationof the price parameters associated with SKUs thatare not available in all periods infeasible. Alterna-tively, one could abandon the multistage budget-ing altogether and adopt a discrete choiceapproach which is inherently better suited to deal-ing with varying choice sets and limited pricevariation. A discrete choice approach would beparticularly appealing for modeling the SKUchoice conditional on product group choice, giventhat within each group, all SKUs contain the same

20 See Steven Berry et al. (1995) or Aviv Nevo (2001)for a related discussion.


molecule and are hence comparable. As notedearlier, however, a discrete choice approach at thehigher stages of the demand estimation requiresconverting revenue to physical shares, which ischallenging in the case of systemic drugs, such asantibiotics, for which the appropriate dosage is notwell defined. Finally, a combination of a multi-stage budgeting approach for the higher stages anda discrete choice approach for the lowest stagewould be feasible to estimate, but inconsistentwith a model of utility maximization. For thesereasons, we have no alternative but to adopt anapproximation for measuring the price index foreach product group.

Let us denote this approximation by pjA, and let

j denote the proportional “approximation” errorassociated with measuring the true price index forgroup j, so that ln pj

A � ln pjT � ln j. The

estimating demand equation can then be written as

�i � �i � �j

�ijln pjA � �iln�XQ

PQ�

�j

�ijln j � �i

or

�i � �i � �j

�ijln pjA � �iln�XQ

PQ� � �i ,

where the new error term �i � ¥j �ijln j � �iis comprised of two components: the first onereflects “measurement” error due to the fact thatthe product group prices we employ are approx-imations and not exact price indices; and thesecond one captures conventional demandshocks. The demand equation as written abovecorresponds to the equation we take to the data.

The particular proxy we use for the exact priceindex for each product group j is the revenue-share-weighted average of the prices of multipleSKUs available within this group, that is:

pjA � �

k � j

�k pk .

The notation above illustrates the two source ofprice variation: variation in the SKU prices pk,and variation in the weights �k.

We are concerned with potential correlation ofthe so-constructed product group prices with bothcomponents of the product group demand error

term. The correlation between pjA and the first

component ¥j �ijln j is independent of theparticular way we construct pj

A, and inherent in thefact that we measure the exact price index witherror. The potential correlation between pj

A and thesecond component of the product group demanderror, the shock �i, arises, however, because of thespecific way we construct the proxy pj

A, and inparticular because of the presence of the reve-nue share weights �k in pj

A: since the SKUrevenue share weights �k will generally dependon the product group expenditure, they arelikely to be correlated with the product groupdemand shock �i.

To address the simultaneity bias we use in-strumental variables. To this end, we need vari-ables that are correlated with the proxies pj

A, butuncorrelated with the error term �i. We use thenumber of SKUs within each product group j asan instrument, as it does justice to the idea thatvariation in the product group price index stemsin part from variation in the set of SKUs that areavailable each period. It is clearly correlatedwith the average group prices pj

A, and plausiblyuncorrelated with the demand shock �i. Theassumption that the number of SKUs is uncor-related with the conventional demand error will,however, be violated if the introduction of anew SKU changes the perceived quality of adrug,21 or if it is accompanied by promotionalactivities which will be reflected in �i. Our hopeis that this is not too often the case. Assumingthat the SKU number for each group j is alsouncorrelated with the weighted sum of the mea-surement errors of all product groups ¥j �ijln j, itcan be used as an instrument for the product groupprice pj

A.22 If one accepts the premise that prices atthe SKU level are exogenous, then SKU pricescan also be used as instruments. The final list ofinstruments we use hence includes: the number ofSKUs in each group, the prices of the five largestSKUs for each group, and all other exogenousvariables in the demand estimation, such as re-gional/product dummy interactions and the upper-

21 Note that we already proxy for the quality of each prod-uct group through the product fixed effects �i. In the actualestimation, we actually let these fixed effects be region-specific(�ir), so that we allow quality to vary across regions.

22 If the measurement error increases in the number ofincluded SKUs, this assumption will be violated. Unfortu-nately, we have no way of checking the validity of thisassumption.


level total expenditure on antibiotics. Regressionsof group prices on the instruments above yieldhigh R-squares, with most regressors highly sig-nificant, indicating that our instruments are highlycorrelated with prices.23

Our sample includes four molecules: cipro-floxacin, norfloxacin, ofloxacin, and spar-floxacin. Except for sparfloxacin, all othermolecules are produced by both foreign anddomestic firms.24 So we have seven products(domestic ciprofloxacin, foreign ciprofloxa-cin, etc.), with 96 observations (two years ofmonthly data, four geographical regions foreach period) for each product. The parametersin the lower-level AIDS demand system asdefined in equation (8) are: the product fixedeffects �i , the product–regional dummy inter-actions �ir, the own revenue-share price elas-ticities �ii , the cross revenue-share priceelasticities �i,10, �i,01, �i,00, and the revenue-share expenditure elasticities �i. In estimatingthe parameters, we first regress prices on allinstrumental variables, and then plug the pre-dicted values for prices in the constrainedleast-square regression. (For a detailed expla-nation of the constraints, see the previoussection.)

Given that we impose many cross-equationconstraints and employ instrumental variablesin the estimation, it is difficult to derive stan-dard errors for the parameter estimates ana-lytically. Our error term interpretation in (8)implies that the error terms for each productgroup are likely to be correlated across re-gions; for example, if a national advertisingcampaign increases the demand for a rela-tively expensive presentation in one region,simultaneously increasing the aggregate de-mand and price index for the correspondingproduct group in that region, it is likely that thesame effect will be observed in other regions. Inprinciple, we could exploit the cross-regionalcorrelation in the error terms to estimate thedemand system using seemingly unrelated re-

gression (SUR). This, however, would requireus to derive the variance-covariance matrix ofthe parameters analytically, which, as notedabove, is cumbersome in the current context.Instead, we use the bootstrap method, choosingto remain agnostic about the structure of thevariance-covariance matrix. The potential dis-advantage of this approach over SUR is a loss inefficiency, but as will become apparent in theresults section, the model parameters are fairlyprecisely estimated. To maintain the marketstructure, we randomly sample the periods (withreplacement) and use the same periods for allproducts. Regarding the optimal number ofbootstrap repetitions, ideally one would followthe three-step method proposed by Donald W. K.Andrews et al. (2000). Empirical evidence sug-gests, however, that one rarely needs more than200 replications to estimate the standard er-rors.25 To be safe, we generate 300 bootstrapsamples (with replacement) based on the origi-nal data, and estimate the standard errors usingthe standard errors of the bootstrap sampleestimates.

The estimation of the top-level AIDS systemis similar. The constraints imposed on this top-level demand system are adding-up, homogene-ity, and symmetry, as defined on page 12.Again, bootstrapping is used to obtain the stan-dard errors of the parameter estimates.

D. The Counterfactual Scenarios

In assessing the effects of patent enforce-ment, we start by focusing on the most extremecase, in which compulsory licensing is not anoption, and foreign firms are not subject to pricecontrols. We use the results from the analysis ofthis case as a benchmark. In reality, the outcomeof the WTO negotiations is more likely to in-volve some constraints on the monopoly powerof foreign firms selling patented products indeveloping countries, such as price caps orcompulsory licensing. Our framework can eas-ily accommodate these cases, as will becomeapparent in the next subsection.

We now focus on the effects of potentialpatent enforcement in the quinolone segment.We consider several scenarios, which vary inthe number and size of domestic products that

23 Specifically the R-squares from the first-stage regres-sions of product group prices on instruments range from0.57 to 0.95, except for domestic ciprofloxacin, for whichthe R-square is 0.17.

24 Sparfloxacin is actually offered by one foreign subsid-iary in India (see Table 2). However, its revenue share isminiscule. We therefore treat Sparfloxacin as being pro-duced by domestic firms only. 25 See Bradley Efron and Robert J. Tibshirani (1993).


will be removed from the market. In particular,we consider the following five scenarios26:

● Withdrawal of one large domestic productgroup only: domestic ciprofloxacin;

● Withdrawal of one relatively small domesticproduct group only: domestic ofloxacin;

● Withdrawal of three domestic productgroups: domestic ciprofloxacin, ofloxacin,and norfloxacin;

● Withdrawal of three domestic productgroups: domestic ciprofloxacin, ofloxacin,and sparfloxacin;

● Withdrawal of all four domestic quinoloneproduct groups.

As the list above suggests, we proceed fromanalyzing the effects of single-product with-drawal to the analysis of eliminating the en-tire domestic segment. This approach wasmotivated by early empirical results that in-dicated that the existence and extent of com-petition from domestic firms has a significantbearing on the predicted effects of patent en-forcement; that is, our predictions regardingprices and welfare vary substantially, depend-ing on how many domestic products are af-fected by patent enforcement and on whethersome domestic competition will remain presentafter TRIPS. In addition, the size of the affecteddomestic groups is relevant for the welfare pre-dictions; accordingly, we examine both scenar-ios in which a large domestic product group,such as domestic ciprofloxacin, is eliminatedfrom the market, and scenarios in which the

eliminated domestic group is relatively small(e.g., domestic ofloxacin). In all scenarios, wemaintain the assumption that the set of prod-ucts offered by the remaining firms in themarket does not change in response to patentenforcement.

E. Computation of Virtual Prices and NewEquilibrium Prices

The first step in the counterfactual analysisis to derive the new equilibrium prices underpatent enforcement.27 In this context, thereare two sets of prices that are relevant. Thefirst set consists of the virtual prices of those(domestic) products that will not be availableonce TRIPS is put in effect. To calculate thesevirtual prices, we set the expenditures of therelevant products equal to zero. The secondset of prices consists of the prices of thoseproducts that remain in the market. In deriv-ing these prices, we start by assuming profitmaximization without any form of price reg-ulation: the firms remaining in the marketreoptimize in response to the policy change,and set new prices, taking the prices of allother firms as given.28 Of course, at the equi-librium, all prices change in response to thefact that some domestic products are nolonger present. The new equilibrium pricesfor products that remain in the market are thuscomputed by utilizing the first-order condi-tions of profit-maximizing firms, into whichthe virtual prices of the eliminated productsare substituted. Hence, to compute the newequilibrium prices, we solve an equation sys-tem of the following form:

26 As Table 2 indicates, most of the patents for the drugsin the quinolone segment have expired by now, so that noneof the scenarios described in this section is going to mate-rialize in practice. The reason we consider these alternativescenarios in our counterfactual simulations is to get a senseof how the presence and extent of domestic competitionaffects welfare calculations. Such calculations may becomerelevant in the future in other therapeutic classes in whichpatents have not expired yet, and where domestic firmscompete with foreign patent holders. While the particularexit scenarios will depend on the exact year of patent expiryof drugs in each therapeutic class and the specific way inwhich patents will be enforced (e.g., whether certain do-mestic drugs will be grandfathered in), the general conclu-sion that emerges from our calculations is that the consumerwelfare loss is substantially smaller in cases where somedomestic competition remains present in the market. Hence,the particular way in which patent enforcement is imple-mented is essential for assessing its welfare impact.

