Modeling global pricing and launching of new drugs
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Doctorado en Economía: Aplicaciones a las finanzas y seguros, a la economía sectorial, al medio ambiente y a las infraestructuras.
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DOCTORAL THESIS Modeling Global Pricing and Launching of New Drugs Author: Borja García Lorenzo Las Palmas de Gran Canaria, April 2014
DOCTORADO EN ECONOMÍA: APLICACIONES A LAS FINANZAS Y SEGUROS, A LA ECONOMÍA SECTORIAL, AL MEDIO AMBIENTE Y A LAS INFRAESTRUCTURAS. Modeling Global Pricing and Launching of New Drugs Tesis doctoral presentada por D. Borja García Lorenzo Dirigida por Dra. Beatriz González López-Valcárcel La Directora, El Doctorando, Las Palmas de Gran Canaria, abril de 2014
Existimos porque alguien piensa en nosotros, y no al revés
Acknowledgments Foremost, I would like to express my sincere gratitude to my advisor Dr. Beatriz González López-Valcárcel for the continuous support of my Ph.D study and research, for his patience, motivation, enthusiasm, constant feedback and immense knowledge. His guidance helped me in all the time of research and writing of this thesis. My sincere thanks also go to Dr. Izabela Jelovac and Dr. Margaret Kyle for offering me the opportunities to enjoy my visiting scholars in the Groupe d’Analyse et Théorie Economique (GATE) and the Toulouse School of Economics (TSE) respectively, for their encouragement, insightful comments, and hard questions. I also thank Carlos J. Pérez for his helps in the field of decision theory. I thank my fellow officemates in the University of Las Palmas de Gran Canaria (ULPGC): Reinaldo, Hicham, Rubén, Teresa and Federico for the stimulating discussions, for the hard days we were working together, and for all the fun we have had in the last years. I gratefully acknowledge the funding received towards my PhD from the Canarian Agency for Research, Innovation and Information Society of the Canarian Government (ACIISI). Also, I thank IMS for providing the data for the empirical section, particuarly to Miguel Martínez. Last but not the least, I would like to thank my parents Roque and Pepa for supporting my education without regard, and Naira, for her understanding, even so she has not a Ph.D, she has supported me as if she was one. Friends around me have been a great support to reach this moment. Thank you all.
Contents XVII List of Tables TABLE2.1PPRFOROPTIMALCOUNTRYLAUNCHSEQUENCE(PI,QI)................................................................................69 TABLE2.2COUNTRYLAUNCHSEQUENCEINFIGURE2.2..............................................................................................71 TABLE2.3.OPTIMALCOUNTRYLAUNCHSEQUENCEINFIGURE2.3................................................................................72 TABLE2.4.OPTIMALCOUNTRYLAUNCHSEQUENCEINFIGURE2.4................................................................................73 TABLE2.5.OPTIMALCOUNTRYLAUNCHSEQUENCEINFIGURE2.5................................................................................74 TABLE3.1DESCRIPTIVESTATISTICS.RETAILMARKET....................................................................................................83 TABLE3.2DESCRIPTIVESTATISTICS.HOSPITALMARKET................................................................................................84 TABLE3.3RELATIVEPRICESPEARSONCORRELATION.RETAILANDHOSPITALMARKET.........................................................85 TABLE3.4.BIVARIATETEST.ERPVS.NOERP...........................................................................................................85 TABLE3.5.BIVARIATETEST.EMAVS.NOEMA........................................................................................................85 TABLE3.6.LAUNCHDELAYEQUATIONOFTHENPLM................................................................................................103 TABLE3.7.RELATIVELAUNCHPRICEEQUATIONOFTHENPLM....................................................................................106 TABLEA.1OVERVIEWOFTHEORETICALSTUDIES.......................................................................................................120 TABLEA.2.OVERVIEWOFEMPIRICALSTUDIES..........................................................................................................121 TABLEB.1.HEALTHAGENCYSURPLUSOFCOUNTRYC................................................................................................143 TABLEB.2.HEALTHAGENCYSURPLUSOFCOUNTRYD...............................................................................................143 TABLEC.1.LAUNCHEQUATION:D&EVS.UPDATEDMODEL.......................................................................................153 TABLEC.2.LAUNCHPRICEEQUATION:D&EVS.UPDATEDMODEL...............................................................................157 TABLEC.3.LISTOFCOUNTRIES.VERNIERSETAL.VS.UM..........................................................................................165 TABLEC.4.LAUNCHWINDOWANDLAUNCHPRICEEQUATIONS.VERNIERSETAL.VS.UPDATEDMODEL.............................167 TABLEC.5.VARIABLECLASSIFICATIONOFTHENPLM................................................................................................171 TABLEC.6.PROBITSELECTIONEQUATIONOFNPML.................................................................................................171 TABLEC.7.GOODNESSOFFITOFPARAMETRICMODELS.RETAILMARKET.......................................................................174 TABLEC.8.GOODNESSOFFITOFPARAMETRICMODELS.HOSPITALMARKET...................................................................176 TABLA4.1.PPRPARALASECUENCIAÓPTIMADELANZAMIENTO(PI,QI).........................................................................202 TABLA4.2.REGIONESDESECUENCIASÓPTIMASDELANZAMIENTOA)...........................................................................204 TABLA4.3.BONDADDELAJUSTEDELOSMODELOSPARAMÉTRICOS.MERCADOAMBULATORIO..........................................212 TABLA4.4.ECUACIÓNDELPRECIORELATIVODELANZAMIENTO...................................................................................225 TABLA4.5.ECUACIÓNDERETRASOENELLANZAMIENTO............................................................................................227
List of abbreviates AIFA Italian National Agency for Drug Administration and Control Prices AME Average Marginal Effect ATC Anatomic Therapeutic Chemical Classification System CHEPA Centre for Health Economics and Policy Analysis CEA Cost-Effectiveness Analysis CRES Centre de Recerca en Economia i Salut DDD Defined Daily Dosage D&E Danzon & Epstein EMA European Medical Agency ERP External Reference Pricing EU European Union FDA Food and Drug Administration GDP Gross Domestic Product GLS Generalized Least Squares GPRM Global Price Reporting Mechanism HHI Hirschmand-Herfindähl Index HTA Health Technology Assessment
XX Modeling Global Pricing and Launching of New Drugs ICER Incremental Cost-Effectiveness Ratio IMF International Monetary Fund IMR Inverse Mills Ratio LSE London School of Economics MES Minimum Efficacy Standard MEPS Medical Expenditure Panel Survey MLIC Middle and Low Income Country NBER The National Bureau of Economic Research NCE New Chemical Entities NGO No-Governmental Organization NICE National Institute for Health and Care Excellence OECD Organisation for Economic Cooperation and Development OF Objective Function OLS Ordinary Least Squares OTC Over-the-Counter PC Price Cap PE Public Expenses PI Parallel Importer PPI Producer Price Indexes PPP Purchasing Power Parities PPR Preliminary Results
Contents XXI PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses PT Parallel Trade QALY Quality-adjusted Life Years R&D Research & Development RP Reference Price SU Standard Unit UK United Kingdom UM Updated Model US United States WtP Willingness to pay 3SLS Three-stage least squares
Introduction Pharmaceuticals are sold in a global market. This characteristic implies a specific bargaining procedure between pharmaceutical firms and countries’ health agencies. On the one hand, these firms make strategic decisions when launching medicines in different countries and to maximize their global profits; and on the other hand, countries’ health agencies implement pricing policies in order to control their pharmaceutical expenditure and to guarantee access to medicines. From the perspective of national health insurances, pricing policies within the pharmaceutical market are a key factor in controlling public expenditure (Scherer, 1993, Lobo, 2014)1. Particularly, the total pharmaceutical bill: across the Organisation for Economic Cooperation and Development (OECD) countries in 2009, this bill is estimated to have accounted for around 19% of health spending. In relation to the overall economy, pharmaceutical spending accounts for an average 1.5% of GDP in OECD countries. However, the dispersion around this average is high, pharmaceutical spending accounts for less than 1% of GDP in Norway and Denmark, while it reaches close to 2.5% of GDP in Greece, Hungary and the Slovak Republic. Expenditure on pharmaceuticals is predominantly financed through third-party payers in most OECD countries – either through the public health insurance, which accounts for around 60% of the total on average, or through private insurance coverage, leaving an average of more than a third of the total to be charged to households (OECD, 2011). From the pharmaceutical industry view, pricing and launching a new drug is a complex task directly connected to R&D policy, industrial policy and healthcare policy. Hence, pricing and launching are major strategic decisions. In many countries, the price is agreed with health care insurance providers (public or private). National pricing policies 1 The case of Spain as an example of the price regulation LOBO, F. 2014. La Intervención de Precios de los Medicamentos en España, Madrid, Springer..