27 Of course, until product patents are in fact introduced,these prices will not be observable. Note also that we areassuming here that the range of available products will notchange with the introduction of patents.

28 As mentioned above, this first set of calculations ab-stracts from the existence of remaining price controls orother government regulations that would impose constraintson the firms’ profit maximization problem. Accordingly, theresulting numbers should be interpreted as a benchmark ofwhat would happen if markets were completely unregulated.To examine what the welfare effects of TRIPS would be inthe more realistic scenario of price regulation, we subse-quently consider a scenario in which regulation keeps theprices of the products provided by patent-holders at theirpre-TRIPS level. For a more detailed discussion, see theresults section.


● For products i that are withdrawn from themarket:

(14) 0 � �i � �ir � �iiln pirV � �i,10ln p�jr, j �Di

10

� �j � Di

01

��i,01ln p�jr�

� �j � Di

00

��i,00ln p�jr�

� �iln�X�Qr

P�Qr�.

● For products k that remain in the market:

(15) p�kr � ckr � �1 �1

�kk� p�kr,p�jr ,pirV ��1

.

In the equations above, pirV denotes the virtual

prices of the products that are removed fromthe market, while p�jr denotes the updatedprices of all other products. Note that, whensolving for the virtual prices, we account forthe fact that both the price index for quino-lones PQr, and the expenditure allocated tothis subsegment XQr, need to be updated toreflect the fact that as a result of the pricechanges there may be substitution away fromthis subsegment. To obtain the new quinoloneexpenditure X�Qr and the new price index P�Qr,we use the estimates and formulas for thehigher-level AIDS system. In equation (15),ck refers to the marginal cost for product k thatwe have obtained from the previous estimationstage. As mentioned in the previous subsection,we conducted the simulations using both theupper and lower bounds for marginal cost, ck

u

and ckL, respectively. The term �kk(p�kr

,p�jr,pirV) re-

fers to the unconditional own-price elasticity forproduct k, which is a function of the eliminatedproducts’ virtual prices and the remaining prod-ucts’ new equilibrium prices. We conduct thecounterfactual simulations and welfare analysisat the regional level because the presence ofregion-specific product effects in the demandestimation implies region-specific demand elas-ticities, and hence region-specific marginalcosts and markups. The presentation and discus-sion of our results focus on national averages ofthe relevant variables that we construct by com-

puting weighted averages across regions, usingthe population of each region as a weight.

F. Welfare Assessment

The simulation of the new equilibrium underpatent protection can provide important insightsinto how consumers and firms will respond tothe removal of domestic products in the market(for example, toward which products consumerswill substitute or which prices will increase themost). To get a more precise idea of how peo-ple’s well-being will ultimately be affected byTRIPS, we compute, as a last step in our anal-ysis, the welfare effects of the policy change.Social welfare is defined as consumer welfare,and the sum of domestic firm profits. Thechange in domestic profits can easily be calcu-lated by comparing the domestic firm (variable)profits at the pre-TRIPS prices to the profitsthese firms will realize at the new simulatedprices. Although foreign firm profits do notcount in domestic welfare calculations, we alsocompute the effects of patent enforcement onforeign firm profits, to get an idea of how largethe expected benefits of TRIPS are for thesefirms. This provides, in some sense, an indirectway of assessing whether the claims that patentenforcement in countries like India will lead tomore research on developing-country-specificdiseases (such as malaria) have any validity; if,for example, we find that the effect of patentenforcement on the foreign firm profits realizedin India is small in magnitude, it is unlikely thatforeign firms will engage in more developing-country-specific research in response to TRIPS.It is important to note that, in all these calcula-tions, we work with the lower-bound estimatesfor marginal costs, since these give rise to thehighest possible markups. Hence, our estimateof profit loss for domestic producers most likelyoverstates this loss.

On the consumer side, we measure changes inconsumer welfare by the compensating variation(CV), defined as the additional expenditure thatconsumers need in order to achieve the sameutility level as before patent enforcement at thenew prices. Specifically, let P0 denote the pricevector before patent enforcement, P� the simulatedprice vector post-TRIPS (that we obtained usingthe methods described in the previous subsection),u0 the utility attained by consumers before TRIPS,


and E(u, P) the higher-level expenditure function.Then the compensating variation is given by

(16) CV � E�u0, P�� E�u0, P0�.

Note that the CV as computed in (16) representsthe combination of three effects:

● The pure product-variety effect, that is, theeffect that arises because one or more prod-ucts are not available to consumers anymore,holding the prices of all other remainingproducts, and the total expenditure on thequinolone subsegment XQ, constant.

● The expenditure-switching effect, that is, theeffect arising from substitution away fromquinolones and toward other subsegments ofthe antibiotics market, again holding the pricesof all other remaining products constant.

● The reduced-competition effect, that is, theeffect that arises because the firms remainingin the market adjust (increase) their prices inresponse to the removal of domestic products.

From both an analytical and a policy point ofview, it is desirable to assess how large each ofthese effects is. Accordingly, we decompose thetotal effect on consumer welfare (the CV as givenby equation (16)), using the following procedure.

To get the pure product variety effect, wecompute virtual prices for the products that areremoved from the market, holding the quino-lone expenditure XQ and the prices of all otherproducts fixed. Let us call the resulting pricevector P1. Then, the pure product variety effectis represented by E(u0, P1) E(u0, P0).

To compute the expenditure-switching effect,we compute another set of virtual prices, againholding the prices of all remaining products fixed,but letting quinolone expenditure adjust in re-sponse to the new price index for the quinolonesegment. (Given that the prices of the remainingproducts remain fixed, the change in the priceindex arises only because of the removal of one ormore domestic products.) Note that this scenariomost closely resembles the case in which patentlaws are enforced, but strict price regulation keepsthe prices of the products offered in the market attheir pre-TRIP level. Let us label the so-computedprice vector P2. The expenditure-switching effectis then E(u0, P2) E(u0, P1).

Finally, the reduced competition effect aris-ing from higher prices for the remaining prod-

ucts is computed as the residual change in thecompensating variation once the product-varietyand expenditure-switching effects have been ac-counted for, that is, E(u0, P�) E(u0, P2),where the price vector P� is computed accordingto the formulas (14) and (15) to reflect theadjustment of prices to the new regime.

To compute the standard errors associatedwith the counterfactual simulations (that is, thestandard errors for the counterfactual prices andwelfare estimates), we again use bootstrapping:We first bootstrap the original sample (sales andprices of all the drugs) 300 times. Next, we esti-mate the AIDS model for each of these samples,and compute the counterfactual equilibrium pricesand welfare losses corresponding to each policyscenario for each of the 300 simulations. In thefinal step, we compute standard errors.29

One limitation of our framework is that itdoes not allow for a heterogeneous response ofconsumers to the policy change. Accordingly,our framework is not suited to addressing thequestion of how different groups in the popula-tion will be affected by patent law enforce-ment.30 Along the same lines, our frameworkdoes not accommodate the possibility of pricediscrimination, which might lead to differentresults regarding welfare losses and profit gainsrelative to uniform pricing.31 Still, we believethat the results of the counterfactual simulationscan provide important insights into the likelyaggregate response to patent enforcement andthe factors that drive this response.

III. Results

A. The Structure of Demand

Tables A2 and A3 in the Appendix display theresults from estimation of the lower- and upper-level AIDS systems, respectively. For ease of in-terpretation, rather than discussing the coefficientestimates, we focus our discussion on the implied

29 In cases some of the simulations do not converge, wecompute the standard errors using only those simulationsthat converged.

30 Addressing this question would require, at a mini-mum, micro data on consumer purchases, as in Goldberg(1995). To our knowledge, such data do not exist for theIndian pharmaceutical market.

31 See Ernst R. Berndt (1994) for an extensive discussionof uniform pricing.


unconditional price and expenditure elasticities re-ported in Table 6. Given that the region-specificproduct group effects, �ir, reported in the last fourcolumns of Tables A2 and A3 imply region-spe-cific demand elasticities, we report separate elas-ticities for each region in Tables 6A and 6B. Asevident from the comparison of these tables, theelasticities are very similar across regions, so thatwe can focus the remaining discussion on theelasticities of one region only, the northern region,displayed in Table 6A.

The diagonal terms of Table 6A report theown-price elasticities, which are, in all but onecase, negative and highly significant. The oneexception is the foreign norfloxacin productgroup—whose share of quinolone sales is 0.07percent—for which we estimate a negative butinsignificant own-price elasticity. For the remain-ing product groups, demand appears to be highlyelastic, with the estimated elasticities lower than2 in four out of the six cases. The magnitude ofthe own-price elasticities matches the features ofthe Indian pharmaceutical market mentioned ear-lier, which would suggest that Indian consumersare likely to be quite price-sensitive.32 The elas-ticities appear especially large if one takes intoaccount that they refer to product groups (such asdomestic ciprofloxacin) and not individual drugsoffered by particular firms. Their relative magni-tudes are also intuitive: the drug with the largestmarket share and a relatively high price (domesticciprofloxacin) appears to be one of the least elas-tic. In contrast, foreign ciprofloxacin is highlyprice elastic; this is plausible, as ciprofloxacindrugs offered by subsidiaries of multinationalsface the stiffest competition in this market seg-ment from approximately 75 Indian firms offeringthe same molecule.

The estimated expenditure elasticities appear inthe last column. These are all positive, indicatingthat the demand for all product groups is normal.The remaining cells display the estimated cross-price elasticities. As one might perhaps expect forproducts within a therapeutic subsegment, theseare mostly positive. Out of a total of 49 priceelasticities we estimate, there are six that do notconform to expectations; these are the cross-price elasticities between different foreign prod-

uct groups, which are estimated to be negativeand significant.33 Fortunately, these elasticitieshave negligible impact on the welfare analysis:given that our counterfactuals focus on theeffect of withdrawing one or more domesticproducts from the market, the most relevantelasticities are the ones that capture the responseof various product group shares to a change in theprice of one or more domestic groups; these elas-ticities are the cross-price elasticities between var-ious domestic groups, and the ones betweendomestic and foreign groups, which are plausibleand precisely estimated.34

Regarding these elasticities, a striking aspectof our estimates is how large, positive, andsignificant the cross-price elasticities betweendifferent domestic product groups are—in fact,for norfloxacin and ofloxacin, we estimate thatdomestic product groups containing differentmolecules are closer substitutes for one anotherthan product groups that contain the same mol-ecule but are produced by foreign firms. Incontrast, for ciprofloxacin (the molecule withthe largest revenue share), we estimate a largepositive cross-price elasticity between the do-mestic and foreign versions.

The fact that domestic products appear to beclose substitutes for other domestic productsthat contain different molecules truly representsan “empirical” finding in the sense that we do

32 In developed economies, elasticities of this magnitude havetypically been found only for generic drugs (and even then, onlyrarely) or among consumers who lack health insurance.

33 While these elasticities are clearly counterintuitive, theyare not inconsistent with the underlying demand system, whichimposes no restrictions on the sign of the cross-price elastici-ties. We do not have a good explanation of why these elastic-ities are estimated to be negative. A potential explanation isthat the shares of the foreign products are very small; giventhis, we observe very few consumers switching from one (verysmall) foreign group to another (very small) foreign group,when the price of the first foreign group goes up, and hence theinference is not very reliable in this case.

34 The cross-price elasticities between foreign drug groupscontaining different molecules will also have an effect on thewelfare estimates, given that the withdrawal of domestic prod-ucts will generally lead to changes in the prices of all foreignproducts, and market shares will be reallocated from someforeign products to other foreign products based on the newprices. However, this reallocation from “foreign to foreign” istruly second order in our case, compared to the reallocationfrom “domestic to domestic” and “domestic to foreign” prod-ucts. In scenarios in which we consider patent enforcementaccompanied by strict price regulation, the cross-price elastic-ities between foreign product groups are in fact completelyirrelevant, as the prices of foreign products are not allowed toincrease in these cases. Still, our welfare loss estimates remainsubstantial, driven—as before—by the loss of product variety.


not impose it through any of our assumptionsregarding the demand function. The questionthat naturally arises, then, is what might explainthis finding. While we cannot formally addressthis question, anecdotal accounts in various in-dustry studies suggest that the explanation maylie in the differences between domestic andforeign firms in the structure and coverage ofretail distribution networks.

Distribution networks for pharmaceuticals inIndia are typically organized in a hierarchicalfashion. Pharmaceutical companies deal mainlywith carrying and forwarding (C&F) agents, inmany instances regionally based, who each sup-ply a network of stockists (wholesalers). Thesestockists, in turn, deal with the retail pharma-cists through whom retail sales ultimately oc-cur.35 The market share enjoyed by a particularpharmaceutical product therefore depends inpart on the number of retail pharmacists who

stock the product. And it is here that thereappears to be a distinction between domesticfirms and multinational subsidiaries. In particu-lar, the retail reach of domestic firms, as agroup, tends to be much more comprehensivethan that of multinational subsidiaries (IndianCredit Rating Agency (ICRA), 1999).36

There appear to be two reasons for this. Thefirst is that many of the larger Indian firms,because they have a much larger portfolio ofproducts over which to spread the associatedfixed costs, typically have more extensive net-works of medical representatives. The second issimply that there are many more domestic firms(and products) on the market. At the retail level,this would imply that local pharmacists mightbe more likely to stock domestic products con-taining two different molecules, say ciprofloxa-cin and norfloxacin, than they would domesticand foreign versions of the same molecule. Tothe extent that patients (or their doctors) arewilling to substitute across molecules in order tosave on transport or search costs (e.g., going toanother pharmacy to check whether a particular

35 There are estimated to be some 300,000 retail pharma-cists in India. On average, stockists deal with about 75 retailers(ICRA, 1999). There are naturally variations in this structure,and a host of specific exclusive dealing and other arrangementsexists in practice. Pharmaceutical firms also maintain networksof medical representatives whose main function is to marketthe company’s products to doctors who do the actual prescrib-ing of drugs. In some instances, firms do sell directly to thedoctors who then become the “retailer” as far as patients areconcerned, but these are relatively rare.

36 These differences were also highlighted in conversa-tions one of the authors had with CEOs and managingdirectors of several pharmaceutical firms, as part of a sep-arate study.

TABLE 6A—DEMAND PATTERNS WITHIN THE QUINOLONES SUBSEGMENT:UNCONDITIONAL PRICE AND EXPENDITURE ELASTICITIES IN THE NORTHERN REGION

Product group

Elasticity with respect to:

Prices of foreign productgroups Prices of domestic product groups Overall

quinolonesexpenditureCipro Norflo Oflo Cipro Norflo Oflo Sparflo

Foreign ciprofloxacin 5.57* 0.13† 0.15* 4.01* 0.11† 0.11† 0.16* 1.37*(1.79) (0.07) (0.07) (1.84) (0.06) (0.06) (0.06) (0.29)

Foreign norfloxacin 4.27† 0.45 4.27† 3.50† 6.02 4.51* 4.65* 2.20*(2.42) (1.12) (2.42) (2.10) (6.23) (1.84) (1.83) (1.05)

Foreign ofloxacin 0.11* 0.10† 1.38* 0.09 0.09† 0.23 0.11* 1.16*(0.05) (0.05) (0.31) (0.27) (0.05) (0.28) (0.04) (0.17)

Domestic ciprofloxacin 0.18* 0.01* 0.01 1.68* 0.08* 0.08* 0.10* 1.17*(0.08) (0.00) (0.01) (0.23) (0.02) (0.02) (0.02) (0.03)

Domestic norfloxacin 0.04* 0.03 0.04* 0.58* 2.23* 0.42* 0.40* 0.73*(0.01) (0.03) (0.01) (0.17) (0.11) (0.04) (0.03) (0.09)

Domestic ofloxacin 0.05* 0.05* 0.11 0.77* 0.74* 3.42* 0.74* 0.89*(0.02) (0.02) (0.13) (0.28) (0.08) (0.25) (0.08) (0.21)

Domestic sparfloxacin 0.07* 0.04* 0.07* 1.15* 0.63* 0.63* 2.88* 0.28*(0.02) (0.01) (0.02) (0.15) (0.06) (0.06) (0.17) (0.12)

Notes: Standard errors in parentheses. Elasticities evaluated at average revenue shares. Asterisk (*) denotes significance at the5-percent significance level, and dagger (†) denotes significance at the 10-percent level.


TABLE 6B—DEMAND PATTERNS WITHIN THE QUINOLONES SUBSEGMENT:UNCONDITIONAL PRICE AND EXPENDITURE ELASTICITIES IN OTHER REGIONS

Demand patterns in the eastern region

Product group


Foreign groups’ prices Domestic groups’ pricesQuinolonesexpenditureCipro Norflo Oflo Cipro Norflo Oflo Sparflo

Foreign ciprofloxacin 5.94* 0.14† 0.16* 4.31* 0.13* 0.11 0.17* 1.40*(1.95) (0.08) (0.08) (2.01) (0.06) (0.07) (0.07) (0.31)

Foreign norfloxacin 3.29† 0.58 3.29† 2.64† 4.60 3.46* 3.59* 1.92*(1.80) (0.83) (1.79) (1.53) (4.51) (1.38) (1.39) (0.82)

Foreign ofloxacin 0.12* 0.10† 1.40* 0.10 0.10* 0.24 0.13* 1.17*(0.06) (0.06) (0.34) (0.31) (0.05) (0.30) (0.05) (0.19)

Domestic ciprofloxacin 0.18* 0.01* 0.01 1.72* 0.09* 0.08* 0.11* 1.17*(0.08) (0.00) (0.02) (0.27) (0.02) (0.03) (0.02) (0.03)

Domestic norfloxacin 0.04* 0.04 0.04* 0.68* 2.42* 0.49* 0.46* 0.70*(0.02) (0.04) (0.02) (0.20) (0.13) (0.05) (0.04) (0.10)

Domestic ofloxacin 0.04† 0.04* 0.09 0.61* 0.60* 2.95* 0.60* 0.92*(0.02) (0.01) (0.10) (0.28) (0.07) (0.22) (0.07) (0.17)

Domestic sparfloxacin 0.05* 0.03* 0.05* 0.92* 0.48* 0.51* 2.51* 0.43*(0.02) (0.01) (0.01) (0.17) (0.05) (0.06) (0.16) (0.10)

Demand patterns in the western region

Product group




Foreign norfloxacin 7.14† 0.08 7.10† 5.94† 9.95 7.42* 7.70* 2.99†

(3.69) (1.83) (3.68) (3.28) (10.15) (2.97) (2.96) (1.78)Foreign ofloxacin 0.13* 0.12† 1.45* 0.09 0.12* 0.26 0.13* 1.19*

(0.07) (0.06) (0.37) (0.29) (0.05) (0.33) (0.05) (0.21)Domestic ciprofloxacin 0.19* 0.01* 0.00 1.74* 0.09* 0.08* 0.11* 1.18*

(0.08) (0.00) (0.01) (0.24) (0.02) (0.03) (0.02) (0.04)Domestic norfloxacin 0.04* 0.04 0.04* 0.67* 2.43* 0.50* 0.47* 0.69*

(0.02) (0.04) (0.01) (0.18) (0.14) (0.05) (0.04) (0.10)Domestic ofloxacin 0.03 0.03* 0.07 0.48* 0.48* 2.57* 0.48* 0.93*

(0.02) (0.01) (0.08) (0.22) (0.05) (0.15) (0.04) (0.14)Domestic sparfloxacin 0.06* 0.03* 0.05* 0.83* 0.46* 0.49* 2.41* 0.46*

(0.02) (0.01) (0.01) (0.14) (0.04) (0.05) (0.12) (0.09)

Demand patterns in the southern region

Product group




Foreign norfloxacin 5.33† 0.31 5.31† 4.31† 7.48 5.59* 5.84* 2.49†

(3.01) (1.34) (3.00) (2.45) (7.34) (2.19) (2.19) (1.28)Foreign ofloxacin 0.14* 0.12† 1.48* 0.11 0.12* 0.29 0.16* 1.20*

(0.07) (0.07) (0.39) (0.33) (0.06) (0.35) (0.06) (0.22)Domestic ciprofloxacin 0.17* 0.01* 0.00 1.69* 0.08* 0.07* 0.11* 1.16*

(0.08) (0.00) (0.01) (0.25) (0.02) (0.03) (0.02) (0.03)Domestic norfloxacin 0.04* 0.03 0.03* 0.59* 2.25* 0.43* 0.40* 0.73*

(0.01) (0.03) (0.01) (0.19) (0.12) (0.04) (0.04) (0.09)Domestic ofloxacin 0.04† 0.04* 0.09 0.62* 0.60* 2.96* 0.60* 0.91*

(0.02) (0.01) (0.10) (0.26) (0.06) (0.19) (0.07) (0.17)Domestic sparfloxacin 0.08* 0.05* 0.08* 1.38* 0.73* 0.75* 3.23* 0.16

(0.02) (0.01) (0.02) (0.18) (0.07) (0.08) (0.21) (0.14)

Notes: Standard errors in parentheses. Elasticities evaluated at average revenue shares. Asterisk (*) denotes significance at 5-percent level, anddagger (†) denotes significance at the 10-percent level.


foreign product is in stock), in aggregate datawe would expect to find precisely the substitu-tion patterns that we report in Table 6.

Whether the particular explanation we provideabove is the correct one, the high degree of sub-stitutability between domestic product groupsturns out to have important implications for thewelfare calculations. We discuss these in moredetail below when we present the results of thecounterfactual welfare analysis. Another elasticitywith important implications for the counterfactu-als is the price elasticity for the quinolone subseg-ment as a whole, which indicates how likelyconsumers are to switch to other antibioticsgroups, when faced with a price increase for quin-olones. This elasticity is computed on the basis ofthe results in Table A3, and it is at 1.11 (stan-dard error: 0.24); this is large in magnitude,but—as expected—smaller in absolute value thanthe own-price elasticities of the product groupswithin the quinolone subsegment.