Modeling Global Pricing and Launching of New Drugs 2 and strategies are essential elements in setting prices and making medicines available, since drug pricing should contribute to enhancing social welfare and take into account the interests of the industry, consumers and public insurers. Therefore, encouragement must be provided to develop new medicines, make them available to consumers and, at the same time, control pharmaceutical expenditure. Pricing and launching involve trade-offs between public welfare and private profits, between the interests of the manufacturer and those of the country. When countries set a drug price, they risk the possibility of not providing it at the time they desire, which may have consequences for the health and the welfare of the population (Lichtenberg, 2005). In turn, a firm that delays the launch of a medicine in a country is also delaying the profits to be derived from this country. However, in an increasingly globalized world, national pricing/launching of drugs has become in fact an international matter and interdependencies across countries should be taken into account. Both companies and countries must act locally but think globally. Due to mechanisms like external reference pricing (ERP, henceforth) and parallel trade (PT, henceforth) (Danzon et al., 2005, Danzon and Epstein, 2008, Garcia Mariñoso et al., 2011), setting the price of a drug in a particular country influences other countries’ pricing and launching. The use of ERP by countries may make a firm apply international pricing strategies that may harm countries’ welfare. On the one hand, the firm may set a single price2, which may benefit high-price3 countries but harm low-price ones. On the other hand, the firm may either attempt to set high4 prices in the first countries to avoid low-prices in later launches via ERP, or delay launches in low-price countries to avoid spill-over effects. These strategies can harm lowprice countries, and may even harm high-price ones (Garcia Mariñoso et al., 2011). Among existing drug pricing policies, most countries in the industrialized world have implemented either Cost-Effectiveness Analysis (CEA, henceforth) or ERP at some 2 Two factors contribute to price uniformity between different markets: a) the threats of parallel imports, and b) the use of international reference pricing DANZON, P. M. & TOWSE, A. 2003. Differential Pricing for Pharmaceuticals: Reconciling Access, R&D and Patents. International Journal of Health Care Finance and Economics, 3, 183-205.. 3 In the long run, consumers from high price countries will be worse off if this lower price results in lower than expected returns on R&D, and hence fewer new medicines than they would have been willing to pay for DANZON, P. M. 1997. Price Discrimination for Pharmaceuticals: Welfare Effects in the US and the EU. International Journal of the Economics of Business, 4, 310-322.. 4 This company strategy will not work if the high-price country revises its prices downwards after launch DANZON, P. M. & TOWSE, A. 2003. Differential Pricing for Pharmaceuticals: Reconciling Access, R&D and Patents. International Journal of Health Care Finance and Economics, 3, 183-205, DANZON, P. M. 1997. Price Discrimination for Pharmaceuticals: Welfare Effects in the US and the EU. International Journal of the Economics of Business, 4, 310-322.
Introduction 3 point in time with the aim of controlling pharmaceutical expenditure, while still ensuring access to medicines, mainly in on-patent medicines (Espin J et al., 2011, Rawlins, 2012). In this thesis, ERP is defined as “the practice of setting a price cap for pharmaceuticals, based on ex-manufacturer5 prices of identical or comparable products in other countries” (Garcia Mariñoso et al., 2011). Most countries use ERP as a pharmaceutical pricing strategy. The use of ERP as a mechanism to set pharmaceutical prices is quite widely applied: 24 of the 30 OECD countries (Espin J et al., 2011) and approximately 24 of the 28 EU Member States (Leopold et al., 2012) have used it. However, ERP is not applied homogeneously in every country. There are a wide variety of methods to design a foreign price index (Leopold et al., 2012, Espin J et al., 2011). It mainly depends on each country’s basket, the type of prices collected6, the method used (the lowest price, the average price, a percentage of the previous ones, etc.) and whether a weighted-index7 is used or not. We also note that some countries take into account ERP as a complementary pricing policy together with other pricing policies to help to make the price decision, and therefore it is not exclusively applied as a blind pricing policy8. ERP is used because of its simplicity at a technical and analytical level; collecting price information abroad does not require a huge effort. Furthermore, ERP users think that the prices taken as reference are roughly right, suitable or fair. However, they recognise that it is difficult to assess if the resulting prices are appropriate, efficient or optimal in accordance with any objective criterion. Additionally, if referencing countries set their prices too high or too low, then any country later applying the ERP method may run the risk of repeating the same mistake (Espin J et al., 2011). CEA in health economics aims to estimate the ratio between the cost of a healthrelated intervention and the benefit it produces in terms of the number of years lived in full health by the beneficiaries. Cost is measured in monetary units, while benefit needs to be expressed in gain of health measured by quantitative values. However, unlike cost– 5 Prices are ex-manufacturer prices. 6 Current price vs. price at launch 7 The most widely method used for new drugs is through non-weighted measures; such methods will not help to achieve the target of obtaining a comparable average level of prices. The application of weighted price indexes, comparable and useful as reference to the rest of countries, has been proposed DANZON, P. M. & CHAO, L. W. 2000. Cross-national price differences for pharmaceuticals: How large, and why? Journal of Health Economics, 19, 159-195.. 8 Espín et al. state that “regulators might not always be able or willing to “impose” a certain price, but instead use the price computed as a benchmark or reference for negotiations, often alongside other criteria, such as cost-plus, internal or therapeutic pricing”.