The results in Tables 6A and 6B are based onour preferred specification discussed in SectionII. In Tables A4 to A6 in the Appendix, weexperiment with some alternative specifications.Tables A4(a)–A4(c) correspond to a specificationthat includes, in addition to product-group-specificregional fixed effects, product-group-specific (andfor the upper level antibiotics-segment-specific)seasonal effects. We distinguish among three sea-sons—the summer, monsoon, and winter—andreport the unconditional demand elasticities for

the northern region for each of these seasons. Asevident from the tables in the Appendix, our elas-ticity estimates are robust to the inclusion of sea-sonal effects. The demand elasticities in Table A5are based on estimation of the demand system byOLS. Compared to the elasticities obtained by IV,the OLS elasticities are smaller in absolute value,implying that welfare calculations based on theOLS estimates would produce larger welfare lossestimates. Nevertheless, some of the patterns re-garding the cross-price elasticities discussed ear-lier are also evident in the OLS results; inparticular, the cross-price elasticities between dif-ferent domestic product groups are all positive,large, and significant, and in most instances largerthan the cross-price elasticities between drugs thatcontain the same molecule but are produced byfirms of different domestic/foreign status. Theclose substitutability of domestic products indi-cated by both the OLS and IV estimates seems tobe one of the most robust findings of the paper.

B. Cost and Markup Estimates

Table 7 displays the marginal costs, markups,and profits implied by the price elasticity esti-mates of Tables 6A and 6B for each of the sevenproduct groups. Given that our regional effectsimply different price elasticities for each region,our marginal cost and markup estimates alsodiffer by region. Given, however, that based on

TABLE 7—UPPER AND LOWER BOUNDS FOR MARGINAL COST, MARKUP, AND ANNUAL PROFIT BY PRODUCT GROUPS WITHIN

THE QUINOLONE SUBSEGMENT

Product groupLower boundfor MC (Rs.)

Upper boundfor markup

Upper boundfor profit(Rs. mill)

Upper boundfor MC (Rs.)

Lower boundfor markup

Lower boundfor profit

(Rs.)

Foreign ciprofloxacin 8.3* 19% 26.9 10.3 0% 0.0(1.23) (0.12) (16.55)

Foreign norfloxacin NA NA NA 5.3 0% 0.0Foreign ofloxacin 32.3 70%* 106.1* 108.5 0% 0.0

(23.16) (0.21) (31.85)Domestic ciprofloxacin 4.7* 59%* 1,701.9* 11.2 0% 0.0

(1.14) (0.10) (298.58)Domestic norfloxacin 5.2* 43%* 280.7* 9.0 0% 0.0

(0.20) (0.02) (15.32)Domestic ofloxacin 58.7* 34%* 161.2* 90.1 0% 0.0

(2.18) (0.02) (12.80)Domestic sparfloxacin 49.5* 37%* 198.5* 78.8 0% 0.0

(1.57) (0.02) (11.00)

Notes: Standard errors in parentheses. Asterisk (*) denotes significance at the 5-percent level. Estimated lower bound forforeign norfloxacin’s marginal cost is negative, since the estimated price elasticity is less than one in absolute value.


Tables 6A and 6B the price elasticities do notseem to differ substantially across regions, wereport for ease of exposition only the nationalaverages for marginal costs and markups inTable 7.

Table 7 has two parts. In the left part (firstthree columns) we report the lower bound formarginal cost and the corresponding upperbound for markup, and upper bound for totalannual profit for each product group. Thesenumbers are based on the price elasticities weobtained from estimating the two-level AIDSdemand system. Since we do not have a reliableestimate for the price elasticity of foreign nor-floxacin (the point estimate is negative, but lessthan one in absolute value, and insignificant),we cannot compute the lower bound for mar-ginal cost in this case. It is important to note thatthese estimates do not reflect either the actualmarginal cost or the actual markup for thesedrugs, both because the existence of price reg-ulation implies that the unconstrained first-orderconditions are unlikely to hold each period, andbecause our aggregation across firms of thesame domestic/foreign status supplying thesame molecule makes the interpretation of theseestimates problematic. In particular, the fact thatwe ignore competition among firms within eachproduct group implies that our estimates willtend to overstate market power. These numberswill prove useful, however, in the counterfac-tual simulations, as they can give us a sense ofhow large the maximum profit gains for multi-national firms and the maximum profit lossesfor domestic firms are likely to be under patentenforcement. The right part of the table (lastthree columns) reports the upper bound for mar-ginal cost, which we obtain by simply taking themarginal cost to be equal to the observed price.We do not report standard errors in this case,since the numbers are based on actual data. Themarkups corresponding to these marginal costupper bounds are, of course, zero. We conductthe counterfactual simulations using both thelower and upper bounds for marginal costs.

The (upper bounds for) markups on the leftside of the table are generally plausible. Thedomestic ciprofloxacin product group that dom-inates the quinolone subsegment, and for whichwe documented high prices, a high marketshare, and a relatively low elasticity of demand,enjoys one of the highest markup upper bounds(60 percent) and accounts for nearly 70 percent

of all profits derived within the subsegment.Foreign ciprofloxacin, on the other hand, whichfaces the stiffest competition from domesticfirms and for which we estimated a highly elas-tic demand, has the lowest markup upper bound(19 percent).

C. Counterfactual Estimates of the Impact onPrices and Welfare

With estimates of the key demand and costparameters in hand, we turn to the counterfac-tuals. We consider the five separate scenarioslisted in the previous section. All of the scenar-ios involve the withdrawal of one or more of thedomestic product groups from the market. Ta-ble 8 displays our estimates of the consumerwelfare losses that result under the differentscenarios. The losses are expressed in billion ofrupees per year. All numbers presented in Ta-ble 8 and subsequent tables are based on usingthe lower bounds for marginal cost and upperbounds for markup in the simulations. As dis-cussed earlier, these numbers are the more in-teresting to work with, since they give us anupper bound for the changes in the profits ofdomestic and foreign firms that would resultfrom patent enforcement. In the Appendix, wealso present results based on using the upperbounds for marginal costs in the simulations, inwhich case the pre-TRIPS profits of domesticand foreign firms are zero. In all cases, marginalcosts are assumed to be constant in output.While naturally the profit implications differdepending on whether one uses the upper orlower bounds for marginal costs (firm profits arezero if one assumes the upper bound of mar-ginal cost, in which case price equals marginalcost), the estimated consumer welfare losses aresimilar in the two cases. We discuss these re-sults in more detail at the end of this section.

The first column presents our estimates of theconsumer welfare losses attributable to the pureloss of product variety effect, where we fix theprices of all remaining products as well as theoverall expenditure on quinolones, while with-drawing one or more of the domestic productgroups. Note that, in our initial specification ofthe demand system, had we not allowed for thepossibility that consumers might differentiatebetween domestic and foreign products evenwhen they contain the same molecule, this


particular component of the loss of consumerwelfare would not have arisen.

The estimates reported in the second columnincorporate the expenditure-switching effect ontop of the loss of product variety. Here, basedupon the price elasticity estimates from thehigher-level AIDS system, we adjust (down-ward) the expenditures allocated to the quino-lone subsegment as the composite price ofquinolones effectively increases as a consequenceof the higher virtual prices of the domestic productgroups that are withdrawn from the market. Be-cause the estimates in this column are generatedassuming that the prices of the products that re-main in the market are not adjusted upward, theyprovide a sense of what consumer losses would beif the introduction of product patents were coupledwith strict price regulation aimed at maintainingprices at prepatent levels. Alternatively, they canbe thought of as the relevant welfare numbers ifintense competition among firms within the re-maining product groups kept the prices of theproducts that were still offered in the market closeto the firms’ marginal costs.

The last column of Table 8 displays the esti-mated consumer welfare losses when bothcross-segment expenditure-switching and with-in-segment upward price adjustments are takeninto account.

If we compare the results across the first,second, and third columns, all the counterfac-tual scenarios produce qualitatively similar pat-terns, patterns that are consistent with what we

would expect. Starting from the initial loss ofwelfare attributable to the loss of product vari-ety, the option of switching expenditures outof the quinolone subsegment to other subseg-ments mitigates some of the initial welfareloss. But if we then incorporate the upwardprice adjustments that result in response to thereduced competition, the welfare losses aremagnified.

Of particular interest from a policy perspec-tive are the relative magnitudes of these threeeffects, which are similar under all the counter-factual scenarios, though absolute levels varyconsiderably. First, despite the fact that the de-mand for quinolones is quite sensitive to thecomposite price of quinolones—the upper levelprice elasticity is 1.11—the cross-subsegmentexpenditure-switching effects are, in all thecases, small (in absolute value terms) relative tothe other two effects. For instance, under thescenario where all the domestic quinolone prod-uct groups are withdrawn from the market, theoverall consumer welfare loss of Rs. 17.81 bil-lion per year can be decomposed into an initialloss of Rs. 11.76 billion (66 percent) attribut-able to the loss of product variety, a slightreduction in this initial loss of Rs. 0.41 billion(2 percent), from Rs. 11.76 billion to Rs.11.35 billion, because of expenditure switching,and a subsequent additional loss of Rs. 6.46billion (36 percent), from Rs. 11.35 billion toRs. 17.81 billion, because of the reduced com-petition and consequent price increases.

TABLE 8—COUNTERFACTUAL ESTIMATES OF CONSUMER WELFARE LOSSES FROM PRODUCT WITHDRAWAL DUE TO THE

INTRODUCTION OF PHARMACEUTICAL PATENTS (RS. BILL PER YEAR)

Counterfactual scenarios: withdrawal ofone or more domestic product groups

Pure loss ofvariety

Loss of variety and:

Cross-segmentexpenditureswitching

Within-segment price-adjustmentand cross-segment expenditure

switching

Only ciprofloxacin 4.98* 4.92* 7.32*(0.87) (0.89) (1.46)

Only ofloxacin 0.08 0.08 0.23*(0.08) (0.08) (0.10)

Ciprofloxacin, ofloxacin, and norfloxacin 7.52* 7.40* 12.53*(1.77) (1.80) (4.15)

Ciprofloxacin, ofloxacin, and sparfloxacin 6.14* 6.03* 10.58*(1.42) (1.45) (3.31)

All four domestic quinolones products 11.76† 11.35† 17.81(6.43) (6.34) (12.70)

Notes: Standard errors in parentheses. Asterisk (*) denotes significance at the 5-percent significance level, and dagger (†)denotes significance at the 10-percent level.


The basic claim made by proponents ofTRIPS is that any adverse impacts on consumerwelfare from the introduction of a product patentin a particular market will be mitigated by theavailability of close therapeutic substitutes. Therelatively minor role that cross-subsegment ex-penditure switching appears to play suggests thatfor this claim to be valid, there need to be unpat-ented (i.e., patent-expired) substitutes availablewithin fairly narrowly defined therapeutic catego-ries. Since the extent to which this is true will varyacross therapeutic segments, the impact of TRIPSis likely to be correspondingly variegated, a pointemphasized by Maskus (2000, p. 163).

Price regulation and compulsory licensing aretwo of the most widely mentioned post-TRIPSpolicy options available to governments of devel-oping economies. There is an ongoing debateabout how much leeway governments shouldhave to introduce these options, and about therelative efficacy of the two options in limitingprice increases. The magnitude and importance ofthe welfare losses we estimate from the loss ofproduct variety suggest that there may be an in-dependent role for compulsory licensing, in addi-tion to or in lieu of price regulation, for the solepurpose of mitigating the loss of product variety.

Turning next to a comparison of the consumerwelfare losses under the different scenarios, themost striking result is that the estimated loss ofconsumer welfare (Rs. 17.81 billion) from thesimultaneous withdrawal of all four domesticproduct groups—the scenario that most closelyresembles what is likely to happen underTRIPS—is more than two times the sum of theestimated losses from the four separate scenariosin each of which only one of the domestic productgroups is withdrawn.37 What this very clearlyindicates is that past studies that have estimatedthe aggregate effects of patent protection by add-ing up the losses, estimated separately, in each ofa number of patentable markets may have sub-stantially underestimated the magnitude of the

consumer welfare losses from the introduction ofpharmaceutical product patents.

The result that the simultaneous withdrawal ofall domestic products magnifies the scale of thewelfare losses is driven by our estimates of high,positive cross-price elasticities between domesticproducts. As noted earlier, these elasticities implythat such products are close substitutes to oneanother. Hence, when all four domestic productsdisappear from the market, the resulting consumerloss is substantial. In contrast, the welfare lossesassociated with the withdrawal of a single domes-tic product or a subset of domestic products aremore modest; with domestic product groupswithin the quinolone subsegment being relativelygood substitutes, if only one of them is withdrawn,consumers switch to the others, and this limits anywelfare losses.

We should note that if, as we speculated above,the high degree of substitutability between domes-tic products stems in part from the differentialreach of the distribution networks of domestic andforeign firms, these estimates may overstate thewelfare loss from the simultaneous withdrawal ofall domestic products. That is because, with Indiabecoming TRIPS compliant, foreign subsidiariesmay well choose to expand their product portfo-lios in India and simultaneously expand their dis-tribution networks in India, most likely throughjoint marketing ventures with Indian firms. Mediaaccounts and interviews with industry sources in-dicate that such initiatives are increasing in num-ber. In this case, the welfare loss from thereduction in variety would be a purely transitionalphenomenon. Over time, foreign products wouldbe more readily available in local pharmaciesthroughout India, and this would compensate forthe reduction in the number of domestic products.Alternatively, if Indian consumers insist on buy-ing products produced by Indian firms, foreignmultinationals could use licensing to recover thewelfare loss associated with the loss of variety.Note, however, that even under this scenario, thecomponent of consumer welfare loss due to up-ward price adjustment remains. And a crude cal-culation based on the estimates in the last row ofTable 8 suggests that this is likely to be significant.In particular, if we subtract from our estimate ofthe overall consumer welfare loss (Rs. 17.81 bil-lion), the component attributable to the reductionin variety taking into account expenditure switch-ing (Rs. 11.35 billion), we are still left with anestimated welfare loss of Rs. 6.46 billion. Given

37 For ease of exposition, Table 8 reports only a subset ofthe scenarios we have investigated. The consumer welfareloss associated with the withdrawal of domestic norfloxacinonly (not reported in Table 8) is approximately Rs. 0.1billion; the welfare loss associated with the withdrawal ofsparfloxacin only is close to zero. Hence, the sum of theestimated welfare losses from the scenarios in which onlyone domestic product is withdrawn is (7.32 � 0.23 � 0.1 �0) � Rs. 7.65 billion per year.


the size of the welfare loss due to upward priceadjustment, policymakers may be tempted to con-tinue the use of price controls and other domesticregulations. Such policies, however, would put alimit not only on prices, but also on the incentivesof foreign producers to expand their operations inthe Indian market, so that the welfare loss due tothe reduction of product variety could become apermanent phenomenon.

Table 9 documents our estimates of the priceincreases that would result under the variouscounterfactual scenarios. The table reports theprice increases for the product groups, foreignor domestic, that would remain in the marketunder each of our scenarios. The groups that arewithdrawn from the market are indicated by theshaded areas. For the foreign products thatwould remain in the market, we estimate priceincreases between 100 percent and 400 percent.While these numbers are based on simulations,and thus not observed, we can obtain a roughidea about their plausibility by comparing themto the prices of the same products observed incountries “similar” to India in terms of demo-graphics, but which have less competitive do-mestic markets. Pakistan is a natural candidate.For the drug ciprofloxacin, for example, wepredict that the price of the (patented) foreignproducts in India would be approximately fivetimes higher than it is now (see first column ofTable 9, last row; the relevant scenario here is

one where all domestic products are withdrawnfrom the market, since this is the situation thatmost closely resembles that of Pakistan). Lan-jouw (1998, p. 39, Table 2) reports that the priceof ciprofloxacin in Pakistan is about seven timesthe price of the same drug in India. The twonumbers are of similar order of magnitude, al-though our estimate is on the low side. Thesecomparisons give us confidence that the empir-ical framework we use as a basis for conductingcounterfactual simulations in India captures themain features of this market.

Table 10 presents our estimates of the netimpact of the withdrawal of one or more do-mestic product groups on the collective profitsof domestic Indian firms in the quinolone sub-segment. Under the scenario where all the do-mestic product groups are withdrawn from themarket, the net impact equals the gross impactand is simply the loss of the profits initiallyenjoyed by domestic firms: Rs. 2.34 billion peryear. In the other cases, the foregone profitsof those domestic firms whose products arewithdrawn from the market are partly orwholly offset by the increased profits of thosedomestic firms that remain in the market andbenefit from the reduced competition. FromTable 10 it can be seen that this latter resultarises when domestic ofloxacin is withdrawnfrom the market, in which case consumersswitch to other domestic drugs within quino-

TABLE 9—COUNTERFACTUAL ESTIMATES OF DRUG PRICE CHANGES AFTER PRODUCT WITHDRAWAL DUE TO INTRODUCTION OF

PHARMACEUTICAL PATENTS


Changes in prices with cross-segment expenditure switching andwithin-segment price adjustment (% of original prices)

Foreign product groups Domestic product groups

Cipro Norflo Oflo Cipro Norflo Oflo Spar

Only ciprofloxacin 189.4%* 314.7% 98.2%* � 148.6%* 141.4%* 164.1%*(0.18) NA (0.21) (0.09) (0.08) (0.08)

Only ofloxacin 100.4%* 314.7% 102.9%* 102.4%* 108.3%* � 110.7%*(0.01) NA (0.08) (0.01) (0.01) (0.01)

Ciprofloxacin, ofloxacin, and norfloxacin 247.6%* 627.8% 154.4%* � � � 296.3%*(0.42) NA (0.39) (0.70)

Ciprofloxacin, ofloxacin, and sparfloxacin 255.2%* 627.8% 158.3%* � 250.1%* � �(0.40) NA (0.37) (0.64)

All four domestic quinolones products 396.4% 627.8% 318.4%† � � � �(3.34) NA (1.73)

Notes: Standard errors in parentheses. Asterisk (*) denotes significance at the 5-percent level, and dagger (†) denotessignificance at the 10-percent level. We fix foreign norflo’s price at the numbers shown, because an estimate for the marginalcost lower bound is not available for this drug. We tried many different values for foreign norflo’s counterfactual prices, andthe results are remarkably robust.


lones, increasing the profits of the domesticfirms selling those drugs.38

Critics of the Indian government’s stance onTRIPS frequently assert that it is motivated lessby concerns about consumer welfare than it isby a desire to protect the domestic pharmaceu-tical industry. Whether or not that is the case,the estimates presented in Table 10 indicate thatthe loss of domestic producer surplus is unlikelyto be the biggest consequence of TRIPS-induced patent protection. First, as just men-tioned, there are scenarios under which thecollective profits of domestic firms would actu-ally go up, though there always is a segmentthat would be adversely affected.39 Second,even when collective profits go down, a com-parison with Table 8 indicates that the loss ofconsumer welfare is much greater in every in-stance. And under the scenario where the col-

lective loss of profits is the greatest and thereare no winners among the domestic Indianfirms, the loss incurred by producers—Rs. 2.3billion on an annualized basis—pales in com-parison to the decrease in consumer welfarereported in Table 8 under the same scenario—Rs. 17.81 billion annually—especially if onetakes into account that the profit loss of Rs. 2.3billion was derived by using an upper bound forthe domestic firms’ Pre-TRIPS markups, andhence clearly overstates the profit loss that thedomestic sector would actually incur.40

Table A6 in the Appendix reports resultsfrom counterfactual simulations in which weused the upper bound for marginal costs. Thepre-TRIPS domestic firm profits are of coursezero in this case, since the upper bound wasderived by setting marginal cost equal to price.Interestingly, the results in Table A6 that reportthe loss in consumer welfare, indicate that theconsumer loss would be slightly higher thanbefore. This is due to the fact that because of thehigher marginal cost estimates, the price in-creases are now higher compared to the casewhere the lower bound of marginal cost wasused. The new consumer welfare loss estimateis, however, roughly of the same order of

38 To be consistent with Table 8, which reported con-sumer welfare losses as positive numbers, Table 10 reportsforegone profits as positive numbers. Thus, if the collectiveprofits of domestic Indian firms actually increase, negativenumbers are reported.

39 This may in part explain why the Indian pharmaceu-tical industry has been divided in its reaction to TRIPS. TheOrganization of Pharmaceutical Producers of India, whichincludes among its members most of the leading Indianfirms, as well the subsidiaries of foreign MNCs, is openlysupportive of strengthening India’s intellectual propertyrights regime (http://www.indiaoppi.com/). Other industryassociations, such as the Indian Drug Manufacturers Asso-ciation, with memberships drawn from smaller firms tend tobe more critical of TRIPS.

40 There are other factors as well that might serve tomitigate the losses experienced by Indian firms, among themthe possibility of joint ventures with, or contract manufacturingfor, multinationals. Such collaborations are increasing in fre-quency in the Indian pharmaceutical industry.

TABLE 10—COUNTERFACTUAL ESTIMATES OF FOREGONE PROFITS OF DOMESTIC PRODUCERS FROM PRODUCT WITHDRAWAL

DUE TO THE INTRODUCTION OF PHARMACEUTICAL PATENTS (RS. BILL PER YEAR)


Pure lossof variety




switching

Only ciprofloxacin 0.95* 1.09* 0.40(0.16) (0.08) (0.31)

Only ofloxacin 0.04* 0.03† 0.14*(0.01) (0.02) (0.04)

Ciprofloxacin, ofloxacin, and norfloxacin 1.24* 1.40* 0.42(0.17) (0.11) (0.60)

Ciprofloxacin, ofloxacin, and sparfloxacin 1.11* 1.26* 0.48(0.17) (0.11) (0.50)

All four domestic quinolones products 2.34* 2.34* 2.34*(0.19) (0.19) (0.19)

Notes: Standard errors in parentheses. Asterisk (*) denotes significance at the 5-percent level, and dagger (†) denotessignificance at the 10-percent level.


magnitude as before, so we focus the rest of ourdiscussion on the results we obtained by usingthe lower bound of marginal cost.