10 Modeling Global Pricing and Launching of New Drugs on the country size and positively on the country per capita income. This means that as long as the country size is large, the regulator will have greater bargaining power to negotiate prices with the pharmaceutical industry. By contrast, a high GDP per capita is related to a high willingness to pay for a drug. Also, the ask price depends positively on the country propensity for spillovers due to the use of ERP policy and parallel exports. This means that whether the country is a potential referenced country (see 2.2. below) or a potential parallel exporter (see 2.3 below), the firm will increase its reservation price. The bargaining results in the launch of the product if the country’s maximum offer price equals or exceeds the firm’s minimum ask price. If this condition is not met the delay occurs, moreover, the greater this difference, the longer the delay in launch. Danzon and Epstein (Danzon and Epstein, 2008) work under the same hypothesis as Danzon et al. (Danzon et al., 2005). In this case, they also contemplate the bargaining results in price and add more variables as explanatory factors of pricing and launching. One of them is the regulatory regime, which can be an internal reference price (RP) or ERP; both are expected to positively affect prices because substitute prices, either at home or abroad, are expected to increasing prices (see 2.2. and 2.6. below). The other variable added, the firm’s location, is measured by the fixed costs, which are expected to be lower if the launching firm is located in the country analysed (see 2.5. below). Now, the bargaining results in a price agreed within the range between the offer price of the country and ask price of the firm. Then the launch is likely to occur when the offer price of the country equals or exceeds the ask price of the firm. The authors underline that the trade-offs between price and delay are expected to differ across markets and across products within markets. 1.2.2 How the ERP is affecting the bargaining results in pricing and launching? Both Danzon et al. and Danzon and Epstein consider that the propensity of being a reference country may positively affect the price of a drug (Danzon et al., 2005, Danzon and Epstein, 2008). This argument is supported by the extended use of ERP by countries as a cost-containment policy (see Leopold et al. (Leopold et al., 2012)). Basically, ERP consists of setting a price cap for pharmaceuticals, based on prices of identical or comparable products in other countries. Despite the method of calculation (see Leopold et al., Richter, Stargardt and Schreyögg (Leopold et al., 2012, Richter, 2008, Stargardt and Schreyögg, 2006), we think that, whether a country is taken as reference by other
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 11 countries when pricing medicines, it is reasonable to consider that firm’s incentive will be to set high prices in the reference country. As discussed by Richter (Richter, 2008), the firm is better off launching its drug in high-price countries first, to influence prices in other countries to its advantage. In this sense, García-Mariñoso et al. (Garcia Mariñoso et al., 2011) directly analyse the effects of an ERP policy on a referencing country on the negotiation in this country and, furthermore, the incentive of the referencing country to apply ERP. To go into this idea, the authors consider one pharmaceutical firm, one onpatent drug and two countries operating a positive list of reimbursed pharmaceuticals, where patients pay a fixed and exogenous co-payment. Countries differ in the population size and the level of co-payment. A model of negotiation process as a Nash bargaining game is designed through which the authors compare independent price negotiations to the situation in which one country (the referencing country) engages in ERP. Two different scenarios are analysed, under “weak threats”12 if the drug is not reimbursed, or under “tough threats”13 if the drug is banned. In the case of ERP with weak threats, when the referencing country engages in ERP, the price negotiated in the referenced country increases. The total surplus generated by the negotiation between the referenced country and the firm increases14. This shows that the implicit negotiation power of the firm is higher when the referencing country engages in ERP as compared with independent negotiations. As Danzon et al. and Danzon and Epstein had suspected (Danzon and Epstein, 2008, Danzon et al., 2005), García-Mariñoso et al. (Garcia Mariñoso et al., 2011) show the fact that the referencing country engaged in ERP policy harms the referenced country in terms of high price and lower outputs. The same authors also examine the incentives to apply ERP policy rather than independent negotiations. Under the same hypothesis stated above, they state that a country has an incentive to engage in ERP if its co-payment levels are high when compared with the referenced country. This preference decreases as the size of the referencing country increases, and as co-payments of both countries converge. First, the referencing country size increases the ERP negotiated 12 Under “weak threats”, if the negotiations fail, the firm can still sell the drug at any price of its choice, but with no subsidy.This assumption is motivated by the fact that, in Europe, price-negotiating agencies have a minor role in the authorization of drugs. 13 Some countries outside Europe, such as Brazil or Canada, are known to threaten the firms with not authorizing drug sales if negotiations fail or if the firm does not accept ERP. 14 In this model, ERP is based on the price of a single reference country. However, results are highly sensitive to the modalities of the ERP.
12 Modeling Global Pricing and Launching of New Drugs between the referenced country and firm in two ways. The pie to be shared between both parties is larger, and the firm has a stronger disagreement payoff while the disagreement payoff of the referenced country remains the same. Second, as the negotiated price is shown to be increasing with the patients’ co-payment (see (Jelovac, 2008)), if the copayment in the referenced country decreases with respect to the referencing country then, the ERP will decrease and therefore the difference between the price independently negotiated and the ERP will decrease. Then, they conclude that only small countries should be observed to engage in ERP and/or ERP should be based on prices in large countries (or a large group of countries). The analysis yields an analogous prediction if one substitutes “large country” by ‘small co-payment country’ and vice versa. The authors further extend their analysis to account for competition between the firm’s pharmaceutical product and a therapeutic substitute that is already present on the market in both countries. This extension adds realism, particularly, it makes the weak threats scenario compatible with the observation that, in most European markets, being excluded from the public funding may be almost as bad as being banned, as sales out of the positive list of reimbursed drugs are negligible if subsidized therapeutic substitutes are available. Now, two drugs, 1 and 2, with similar therapeutic indications are considered. Each drug is produced by a different firm (firm 1 and firm 2). Both drugs are off patent and one is the generic substitute of the other15. The consumer perceives them to be different but face the same co-payment, although this co-payment may differ among countries. The drug 2 is already listed in both the referenced and the referencing markets. The two drugs are horizontally differentiated á la Hotelling (see (Hotelling, 1929)). As the independent price negotiations lead to a higher price in the referencing country, the firm will reject low prices in the referenced countries knowing that they will face a price cap in the referencing one. Main results continue to hold in this extension: ERP benefits the referencing country and harms the referenced country as well as the firm. On the other hand, under tough threats (see footnote 13), the firm suffers a harsher punishment in the case that negotiations fail (drug is banned). The main result with weak threats remains, i.e., ERP benefits the referencing country and harms the firm, 15 Lobo and Feldman have also modelled the role of trademarks, advertising and generic names on competition FELDMAN, R. & LOBO, F. 2013. Competition in prescription drug markets: the roles of trademarks, advertising, and generic names. European Journal of Health Economics, 14, 667-675, LOBO, F. & FELDMAN, R. 2013. Generic Drug Names and Social Welfare. Journal of Health Politics Policy and Law, 38, 573-597..
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 13 but the referenced country is not affected by the ERP. 1.2.3 Which role does PT play in the pharmaceutical market? As mentioned earlier in the section above, the firm prefers firstly launching its drug in high-price countries to influence prices in other countries to its advantage. This strategic behaviour may be not so useful if PT exists. As Danzon et al. and Danzon and Epstein commented, to be a parallel exporter country might influence positively on prices (Danzon et al., 2005, Danzon and Epstein, 2008). The reason has been clearly explained by Richter (Richter, 2008). We note that Richter considers that PT implies a loss of income for the firm, since it stops selling a certain amount at a higher price than in the absence of PT, the author includes PT as variable into objective function firm. As expected, in order to compensate this loss of income, the firm will have to increase the price. The author proposes a mathematical optimization problem for the firm, considering that the firm sells a drug across all countries and over all time periods and the lowest price country will be the parallel exporter one. This is quantified as the sum of the differences between the lowest price among all countries and the price stated in each country, multiplied by the quantity lost16 in the parallel importer country. Ganslandt and Maskus (Ganslandt and Maskus, 2004) go further and, not only consider PT as potential loss of income for the original manufacturer, but also investigate how PT firms behave and how the presence of PT affects equilibrium prices in the parallel importer (PI) and exporter countries. They develop a simple model of parallel imports in which an original manufacturer competes in its home market (Sweden) with PI firms and all firms set prices simultaneously. The authors suppose that the quantity to trade is exogenously given, and it is all sold. This idea is supported by the fact that in high-price markets the PI quantity rarely exceeds 10%, except in a few major products. A model of two countries is considered, with a high-income and a low-income country. The highincome country is unregulated, the low-income country has a price cap set by the government and the drug sold is an on-patent drug without substitutes. A marginal and a fixed cost of engaging in PT also exist. Given the quantity chosen by PI firms, the profitmaximizing price is calculated. The authors compare a model under a limited PI quantity 16 The quantity lost is calculated multiplying the market share lost by the quantity sold in the parallel importer country.
14 Modeling Global Pricing and Launching of New Drugs to another model that allows unlimited PI quantity. Under an unlimited quantity of PI, an “arbitrage-free” price arises, and consequently a price convergence from the high-income country to the low-income one. However, under a limited quantity of PI, the most real case as stated above, the manufacturing firm has an incentive to adapt to PT rather than to set an arbitrage-free price in its market. In this case, the equilibrium price of high-income country converges to the low-income one plus a variable trade cost17. As expected, there is an effect of competition, whereby the equilibrium price in the high-income country falls in the number of PI firms. Also, the authors identify that the equilibrium number of PI firms increases in the size of the market but decreases in the low-income country price and in the fixed and variable trade cost. 1.2.4 How asymmetric information on quality of drugs may affect drug pricing and launching? One of the factors influencing pricing and launching considered by Danzon et al. and Danzon and Epstein, has been the ICER that affects positively drug prices (Danzon et al., 2005, Danzon and Epstein, 2008). This measure may be interpreted as a price/quality indicator of the drug. In the literature, under different hypotheses, we can observe that information about quality matters. Two papers have considered the asymmetric information about either the quality drugs or the demand of quality drugs, to analyse the pricing and launching drugs (Atella et al., 2012, García-Mariñoso and Olivella, 2012). Atella et al. (Atella et al., 2012) propose a model of asymmetric information on the quality of the drugs, to find out how two types of regulatory regimes, one focused on quality and another on price control, affect drug prices, and furthermore, how the price regulation affects, ultimately, the quality of the drugs. On the other hand, García-Mariñoso and Olivella (García-Mariñoso and Olivella, 2012) propose a sequential launching and analyse how the informational spillovers, issued from the asymmetric information about the quality drugs, affects the drug pricing. The informational spillovers are defined as the claim of lower prices by one country, generated by the knowledge about lower prices in other countries. The notion that low prices may overspill to other countries even in the absence of PT or ERP is here introduced, different from the previous research. Atella et al. (Atella et al., 2012) compare two regulatory regimes. Under the first 17 The price in the low-income country is taken as given.
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 15 regime the government fixes a minimum efficacy standard (MES), this regime corresponds to the regulatory structure of the pharmaceutical market in the United States. Under the second regime, in addition to a MES, the government fixes a price cap (PC); this regime corresponds to the structure in many other countries as Italy. The model considers two countries that differ in their demand for drug efficacy, and one firm, which produces two types of drug, low and high-efficacy, but it cannot distinguish between high and low-type buyers. Regarding the paper of Atella et al. (Atella et al., 2012), this paper introduces patient co-payments, the discount’s factor on profits and on quality from the country. Whether the government fixes a MES that binds the low efficacy drug, then the efficacy and the price of the low efficacy drug increases to meet the MES and to cover the drug marginal cost. Instead, the efficacy of the high-efficacy drug is not affected and its price may be lower. However, if the regulation imposes too high efficacy standards, lowtype buyers would be excluded form the market. There exists a minimum efficacy threshold that optimally balances the higher R&D costs with the higher efficacy drugs delivered to low-type buyers. This optimal level is just below the level that excludes lowtype buyer from the market. Whether a PC that binds on the high-type drug is considered (but not on the lowtype drug), the firms respond producing high-type drugs lower in quality at a price correspondingly lower. Under both regimes, the efficacy and the price of the low efficacy drug increases to meet the MES and to cover marginal cost respectively, however, the efficacy of the high-type drugs may be undermined if the government fixes a PC that binds the high-type drugs, but also that the consumer of high-type drugs will save money. Finally, the net welfare will depend upon how binding is the price control and on the relative size of the two groups of buyers. It has been shown above how different types of regulation may affect on drug quality and pricing under asymmetric information about the buyers. Garcia-Mariñoso and Olivella (García-Mariñoso and Olivella, 2012) assume the asymmetric information on the other side. The countries (i.e. the buyers) do not know about the type of the firm, thus, the firm may be of high or low quality, and this information is in the hands of the firm. As the authors propose a sequential launching, the countries will have their prior beliefs and
16 Modeling Global Pricing and Launching of New Drugs there may exist informational spillovers. Thus, whether a low price is fixed in the country where the drug is first launched, this reveals private information (the quality of the firm concerning the production and distribution costs) to subsequent players concerning the price, and therefore, the following countries will also demand for low prices. Now, low prices may overspill to other countries even in the absence of PT or ERP. The reason is that countries that would in principle make generous price offers whether observe the firm accepting a low price elsewhere, they might change their mind and become aggressive. Along a dynamic game, now it is the firm, which accepts or rejects the offer from the country thus, the game is based on a “take it or leave it offer”. Countries may be aggressive or non-aggressive18. According to the firm strategic behaviour, although information spillovers can be avoided by launching in all countries simultaneously, the firm will prefer to delay if (from more to less expected) (i) the firm is sufficiently patient (high discount’s factor19); (ii) the aggressive country has a sufficient population; (iii) co-payments differ enough across countries; and (iv) countries have relatively pessimistic priors on quality. Interestingly, the authors present a counterargument to the statement that delay only occurs in small countries, thus it could happen that the country that suffers delay is the largest in size (as long as the rest of the factors mentioned go in the right direction) (García-Mariñoso and Olivella, 2012). 1.2.5 Are important the headquarters location and the contacts among firms when pricing drugs? Different from other studies, Cabrales and Jiménez-Martín (Cabrales and Jimenez-Martin, 2007) consider that the firm is located in the countries analysed. There are two countries, one of them regulates prices under ERP (the referencing country) and the other does not (the referenced country). One of the main contributions is that the firm profits are now maximized together with the consumer surplus by the regulator. The authors compare the maximizing price in two situations, when headquarters are located in 18 The aggressiveness will positively depend on the co-payment; the larger is the co-payment, the more aggressive the price offers will be. We already mentioned that if a country has observed a low price acceptation in a previous country, it will update its beliefs and become aggressive. The third factor is dynamic and forward-looking. Being aggressive today may lead the firm to reject the price offer in order to avoid the aggressiveness of future agencies. 19 The discount factor is the factor by which a future cash flow must be multiplied in order to obtain the present value. The higher the discount factor is, the greater the present value is assessed.