Adding up the estimates of consumer welfarelosses from Table 8 and producer losses fromTable 10, we get estimates of the total welfarelosses to the Indian economy. These are re-ported in Table 11. At the upper bound, weestimate that, in the absence of any price regu-lation or compulsory licensing, the total annualwelfare losses to the Indian economy from thewithdrawal of just four domestic product groupsin the quinolone subsegment would be on theorder of Rs. 20.16 billion, which translates atthe then-prevailing exchange rate into a figureof US$450 million.

Given that in practice the simultaneous en-forcement of intellectual property rights andelimination of price controls is unlikely, a morerealistic estimate of the welfare loss can beobtained by assuming that price regulation willprevent upward price adjustments as a result ofproduct withdrawals. This gives us a mid-rangeestimate of Rs. 13.7 billion, or about US$305million per year for the scenario involving with-drawal of all four domestic quinolone productgroups. Of this amount, foregone profits of do-mestic producers constitute roughly Rs. 2.3 bil-lion, or US$50 million (circa 16 percent of thetotal welfare loss). The overwhelming portionof the total welfare loss, therefore, derives fromthe loss of consumer welfare.

Lastly, if we assume that the welfare lossesdue to the reduction in variety are a purely

transitional phenomenon or that they could beneutralized through expanded use of licensing,and subtract these from our upper bound esti-mates, we obtain a lower bound estimate of Rs.6.5 billion (� 20.16 13.7) or $144 millionannually. Though only about 30 percent of ourupper bound estimate, in absolute terms thislower bound estimate is still very large, repre-senting about 24 percent of antibiotic sales in2000.

Finally, Table 12 presents our estimates ofthe profit gains realized by foreign producers asa result of patent introduction. These estimatesindicate that the total profit gains to foreignproducers would be only about Rs. 2.4 billion,or approximately US$53 million per year. Moreimportantly, the US$53 million per year esti-mate corresponds to the rather unrealistic casewhere there is no price regulation, so that mul-tinationals are free to adjust their prices upwardin response to the reduced competition. In thepresence of price regulation that would keepprices fixed at their pre-patent-enforcementlevel (column 2 in Table 12), the profit gain forforeign multinationals becomes only Rs. 0.88billion, or US$19.6 million per year. To putthese numbers in perspective, sales of Ciproalone, the main patented ciprofloxacin productof Bayer, were roughly US$1.6 billion in 2000(Scott Hensley, “Cipro Loses Share in Tradi-tional Market, as Doctors Seek Other Cures inShortage,” Wall Street Journal, November 7,2001). Assuming a 40-percent markup (themarkup usually quoted for the pharmaceutical

TABLE 11—COUNTERFACTUAL ESTIMATES OF TOTAL WELFARE LOSSES FROM PRODUCT WITHDRAWAL DUE TO THE

INTRODUCTION OF PHARMACEUTICAL PATENTS (RS. BILL PER YEAR)

Counterfactual scenarios: Withdrawal ofone or more domestic product groups

Pure lossof variety




switching


Only ofloxacin 0.04 0.05 0.09(0.08) (0.09) (0.10)



All four domestic quinolones products 14.10* 13.70* 20.16(6.57) (6.48) (12.85)

Notes: Standard errors in parentheses. Asterisk (*) denotes significance at the 5-percent level.


industry), this translates to annual profits ofroughly US$640 million per year, for Bayer’sCipro alone.

IV. Conclusion

The results of our analysis suggest thatconcerns about the potentially adverse wel-fare effects of TRIPS in developing countriesmay have some basis. Specifically, we esti-mate that in the quinolone subsegment of thesystemic antibacterials segment alone, patentenforcement would result in a large welfareloss for the Indian economy. The estimatedloss ranges from $144 million to an upperbound of $450 million annually, dependingon the way policies are implemented, the ex-tent of price regulation, and the degree towhich foreign multinationals respond topatent protection by expanding their distribu-tion networks or using licensing more exten-sively. Of this amount, only a small fractionaccounts for the forgone profits of domestic(Indian) pharmaceutical firms. Hence, we donot find much support for the claim thatTRIPS would have detrimental effects on theIndian pharmaceutical industry. In fact, undersome scenarios we find that the profits ofdomestic firms may even increase; this hap-pens because, when certain domestic productsbecome unavailable as a result of patent en-forcement, consumers substitute toward other

domestic products containing different mole-cules, rather than foreign products containingthe same molecule. This differential effect ofTRIPS on domestic firms’ profits may partlyexplain the divided position of the Indianpharmaceutical industry regarding TRIPS.

With respect to the subsidiaries of foreignmultinationals, we estimate the profit gains ofthese firms to be approximately US$53 millionper year when patents are enforced. This is inthe absence of compulsory licensing or priceregulation. With price regulation that wouldkeep the prices of drugs supplied by multina-tional subsidiaries at their pre-TRIPS level, theprofit gains drop to only US$19.6 million peryear. While we certainly do not attempt to drawany conclusions about the relationship betweenintellectual property rights protection and re-search and innovation, we note that this numberrepresents a very small fraction of the annualsales of big pharmaceutical firms in this sub-segment.

By far, the biggest effects of TRIPS concernthe Indian consumers, for whom we estimatesubstantial welfare losses. The losses increasein the number of domestic products that areaffected by TRIPS. The worst-case scenarioinvolves simultaneous withdrawal of all do-mestic product groups in the quinolone sub-segment. In contrast, when only one domesticproduct, or a subset of domestic products, arewithdrawn, the consumer losses are modest.

TABLE 12—COUNTERFACTUAL ESTIMATES OF PROFIT GAINS OF FOREIGN PRODUCERS FROM PRODUCT WITHDRAWAL DUE TO

THE INTRODUCTION OF PHARMACEUTICAL PATENTS (RS. BILL PER YEAR)

Counterfactual scenarios: Withdrawal ofone or more domestic product groups

Pure lossof variety




switching


Only ofloxacin 0.02 0.02 0.01(0.01) (0.01) (0.02)

Ciprofloxacin, ofloxacin, and norfloxacin 0.36* 0.28* 0.71†

(0.05) (0.11) (0.40)Ciprofloxacin, ofloxacin, and sparfloxacin 0.37* 0.30* 0.79*

(0.05) (0.10) (0.38)All four domestic quinolones products 1.17* 0.88* 2.43†

(0.31) (0.41) (1.44)



This pattern is driven by the empirical findingthat domestic products are viewed by Indianconsumers as close substitutes; accordingly,the existence of some degree of domesticcompetition has a big impact on consumerwell-being.

Finally, our decomposition of the total con-sumer loss into a “product-variety” effect, an“expenditure-switching” effect, and a “price-adjustment” effect, has interesting policy im-plications. We find that a substantial fractionof the total welfare loss is attributable to theloss of variety, which we interpret as primar-ily capturing an “ease of access” effect: be-cause the retail coverage of domestic firms inIndia is substantially more extensive than thatof foreign multinationals, drugs produced bydomestic firms are more readily available toIndian consumers than drugs sold by foreignproducers. This suggests a potentially inde-pendent role of compulsory licensing in addi-tion to, or in lieu of, price regulation, for thesole purpose of mitigating the loss of productvariety effect. Even if one considers this ef-fect to be only a transitional phenomenon thatwill diminish in importance as foreign firmsrespond to TRIPS enforcement by expandingtheir product portfolios and distribution net-works, or by using licensing more exten-sively, the welfare loss due to upward priceadjustment remains substantial. The price-adjustment component of welfare loss couldpotentially be mitigated by appropriate pricecontrols or other regulations. In this case,however, the incentives of multinationals toexpand their operations in the Indian marketwould become questionable, and the welfare

loss attributable to the loss of product varietycould become a permanent effect.

In general, our simulations indicate that froma consumer welfare point of view, the issue ofproduct availability is as important as the issueof affordability. In this sense, our analysis sug-gests that policymakers should evaluate TRIPS-related policies not only in terms of theireffects on drug prices, but also in terms oftheir impact on product availability. This ob-servation is more relevant the more likely it isthat there will be tension between policiesdesigned to address these two sets of effects.Intellectual property rights enforcement with-out price regulation is likely to bolster foreignfirms’ incentives to market their products indeveloping countries and use licensing moreextensively than in the past, but it brings withit the potential of substantial price increasesof patented products. Accompanying priceregulation can prevent patent holders fromexploiting their market power, but not withoutdiminishing the incentives of such firms toexpand their operations in the developingworld. A combination of policies that wouldcompletely neutralize TRIPS’ adverse effectson consumer welfare is hence unlikely.

Lastly, we find that expenditure switchingacross subsegments has a limited role in con-taining consumer welfare loss. The claim ofTRIPS proponents that any adverse effects aris-ing from the introduction of a patent in a par-ticular market would be mitigated by theavailability of close therapeutic substitutes isthus valid only if there are patent-expired sub-stitutes available within fairly narrowly definedtherapeutic categories.


APPENDIX: ADDITIONAL TABLES AND ROBUSTNESS CHECKS

TABLE A1—SPECTRUM OF ACTIVITY OF VARIOUS FAMILIES OF ANTI-BACTERIAL DRUGS

Organism Tet

racy

clin

es

Chl

oram

peni

cols

Am

pici

llin,

amox

ycill

in

Cep

halo

spor

ins

Tri

met

hopr

imco

mbi

natio

ns

Mac

rolid

es

Oth

erpe

nici

llins

Am

inog

lyco

side

s

Fluo

roqu

inol

ones

Gram-positive cocciStaphylococcus aureus

Non-penicillinase producing x x x x xPenicillinase producing x x x x x

Streptococcus bovisSerious infections x x xUncomplicated urinary tract infection x x x

Streptococcus pneumoniae x x x x x xGram-negative cocci

Neisseria meningitidis x x xNeisseria gonorrhaoeae

Non-beta-lactamase producing x x x x xBeta-lactamase producing x x x x

Gram-negative bacilliAcinetobacter spp. x x x xBrucella spp. x x x xCampylobacter jejuni x x x xEnterobacter spp. x x x xEscherichia coli

Uncomplicated urinary tract infection x x x x xSystemic infection x x x x

Francisella tularensis x x x xHaemophilus influenzae

Meningitis x x x x xOther infections x x x x

Klebisiella pneumonia x x x x x xLegionella spp. x x x xProteus mirabillis x x x xOther proteus spp. x x x x xProvidencia spp. x x x x xPseudomonas aeruginosa x x x xSalmonella spp. x x x xSerratia marcescens x x x x xShigella spp. x x xYersinia pestis x x x x

Anaerobic bacteriaAnaerobic streptococci x x x x xBacteroides spp.

Oropharyngeal strains x x x x x xGastrointestinal strains x x x x x

Clostridium spp. x x x

Notes: An “x” in a cell indicates that at least one member of the family of drugs indicated in the column heading is listedas the antimicrobial drug of choice or as an alternative agent for the treatment of the bacterial infection indicated in the rowheading.

Source: Table 15-1, pp. 225–26, Principles and Practice of Infectious Diseases (2000).


TABLE A2—COEFFICIENT ESTIMATES FROM THE LOWER-LEVEL AIDS SYSTEM

Product group ConstantOwn-pricecoefficients

Cross-price coefficients

Coefficienton

quinolonesexpenditure

Easternregion

dummy

Westernregion

dummy

Southernregion

dummy

Samemolecule,different

status

Differentmolecule,

foreigngroup

Differentmolecule,domestic

group

Foreignciprofloxacin

0.013 0.120* 0.115* 0.003† 0.004* 0.010 0.027* 0.031* 0.007*(0.02) (0.05) (0.05) (0.00) (0.00) (0.01) (0.01) (0.01) (0.00)

Foreignnorfloxacin

0.006 0.000 0.005 0.003† 0.004* 0.001 0.001* 0.000 0.000(0.01) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00) (0.00)

Foreignofloxacin

0.047* 0.013 0.008 0.003† 0.004* 0.005 0.001 0.003 0.007*(0.02) (0.01) (0.01) (0.00) (0.00) (0.01) (0.00) (0.00) (0.00)

Domesticciprofloxacin

0.603* 0.298* 0.115* 0.004* 0.058* 0.102* 0.001 0.044* 0.033*(0.04) (0.05) (0.05) (0.00) (0.00) (0.02) (0.01) (0.01) (0.01)

Domesticnorfloxacin

0.206* 0.177* 0.005 0.004* 0.058* 0.038* 0.041* 0.034* 0.031*(0.03) (0.02) (0.00) (0.00) (0.00) (0.01) (0.01) (0.01) (0.01)

Domesticofloxacin

0.339* 0.191* 0.008 0.004* 0.058* 0.008 0.018 0.034* 0.026*(0.04) (0.01) (0.01) (0.00) (0.00) (0.02) (0.01) (0.01) (0.01)

Domesticsparfloxacin

0.209* 0.186* 0.004* 0.058* 0.072* 0.006 0.016* 0.028*(0.03) (0.02) (0.00) (0.00) (0.01) (0.01) (0.00) (0.00)


TABLE A3—COEFFICIENT ESTIMATES FROM THE UPPER-LEVEL AIDS SYSTEM

Product group Con

stan

t

Cross-price coefficients

Ant

ibio

tics

expe

nditu

re

Eas

tern

regi

ondu

mm

y

Wes

tern

regi

ondu

mm

y

Sout

hern

regi

ondu

mm

y

Tet

racy

clin

e

Chl

oram

phen

icol

Am

pici

llin

Cep

halo

spor

in

Tri

met

hopr

im

Mac

rolid

es

Oth

erpe

nici

llin

Qui

nolo

nes

Tetracycline 0.11 0.05 0.03 0.14† 0.13* 0.07† 0.08† 0.02 0.05 0.03* 0.03* 0.00 0.00(0.09) (0.06) (0.02) (0.08) (0.06) (0.04) (0.04) (0.03) (0.05) (0.01) (0.01) (0.04) (0.04)

Chloramphenicol 0.02 0.03 0.04* 0.15* 0.09* 0.00 0.09* 0.00 0.14* 0.02* 0.02* 0.01 0.01(0.05) (0.02) (0.01) (0.04) (0.03) (0.02) (0.02) (0.02) (0.04) (0.00) (0.01) (0.01) (0.01)

Ampicillin 0.27 0.14† 0.15* 0.00 0.10 0.02 0.04 0.09 0.03 0.01 0.07* 0.13* 0.18*(0.17) (0.08) (0.04) (0.14) (0.10) (0.06) (0.08) (0.07) (0.07) (0.01) (0.03) (0.05) (0.05)

Cephalosporin 0.22 0.13* 0.09* 0.10 0.26† 0.15† 0.08 0.16† 0.19* 0.03* 0.09* 0.03 0.06(0.22) (0.06) (0.03) (0.10) (0.15) (0.08) (0.10) (0.09) (0.08) (0.01) (0.02) (0.05) (0.05)

Trimethoprim 0.36* 0.07† 0.00 0.02 0.15† 0.04 0.16* 0.03 0.22* 0.01* 0.01 0.06* 0.05†

(0.13) (0.04) (0.02) (0.06) (0.08) (0.13) (0.06) (0.07) (0.05) (0.01) (0.01) (0.03) (0.03)Macrolides 0.14 0.08† 0.09* 0.04 0.08 0.16* 0.19† 0.08 0.05 0.00 0.02 0.04 0.03

(0.21) (0.04) (0.02) (0.08) (0.10) (0.06) (0.11) (0.08) (0.05) (0.01) (0.02) (0.03) (0.03)Other penicillin 0.17 0.02 0.00 0.09 0.16† 0.03 0.08 0.06 0.08† 0.01 0.02† 0.00 0.00

(0.18) (0.03) (0.02) (0.07) (0.09) (0.07) (0.08) (0.07) (0.05) (0.00) (0.01) (0.02) (0.03)Quinolones 0.41* 0.05 0.14* 0.03 0.19* 0.22* 0.05 0.08† 0.02 0.04* 0.03 0.01 0.03

(0.11) (0.05) (0.04) (0.07) (0.08) (0.05) (0.05) (0.05) (0.06) (0.02) (0.02) (0.03) (0.03)

Notes: Standard errors in parentheses. Asterisk (*) denotes significance at the 5-percent confidence level, and dagger (†)denotes significance at the 10-percent confidence level.


TABLE A4—OTHER SPECIFICATIONS FOR THE NORTHERN REGION

A4(a): Demand patterns with seasonal dummies: Summer

Product group


Foreign groups’ prices Domestic groups’ prices

Cipro Norflo Oflo Cipro Norflo Oflo Sparflo

Foreign ciprofloxacin 6.06* 0.14† 0.15* 4.54* 0.12 0.13† 0.16*(1.91) (0.07) (0.08) (2.15) (0.07) (0.07) (0.07)

Foreign norfloxacin 6.10 0.12 6.09 5.27 7.47 6.10 6.19(4.55) (2.26) (4.55) (6.01) (11.68) (4.62) (4.58)

Foreign ofloxacin 0.11† 0.11† 1.58* 0.08 0.11* 0.47 0.11*(0.06) (0.06) (0.32) (0.32) (0.05) (0.29) (0.06)

Domestic ciprofloxacin 0.18* 0.01† 0.00 1.72* 0.07* 0.07* 0.10*(0.08) (0.00) (0.01) (0.29) (0.03) (0.02) (0.03)

Domestic norfloxacin 0.03* 0.03 0.03* 0.58* 2.04* 0.36* 0.33*(0.01) (0.03) (0.01) (0.19) (0.11) (0.04) (0.04)

Domestic ofloxacin 0.06† 0.05* 0.23† 0.89* 0.79* 3.67* 0.77*(0.03) (0.02) (0.14) (0.40) (0.11) (0.36) (0.12)

Domestic sparfloxacin 0.07* 0.04* 0.06* 1.25* 0.60* 0.58* 2.81*(0.03) (0.02) (0.02) (0.20) (0.06) (0.07) (0.17)

A4(b): Demand patterns with seasonal dummies: Monsoon

Product group




Foreign ciprofloxacin 5.80* 0.13† 0.15* 4.32* 0.11† 0.11 0.15*(1.89) (0.07) (0.07) (2.07) (0.07) (0.07) (0.07)

Foreign norfloxacin 3.52 0.35 3.52 3.03 4.31 3.51* 3.57*(2.19) (1.04) (2.19) (2.17) (4.92) (1.71) (1.72)

Foreign ofloxacin 0.10† 0.09† 1.51* 0.06 0.09* 0.41† 0.10*(0.05) (0.05) (0.26) (0.29) (0.04) (0.25) (0.04)

Domestic ciprofloxacin 0.19* 0.01* 0.00 1.72* 0.07* 0.07* 0.10*(0.08) (0.00) (0.01) (0.29) (0.02) (0.03) (0.02)


Domestic ofloxacin 0.05† 0.04* 0.19† 0.72* 0.65* 3.20* 0.64*(0.02) (0.02) (0.11) (0.32) (0.08) (0.27) (0.09)


A4(c): Demand patterns with seasonal dummies: Winter

Product group




Foreign ciprofloxacin 5.66* 0.13† 0.14† 4.19* 0.12† 0.11 0.14*(1.80) (0.07) (0.07) (2.02) (0.07) (0.08) (0.07)

Foreign norfloxacin 4.37† 0.20 4.36† 3.78 5.33 4.34* 4.41*(2.54) (1.42) (2.53) (2.66) (6.10) (2.13) (2.13)

Foreign ofloxacin 0.10† 0.10† 1.55* 0.07 0.10* 0.44† 0.10*(0.06) (0.05) (0.28) (0.30) (0.05) (0.26) (0.05)

Domestic ciprofloxacin 0.19* 0.01* 0.00 1.73* 0.07* 0.07* 0.10*(0.08) (0.00) (0.01) (0.29) (0.02) (0.03) (0.02)


Domestic ofloxacin 0.04† 0.04* 0.18 0.68* 0.61* 3.08* 0.61*(0.03) (0.02) (0.12) (0.33) (0.10) (0.35) (0.10)


Notes: Standard errors in parentheses. Elasticities evaluated at average revenue shares. Asterisk (*) denotes significance at the5-percent confidence level, and dagger (†) denotes significance at the 10-percent confidence level.


REFERENCES

Andrews, Donald W. K., and Moshe Buchinsky.2000. “A Three-Step Method for Choosingthe Number of Bootstrap Repetitions.”Econometrica, 68(1): 23–51.

Bale, Harvey E., Jr. 2001. “Consumption andTrade in Off-Patented Medicines.” WorldHealth Organization, Commission for Mac-roeconomics and Health Working Paper Se-ries WG 4: 3.

Barton, John. 2001. “Differentiated Pricing of

Patented Products.” World Health Organiza-tion, Commission for Macroeconomics andHealth Working Paper Series WG4: 2.

Berndt, Ernst R. 1994. Uniform PharmaceuticalPricing: An Economic Analysis. Washington,DC: AEI Press.

Berry, Steven, James Levinsohn, and Ariel Pakes.1995. “Automobile Prices in Market Equilib-rium.” Econometrica, 63(4): 841–90.