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 17 the regulated country or in the unregulated one. This theoretical model predicts that the price set by the regulator is slightly higher for the local multinational than for the foreign one, and as the size of the referenced country grows with respect to the referencing country, the price of the foreign multinational converges to the local multinational one. In this regard, if we assume that the countries that are stricter regulators were relatively small in size; this would imply that they could not influence substantially the prices in favour of the local multinationals. Not only the location of firms matters, but also the contacts between firms competing in the same markets. The multimarket contact theory implies that more contacts between firms competing in the same markets may induce more collusion. This collusion support prices above the equilibrium prices. At this regard, Coronado et al. (Coronado et al., 2007) try to predict the effect of multimarket contact structure on the equilibrium prices under two different regimes, the price regulation and the free pricing, and ultimately to know if price regulation may affect multimarket contact effect. For this, the authors propose a game infinitely repeated where prices are set simultaneously. The firms can collude and support prices above the equilibrium prices. In case of deviation, the firms will be penalized reverting to the equilibrium prices. It is also supposed that the maximum sustainable price (in collusion) depends positively on the discount factor, i.e., the future profits are more valuable, and therefore the short run benefits from deviation are accordingly less preferred. Taking into account the hypothesis above described, the model predicts that the effect of multimarket contact structure increases the equilibrium prices but this effect is undermined in regulated countries. In summary, the theoretical models predict that both the firm location and the multimarket contact not only affect drug pricing but also, their effects depends on price regulation regimes. 1.2.6 Which effects do arise in pricing and innovation when countries apply internal RP20? Danzon and Epstein (Danzon and Epstein, 2008), extending the paper of Danzon 20 Internal reference pricing, as opposed to the ERP, compares product prices within a single country.
18 Modeling Global Pricing and Launching of New Drugs et al. (Danzon et al., 2005), they consider as influencing factor the RP as regulatory regime being expected to positively affect on prices. At this regard, two papers examine, on the one hand, how the RP policy affects the equilibrium prices and the firm pricing strategies (Miraldo, 2009), on the other hand, how the RP policy influences the intensity of research and the introduction of new pioneer in the market (Bardey et al., 2010). Both papers compare the outputs under no regulation and under RP policy. Furthermore, other authors have deeply studied the RP policy from an international perspective (LopezCasasnovas and Puig-Junoy, 2000, Puig-Junoy, 2010a, Puig-Junoy, 2010b) and particularly the Spanish case (Mestre-Ferrandiz, 2003b, Moreno-Torres et al., 2009, PuigJunoy, 2007) The model developed by Miraldo (Miraldo, 2009) considers two pharmaceutical firms, a continuum of consumers uniformly distributed and a market of drugs horizontally differentiated à la Hotelling (Hotelling, 1929). Each firm produces two distinct variety of drug. Each consumer is assumed to have a most preferred drug that is given by her location on the line segment. Indeed, the constant marginal cost of distance is the loss in utility incurred by a consumer. Miraldo studies the explicit RP formulations and considers a different timing of implementation of the policy. The author analyses a finite dynamic game, in which duopolists compete by non-cooperatively setting prices in two subsequent periods. In the first stage, the two pharmaceutical firms set the prices for one variety. At the beginning of the second, the government fixes the RP level, and then, the firms set prices for the second variety of drugs. Therefore, at the last stage, the firms’ profits depend via demand not only on the pricing strategies but also on the RP level fixed previously by the government. The RP policy is introduced as reimbursement scheme21. Miraldo shows that under the RP policy, the equilibrium prices are at least as high as the equilibrium prices without RP. As a main contribution, when both RP rules are compared, the minimum and the weighted average, the author states that firm set higher prices at first stage when the 21 In countries, such as Germany and Spain, where pharmaceuticals are reimbursed through a RP system, patients are typically reimbursed a lump sum amount for any homogeneous pharmaceutical cluster, independently of the drug variety bought. There are several criteria to cluster drugs and the replicated model applies to countries that use chemical and therapeutic criteria. The first criterion clusters drugs with the same active ingredient and therefore refers to patent expired drugs. The second criterion clusters drugs that have the same therapeutic function and therefore, within the same cluster one can find patent protected drugs.
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 19 RP is calculated as a weighted average than when the minimum policy is applied. The firm pricing strategy in the second period (when already the RP level has been fixed) will depend on the weights of each price. If they are high (i.e., for sufficiently high and low values of the weights), the minimum policy makes firms to fix lower prices than the weighted average rule. But if the weights are similar, results will be ambiguous; they will depend on consumers’ preferences, on the degree of horizontal differentiation and on the discount factor. Anyway, in a symmetric market, in order to avoid higher prices, the regulator should implement a policy where the reference pricing consists of the minimum observed price. In turn, Bardey et al. (Bardey et al., 2010) evaluate the long run impact of RP on pharmaceutical innovation and on health expenditure. The paper is based on a dynamic model with three players: the firms (innovators/producers), the regulator and the consumers22. Both horizontal and vertical differentiations are considered. Vertical differentiation has different levels (therapeutic classes). To simplify, there are two levels, C and N, designing respectively current and new. Patients obtain utility from the treatment of the drug, perceive its side effects, pay a price for the drug and receive a reimbursement. They also assume that drugs are produced at zero cost and when a drug is introduced in the level N, producers of level C have no sales. The price negotiations are developed à la Rubinstein (see Rubinstein (Rubinstein, 1982)). The firms choose the level of research investment, and then negotiate introductory prices for new drugs with the regulator. The innovation process is deterministic and can discover a new product either in the same level as existing products (horizontal innovation or follower), or in a superior level (vertical innovation or pioneer). There exist a cost of bringing an innovation. Bardey et al. authors compare how the dynamic of innovation behaves without RP and under RP. Thus, in terms of delay of introduction, the application of RP yields the delay of followers, and the delay of pioneers if only if the price is above some threshold. In the long run, allowing innovation to occur in level C (prior to the discovery of the first level N drug), the follower may be never introduced (short sequence), or it can be introduced before the pioneer of level C (long sequence). Again, both sequences are 22 The relation between the patient and the physician is considered a relation of perfect agency; therefore they are viewed as a single agent, the consumer.
26 Modeling Global Pricing and Launching of New Drugs countries, as mentioned above, the elasticity is significantly positive. This is consistent with the hypothesis that introducing line extensions is one means of achieving a price increase in countries that do not permit higher prices for established products. The price elasticity with respect to the number of forms is largest in Japan, which presents the greatest price reduction over the product life cycle and hence where there exist strong incentives to introduce new forms and thus obtain a higher price (Danzon and Chao, 2000). In addition, Danzon and Chao show that the global diffusion28 as an indicator of therapeutic value obtains higher prices in unregulated markets, although this effect is insignificant or small at best in less regulated markets such as the UK, Canada and Germany, but is significantly negative in strictly regulated countries such as France, Italy and Japan (Danzon and Chao, 2000). Besides, Coronado et al. show that the product market share is expected to be significantly positive only in the least regulated countries. 1.3.2.2 Competition and substitutes In general, therapeutic substitutes do not appear to exert competitive pressure on price. Danzon and Chao (Danzon and Chao, 2000) show that, competition from therapeutic substitute molecules appears to have small significant negative effects in France, Italy, Germany and the UK, but the interpretation is unclear. The most plausible explanation is that regulators use implicit reference pricing, setting prices for new products based on prices of established products (Danzon and Chao, 2000). Timur et al. do not find significant effect on price from therapeutic substitutes, however, different from Danzon and Chao (Danzon and Chao, 2000). Timur et al. estimate the pool of data and do not show specific results for countries. In this case, Timur et al. suggest that substitute molecules with a higher price might not receive reimbursement, and substitution is not always possible in regulated countries because of prescribing or consumption preferences. Furthermore, the effect of delayed entry for therapeutic substitutes has been also analysed, and the coefficients obtained for the USA and Canada imply that successive molecules enter at lower prices, however these lower prices are not sufficiently low to fully erode the first mover’s advantage. Other country interactions are generally positive, but significant only in Germany. On the contrary, only Verniers et al. (Verniers et al., 2011) find that competition 28 The number of countries where the medicine has already been sold.
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 27 drives down launch prices when estimating the pool of data. In this case, this different result can be supported by the use of a richer sample. Furthermore, Danzon and Epstein collect data for superior and inferior medicines. They state that while the prices of competitors are positively related to launch prices, the prices of inferior medicines do not affect those of superior ones, and vice versa, which means that dynamic competition between subclasses is based on non-price product attributes (Danzon and Epstein, 2008). For middle and low income countries (MLICs), Lanjouw finds that competition from other originator medicines does not appear to be effective at reducing prices in retail channels in these countries (Lanjouw, 2005). Most papers found generic competition to negatively affect prices (Cabrales and Jimenez-Martin, 2007, Coronado et al., 2007, Danzon and Chao, 2000, Danzon and Epstein, 2008, Danzon et al., 2011, Timur et al., 2011). However, Danzon an Epstein (Danzon and Epstein, 2008) only find that the effect of generic prices in inferior class, which may reflect a selection effect: late entrants in inferior subclasses launch only if they expect to receive high prices relative to competing generics. In turn, Cabrales and Jiménez-Martín, and Coronado et al. (Cabrales and Jimenez-Martin, 2007, Coronado et al., 2007) find a significant and negative effect in a large majority of countries. However, the presence of generics on a market does not mean that brand name products will reduce their prices. According to Coronado et al., in some cases, the presence of generics will have the impact of concentrating brand name products over the inelastic portion of the demand, which will then increase the price of these products. Hence, the expected sign of the number of generics will be positive29. Interestingly, Danzon and Chao (Danzon and Chao, 2000) find that generic competition is significant in unregulated or less regulated markets but regulation undermines generic competition in strict regulatory systems. As Coronado et al. (Coronado et al., 2007), Danzon and Chao find positive effects of generic competition but they explain that multi-source suppliers in these countries are usually licensed co-marketers rather than competing generic manufacturers or minor ‘‘new’’ products that enter to obtain a higher regulated price. Danzon et al. (Danzon et al., 2011) find that in MLICs the number of generic competitors only weakly 29 The US, Germany, The Netherlands, the UK and France.