Bresnahan, Timothy F. 1997. “Valuation of NewGoods under Perfect and Imperfect Compe-tition: Comment.” In The Economics of New

TABLE A5—DEMAND PATTERNS WITH OLS COEFFICIENTS

Product group




Foreign ciprofloxacin 2.76* 0.05 0.06 1.39 0.00 0.01 0.05(0.82) (0.05) (0.05) (0.92) (0.07) (0.07) (0.07)

Foreign norfloxacin 1.66 1.24 1.64 0.59 2.25 1.02 1.10(1.75) (1.61) (1.75) (2.04) (6.73) (2.04) (2.07)

Foreign ofloxacin 0.04 0.04 0.99* 0.07 0.02 0.02 0.03(0.05) (0.04) (0.26) (0.24) (0.05) (0.23) (0.05)

Domestic ciprofloxacin 0.06 0.00 0.01 1.56* 0.07* 0.07* 0.10*(0.04) (0.00) (0.01) (0.25) (0.03) (0.03) (0.02)

Domestic norfloxacin 0.01 0.01 0.01 0.46* 2.18* 0.40* 0.39*(0.02) (0.04) (0.01) (0.20) (0.11) (0.04) (0.04)

Domestic ofloxacin 0.02 0.01 0.00 0.81* 0.73* 3.16* 0.71*(0.03) (0.02) (0.10) (0.22) (0.07) (0.24) (0.07)

Domestic sparfloxacin 0.06* 0.01 0.04† 1.25* 0.63* 0.64* 2.76*(0.02) (0.02) (0.02) (0.15) (0.06) (0.06) (0.17)

TABLE A6—COUNTERFACTUAL ESTIMATES OF CONSUMER WELFARE LOSSES FROM PRODUCT WITHDRAWAL DUE TO THE

INTRODUCTION OF PHARMACEUTICAL PATENTS, ASSUMING MC � P (RS. BILL PER YEAR)


Pure lossof variety




switching


Only ofloxacin 0.08 0.08 4.08*(0.09) (0.09) (0.83)



All four domestic quinolones products 11.76† 11.35† 19.46(6.28) (6.28) (15.20)

Notes: Standard errors in parentheses. Asterisk (*) denotes significance at 5-percent confidence level, and dagger (†) denotessignificance at 10-percent confidence level.


Goods, ed. Timothy F. Bresnahan and RobertJ. Gordon, 237–47. Chicago: University ofChicago Press.

Caves, Richard E., Michael D. Whinston, andMark A. Hurwitz. 1991. “Patent Expiration,Entry, and Competition in the U.S. Pharma-ceutical Industry.” Brookings Papers on Eco-nomic Activity, Special Issue: 1–48.

Challu, Pablo M. 1991. “The Consequences ofPharmaceutical Product Patenting.” WorldCompetition, 15(2): 65–126.

Chin, Judith C., and Gene M. Grossman. 1990.“Intellectual Property Rights and North-South Trade.” In The Political Economy ofInternational Trade: Essays in Honor of Rob-ert E. Baldwin, ed. Ronald W. Jones andAnne O. Krueger, 90–107. Cambridge, MA:Basil Blackwell.

Christensen, Laurits R., Dale W. Jorgenson, andLawrence J. Lau. 1975. “Transcendental Log-arithmic Utility Functions.” American Eco-nomic Review, 65(3): 367–83.

Deardorff, Alan V. 1992. “Welfare Effects ofGlobal Patent Protection.” Economica,59(233): 35–51.

Deaton, Angus S., and John Muellbauer. 1980a.“An Almost Ideal Demand System.” Ameri-can Economic Review, 70(3): 312–26.

Deaton, Angus S., and John Muellbauer. 1980b.Economics and Consumer Behavior. Cam-bridge: Cambridge University Press.

Diwan, Ishac, and Dani Rodrik. 1991. “Patents,Appropriate Technology, and North-SouthTrade.” Journal of International Economics,30(1-2): 27–47.

Efron, Bradley, and Robert J. Tibshirani. 1993.An Introduction to the Bootstrap: Mono-graphs on Statistics and Applied Probability.Vol. 57. New York: Chapman and Hall.

Ellison, Sara F., Iain M. Cockburn, Zvi Griliches,and Jerry A. Hausman. 1997. “Characteristicsof Demand for Pharmaceutical Products: AnExamination of Four Cephalosporins.”RAND Journal of Economics, 28(3): 426–46.

Fink, Carsten. 2000. “How Stronger Patent Pro-tection in India Might Affect the Behavior ofTransnational Pharmaceutical Industries.”The World Bank, Policy Research WorkingPaper 2352.

Frank, Richard G., and David S. Salkever. 1997.“Generic Entry and the Pricing of Pharma-ceuticals.” Journal of Economics and Man-agement Strategy, 6(1): 75–90.

Ganslandt, Mattias, Keith E. Maskus, and Eina V.Wong. 2001. “Developing and DistributingEssential Medicines to Poor Countries: TheDEFEND Proposal.” World Economy, 24(6):779–95.

Goldberg, Pinelopi K. 1995. “Product Differen-tiation and Oligopoly in International Mar-kets: The Case of the U.S. AutomobileIndustry.” Econometrica, 63(4): 891–951.

Grossman, Gene M., and Edwin L.-C. Lai. 2004.“International Protection of Intellectual Prop-erty.” American Economic Review, 94(5):1635–53.

Hausman, Jerry A. 1994. “Valuation of NewGoods under Perfect and Imperfect Compe-tition.” National Bureau of Economic Re-search Working Paper 4970.

Hausman, Jerry A., and Gregory K. Leonard.2002. “The Competitive Effects of a NewProduct Introduction: A Case Study.” Jour-nal of Industrial Economics, 50(3): 237–63.

Helpman, Elhanan. 1993. “Innovation, Imita-tion, and Intellectual Property Rights.”Econometrica, 61(6): 1247–80.

Indian Credit Rating Agency. 1999. Indian Phar-maceutical Industry. New Delhi: ICRA, In-dustry Watch Series.

Kremer, Michael. 1998. “Patent Buyouts: AMechanism for Encouraging Innovation.”Quarterly Journal of Economics, 113(4):1137–67.

Lanjouw, Jean O. 1998. “The Introduction ofPharmaceutical Product Patents in India:‘Heartless Exploitation of the Poor and Suf-fering’?” National Bureau of Economic Re-search Working Paper 6366.

Lanjouw, Jean O., and Iain M. Cockburn. 2001.“New Pills for Poor People? Empirical Evi-dence after GATT.” World Development,29(2): 265–89.

Lu, Z. John, and William S. Comanor. 1998.“Strategic Pricing of New Pharmaceuticals.”Review of Economics and Statistics, 80(1):108–18.

Mandell, Gerald l., John E. Bennett, and RaphaelDolin. 2000. Principles and Practice of Infec-tious Diseases. 5th ed. Philadelphia and Lon-don: Churchill Livingstone/Harcourt Brace.

Maskus, Keith E. 2000. Intellectual PropertyRights in the Global Economy. Washington,D.C.: Institute for International Economics.

Maskus, Keith E., and Denise E. Konan. 1994.“Trade-Related Intellectual Property Rights:


Issues and Exploratory Results.” In Analyti-cal and Negotiating Issues in the GlobalTrading System, ed. Alan V. Deardorff andRobert M. Stern, 401–46. Ann Arbor, MI:University of Michigan Press.

Mazzoleni, Roberto, and Richard R. Nelson.1998a. “The Benefits and Costs of StrongPatent Protection: A Contribution to the Cur-rent Debate.” Research Policy, 27(3): 273–84.

Mazzoleni, Roberto, and Richard R. Nelson.1998b. “Economic Theories about the Bene-fits and Costs of Patents.” Journal of Eco-nomic Issues, 32(4): 1031–52.

Nevo, Aviv. 2001. “Measuring Market Power inthe Ready-to-Eat Cereal Industry.” Econo-metrica, 69(2): 307–42.

Nogues, Julio J. 1993. “Social Costs and Bene-fits of Introducing Patent Protection for Phar-maceutical Drugs in Developing Countries.”Developing Economies, 31(1): 24–53.

Nordhaus, William D. 1969. Invention, Growthand Welfare: A Theoretical Treatment ofTechnological Change. Cambridge, MA:MIT Press.

Pollack, Andrew. April 20, 2001. “News Analy-sis: Defensive Drug Industry Fuels Fight overPatents,” New York Times.

Quah, Danny. 2002. “Almost Efficient Innova-tion by Pricing Ideas.” London School ofEconomics, Center for Economic Perfor-mance Working Paper 1219.

Rozek, Richard P., and Ruth Berkowitz. 1998.“The Effects of Patent Protection on thePrices of Pharmaceutical Products: Is Intel-

lectual Property Protection Raising the DrugBill in Developing Countries.” Journal ofIntellectual Property, 1(2): 179–243.

Sachs, Jeffrey D. 2002. “A New Global Effort toControl Malaria.” Science, 298(5591): 122–24.

Scherer, Frederic M., and Jayashree Watal. 2002.“Post-TRIPS Options for Access to PatentedMedicines in Developing Nations.” Journalof International Economic Law, 5(4): 913–39.

Stevenson, Richard W. November 15, 2001.“Measuring Success: At Least the TalksDidn’t Collapse,” New York Times.

Subramanian, Arvind. 1995. “Putting SomeNumbers on the TRIPS Pharmaceutical De-bate.” International Journal of TechnologyManagement, 10(2-3): 252–68.

Trajtenberg, Manuel. 1989. “The Welfare Anal-ysis of Product Innovations, with an Appli-cation to Computed Tomography Scanners.”Journal of Political Economy, 97(2): 444–79.

Wattal, Jayashree. 1996. “Introducing ProductPatents in the Indian Pharmaceutical Sec-tor—Implications for Prices and Welfare.”World Competition, 20(2): 5–21.

Wattal, Jayashree. 2000. “Pharmaceutical Pat-ents, Prices and Welfare Losses: Policy Op-tions for India under the WTO TRIPSAgreement.” World Economy, 23(5): 733–52.

Wright, Brian D. 1983. “The Economics of In-vention Incentives: Patents, Prizes, and Re-search Contracts.” American EconomicReview, 73(4): 691–707.


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15. David Lea. 2008. The Expansion and Restructuring of Intellectual Property and Its Implicationsfor the Developing World. Ethical Theory and Moral Practice 11:1, 37-60. [CrossRef]

16. Keith E. MaskusChapter 15 Incorporating a Globalized Intellectual Property Rights Regime intoan Economic Development Strategy 2, 497-524. [CrossRef]

http://dx.doi.org/10.1257/aer.102.1.396

http://pubs.aeaweb.org/doi/pdf/10.1257/aer.102.1.396

http://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.102.1.396

http://pubs.aeaweb.org/doi/pdfplus/10.1257/aer.102.1.396

http://dx.doi.org/10.1016/j.econlet.2011.12.114

http://dx.doi.org/10.1080/09638199.2010.541273

http://dx.doi.org/10.1002/hec.1790

http://dx.doi.org/10.2165/11539060-000000000-00000

http://dx.doi.org/10.1007/s10657-011-9225-z

http://dx.doi.org/10.1162/REST_a_00056

http://dx.doi.org/10.1186/1758-2652-14-15

http://dx.doi.org/10.1016/j.jinteco.2010.09.001

http://dx.doi.org/10.1016/j.jhealeco.2010.08.003

http://dx.doi.org/10.1080/14765284.2010.493640

http://dx.doi.org/10.1111/j.1542-4774.2010.tb00506.x

http://dx.doi.org/10.1108/13581981011033970

http://dx.doi.org/10.1111/j.1467-6451.2009.00400.x

http://dx.doi.org/10.1007/s10677-007-9084-4

http://dx.doi.org/10.1016/S1574-8715(07)00015-2

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