28 Modeling Global Pricing and Launching of New Drugs affects prices to retail pharmacies, may be because uncertain quality leads to competition on brand rather than price. Contrary, tendered procurement attracts multi-national generic suppliers and significantly reduces prices for originators and generics, compared to prices to retail pharmacies. Only Kanavos and Vandoros (Kanavos and Vandoros, 2011) find that generics is non-significant. They point out the existence of the generics paradox, particularly in the US, where prices of off-patent originator brands do not decline post-patent expiry, but, rather, increase faster than prices of in-patent originator brands. 1.3.2.3 Regulation Characteristics When examining price regulation regimes through explicit regulation variables, the most of the literature does not find significant influences on prices (Kanavos P and CostaFont J, 2005, Verniers et al., 2011, Kanavos and Vandoros, 2011, Danzon and Epstein, 2008, Atella et al., 2012). Only Kanavos et al. find that countries with “free-pricing” systems (the US and Germany) present higher prices significantly positive. As explicit price regulation policy, they only find that the explicit use of HTA (Health Technology Assessment) has a significant negative effect on prices (Kanavos and Vandoros, 2011). In this sense, Atella et al. proposes that rather than higher or lower prices, under a price control regime (as in Italy), there is greater price variability than in a free price regime (as in the US) (Atella et al., 2012). Although not introducing explicit regulation variables in the econometric models, Danzon and Epstein and Atella et al. (Atella et al., 2012, Danzon and Epstein, 2008) retrieve information from price regulation characteristics. In this sense, Atella et al. find a positive relationship between quality and price in a free price regime (as in the US), but also find a negative relationship between drug price and drug quality in a price control regime (as in Italy), which suggests that price regulations have created perverse incentives (Atella et al., 2012). Also, Danzon and Epstein interestingly observe that launch prices increase with the level of the lowest price previously received in other high-price EU countries, whereas the effects of a previous launch in low-price EU countries are insignificant. This is also true for non-EU countries, but only for superior medicines. This result is consistent with the hypothesis that launching first in high-price EU markets can influence prices in low-price ones. That evidence about a sequential launch prices validates the theory that a launch delay in low-price markets may ultimately yield higher
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 29 prices in these markets through spillovers from higher-price ones (Danzon and Epstein, 2008). This theory is shown by Stargardt and Schreyögg (Stargardt and Schreyögg, 2006). They develop an analytical model in their paper, which analyses direct and indirect impact due to the use of ERP from the referenced to the referencing country. The authors estimate the impact of drug price changes in Germany30 on drug prices in other countries using ERP in the former EU-15. The authors use the formulas applied by each referencing country and then, they calculate the partial differential of these formulas with respect to a 1 euro price reduction in Germany. They do not only know the formula (the average, the minimum, a percentage, etc. see Leopold (Leopold et al., 2012)) but also the basket of countries used by each referencing country. They differentiate the direct impact (caused by Germany to other countries) and the indirect impact (caused by the referencing countries that have taken Germany in their baskets, to other referencing countries). The authors state that the relationship between the direct and indirect impact of a price change depends mainly on the scheme applied to set prices. For instance, the price is either determined by the lowest of foreign prices (e.g. Portugal), the average of foreign prices (e.g. Ireland) or a weighted average of foreign prices (e.g. Italy). If the respective drug is marketed in all referenced countries and prices are regularly updated, a price reduction of €1.00 in Germany will reduce prices in the former EU-15 countries from €0.15 in Austria to €0.36 in Italy. Whether we distinguish between direct and indirect impact, almost more than (about) the 50% of the total impact in Austria comes from the indirect impact (0.08 euros), however, only 1% of the total impact in Italy is due to the indirect impact (0.03 euros). Both Austria and Italy include 14 and 12 countries in their ERP scheme respectively, but we observe how Italy, which uses a weighted average, is less harmed by undue indirect impact. Thus, to avoid the negative effects of ERP and determine prices in order to reduce the direct and indirect impact of individual countries, a weighted formula of prices containing as many countries as possible should be used. Surprisingly, the literature does not find a direct effect on pricing when ERP is applied. This result is robust among different specifications, but it could be explained by the database collected. In Verniers et al. (Verniers et al., 2011), the database was limited to the drugs launched as of February 1994 but the ERP had not been widely applied by 30 Germany is chosen because is one of the largest pharmaceutical markets in the world, it is characterised by relatively high prices and it is referenced by most referencing countries scheme.
30 Modeling Global Pricing and Launching of New Drugs that time (Leopold et al., 2012). In Kanavos and Vandoros (Kanavos and Vandoros, 2011), the prices of branded originators do not correspond to launch prices; therefore, competition can also lead to downward pressure of off-patent originator brands, but the ERP is applied on new drugs at launch time. On the other hand, when the ERP is found to have effect on launching, the database comprises prices at launch time between 1995 and 2005. Even more, Kanavos and Costa-Font have found that PT does not influence prices downwards in importing countries (Kanavos P and Costa-Font J, 2005). Furthermore, Danzon and Epstein show that the presence of PT is insignificant for superior medicines, but significant and negative for inferior ones, indicating that the presence of PT reduces launch prices mainly for late entrants into older subclasses (Danzon and Epstein, 2008). Furthermore, Danzon et al. make an interesting contribution for MLICs concerning price regulation policies. They compare the two different ways to provide medicine in MLICs: tendered procurement mechanism by NGOs and standard retail channels. In these terms, they find that originator brands purchased through tendered procurement mechanisms by NGOs tend to lower originator prices, compared to those obtained through standard retail channels. These large procurement effects may reflect not only price-competitive tendering but also a greater willingness of originators to grant discounts to a separate distribution channel that targets lower income customers and is less prone to price spillovers to other countries (Danzon et al., 2011). 1.3.2.4 Country Characteristics In most studies, country characteristics are included in the econometric models in order to capture price differences among countries. The variable most commonly included is that of country GDP per capita (Cabrales and Jimenez-Martin, 2007, Danzon and Epstein, 2008, Borrell, 2007, Danzon et al., 2011). This is coherent with the generally accepted positive relationship between wealth and greater willingness to pay. Thus, the country-level fixed effects controlled by GDP per capita show that although the lowerincome EU countries such as Spain, Portugal and Greece regulate medicine prices, they have relatively high medicine prices with respect to GDP, whereas higher-income EU countries have lower medicine prices relative to their per capita GDP. Then, Danzon and Epstein suggest that ERP has contributed to the price convergence of medicines among EU countries relative to GDP (Danzon and Epstein, 2008). In turn, Kanavos and
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 31 Vandoros (Kanavos and Vandoros, 2011) state that as we move towards newer molecules over time by launch date, there is upward price convergence across the study countries overall. This is partly explained by ERP and the launch sequence for new products, whereby new products are first launched in less-regulated countries followed by price-regulated countries. This launch sequence influences in part the final price in priceregulated countries. According to the convergence above mentioned, Cabrales and Jiménez-Martín (Cabrales and Jimenez-Martin, 2007) observe that the US does not present higher prices than other countries. Contrary to the conventional wisdom about countries regulatory regimes, the fixed effect of the US is significantly lower than that of Canada (but not for all specifications), France or Italy. The authors interpret that if average prices in the US are higher than in other countries it is not because other countries engage in “free riding regulation”, but because the US per-capita income is higher, in fact, in many cases, the US pays less, not more, than countries of similar income or lower income (such as Eastern European countries). The authors also interpret that as the US market size is larger and more competitive than the other countries, it provides with some protection with respect to similarly rich countries. This contradictory result may be result of the innovations introduced with respect to the previous empirical literature in the subject. Mainly, they estimate pricing equations separately for each country, they do not restrict the sample in any way and identify the effect of time-invariant variables by following a two-stage procedure (Cabrales and Jimenez-Martin, 2007). For MLICs, Borrell shows evidence of a persistent positive relationship between drug prices and per capita income in MLICs (Borrell, 2007). In addition, the income distribution within countries has also been considered by Danzon et al. and Borrell (Borrell, 2007, Danzon et al., 2011). Borrell suggests that income effects alone are unlikely to achieve affordable prices in low-income countries. Thus, although per capita income effects are positive, the negative effect of the income distribution implies that the poorest countries face with the highest relative prices. Moreover, skewed income distributions appear to exacerbate high drug prices relative to per capita incomes in MLICs (Danzon et al., 2011), however, Borrell reports ambiguous effects (Borrell, 2007). 1.3.2.5 Firm Characteristics Some studies have examined how certain firm characteristics may influence prices
32 Modeling Global Pricing and Launching of New Drugs (Cabrales and Jimenez-Martin, 2007, Coronado et al., 2007, Danzon and Epstein, 2008, Kanavos and Vandoros, 2011, Verniers et al., 2011). Coronado et al. show that the firm size have a high significant positive effect on prices, indicating that large companies enjoy higher prices either because its products are of higher quality or perceived as such (Coronado et al., 2007). However, Cabrales and Jiménez-Martín find this effect significant and negative, but small (Cabrales and Jimenez-Martin, 2007). As the definition variable and the sample is the same, except the number of countries considered, we think that the use of more variables concerning the firm characteristics may undermine this issue. Another question widely mentioned concerns the type and location of the firm. Danzon and Epstein do not find price premium for medicines launched by local firms (Danzon and Epstein, 2008). In addition, Cabrales and Jiménez-Martín firstly check differences in location for multinational firms between local and foreign multinationals, and contrary to conventional wisdom, in most countries, is found that countries do not distinguish between local and foreign multinationals. However, Verniers et al., different from former results, find that firms obtain higher launch prices in their domestic market than they do in foreign ones (Verniers et al., 2011). These different findings may be due to differences in samples. Verniers et al. have collected a richer sample of countries where we can find more MLICs where usually headquarters are not held, while the most of countries collected by Cabrales and Jiménez-Martín and Danzon and Epstein are middle and high-income countries (Cabrales and Jimenez-Martin, 2007, Danzon and Epstein, 2008). Additionally, Cabrales and Jiménez-Martín also compare between local nonmultinational firms and multinational firms and find that local non-multinational firms tend to have lower prices than any multinational (Cabrales and Jimenez-Martin, 2007). Furthermore, as in the theoretical models, Coronado et al. analyse the existence of a multimarket contact effect and try to find whether more contacts between firms competing in the same markets may induce more collusion. They empirically show that multimarket contacts have a positive influence on prices for the firm in less regulated countries31, and unstable effects in regulated countries32. This suggests that in more regulated markets there exist distortions that interact with market forces. For instance, the product market share is significant and positive only in the least regulated countries. 31 The US, Canada, Germany, the Netherlands and the UK. 32 France, Spain, Italy and Japan.
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 33 Moreover, reducing prices in more competitive markets compared to the existing level may discourage entry and may have a negative dynamic effect in the development of the industry. This may help predict the undesirable effects of public interventions (Coronado et al., 2007). 1.3.3 Factors influencing launching 1.3.3.1 Drug Characteristics As expected, potential prices and volumes positively affect launching (Danzon et al., 2005), although according to Danzon and Esptein, the volume is not significant (Danzon and Epstein, 2008). This insignificant effect of volume contrasts with significant positive effects in Danzon et al. (Danzon et al., 2005). These different findings may reflect differences in sample countries and drugs, in addition to the use of more detailed measures of country-class prices and other characteristics. As previously found for prices, the speed of launch increases with the medicine’s importance33 but falls with its age (Kyle, 2007). Furthermore, peculiarities have been found depending on the type of medicine evaluated. Thus, there are significant differences among therapeutic classes (Danzon et al., 2005, Verniers et al., 2011), for instance, there is a higher probability of never launching for inferior medicines than for superior ones (Danzon and Epstein, 2008). Interestingly, Verniers et al. observe an U-shaped effect of launch price on the launch delay; launch price is highest at moderate launch delay. As expected, at very long launch delay, we will expect a relatively low launch price as a prelude to generic competition. This relationship shows the trade-off for the pharmaceutical firm between the price and the launch delay (Verniers et al., 2011). Regarding the international context, from the first global launch the launch hazard pattern is first decreasing and then increasing34. This idea is generally accepted; there is a threshold at which the firm will not be worried about the spillover effects from the ERP and the presence of PT. On the other hand, the number of countries where the medicine has already been launched affects significant and positively, with the exception of prior 33 Drug’s share of Medline citation for therapeutic class. 34 Quadratic effect.
34 Modeling Global Pricing and Launching of New Drugs launch in the three lowest price EU countries, Spain, Portugal and Greece. This pattern confirms that firms delay the launch in low-price EU countries until it has taken place in higher-price ones (although this is not the case for inferior medicines). Furthermore, a prior launch in a high-price country has a stronger effect on launching in a low-price one than vice versa. This effect is even larger when the countries are both EU members (Danzon and Epstein, 2008). 1.3.3.2 Competition and substitutes With respect to market factors, the launch hazard is positively affected by competitor prices (Danzon and Epstein, 2008, Kyle, 2006, Kyle, 2007), although it does not seem to influence when the endogenous variable is studied as launch window instead of launch hazard (Verniers et al., 2011). These differences may come from the definition of the variable to measure the competition. Verniers et al. construct a Herfindahl– Hirschman index35 for each drug in each country, however, Danzon and Epstein use the competitor prices and Kyle includes the measure by Djankov et al. (Djankov et al., 2002). Furthermore, cross-class price effects are insignificant, indicating that competition occurs within subclasses rather than between subclasses, and that dynamic competition is driven by product characteristics other than price (Danzon and Epstein, 2008). Concerning generic competition, Danzon and Epstein (Danzon and Epstein, 2008) find that the effects of number of generic competitors are negative but statistically insignificant, providing further evidence that availability of older, cheaper generic substitutes is not a significant deterrent to the launch of new brand products, even in older subclasses where generics are more numerous, possibly because generic substitution is mostly within molecules rather than between molecules. 1.3.3.3 Regulation Characteristics The question of regulation has been widely analysed. The most common finding is that price regulation tends to produce a launch delay (Danzon et al., 2005, Kyle, 2006, Kyle, 2007, Lanjouw, 2005). When price regulation reduces prices below the level expected given a country’s per capita income, this problem is exacerbated and launch 35 This index is constructed by summing the squared market shares (MS) (based on revenues in the IMS Health data) of the m drugs in the same ATC4 category as drug i at the time of launch of drug i in country j.
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 35 delays may extend even to high-income countries. Controlling for expected price, the models show that such delays have been observed in countries with strict regulation and in those traditional parallel exporters, either performing negative country fixed effect (Danzon et al., 2005), or directly introducing dummy variables concerning price regulation (Kyle, 2006, Kyle, 2007, Lanjouw, 2005) or via the average price competitors in the country (Danzon and Epstein, 2008). Various degrees of regulation have been explored. Lanjouw (Lanjouw, 2005) examine separately high and low-income countries. For high-income countries, all price regulation – whether moderate or extensive – tends to reduce the probability of a medicine being launched within two years after first launch, while for lower-income countries, extensive price controls clearly lower the probability of new medicines reaching consumers quickly. On the other hand, moderate price control does not appear to have a significant influence on entry in this case. However, as in the richer countries, the effect of moderate price regulation depends on a country’s income level. For example, in lowincome countries the existence of a national formulary positively affects launch hazard (which is not the case for the higher-income countries). One would expect its direct effect to be negative, but within the lower-income country group this variable may be acting as a proxy for bureaucratic competence. On the other hand, Kyle (Kyle, 2007) and Verniers et al. (Verniers et al., 2011) show that entry actually appears more likely in countries using internal RP. Both papers show that direct price controls are not significant factors on launch delay or do affect negatively the drug launch hazard. Therefore, there is some evidence that indirect controls may be preferable to direct ones from the standpoint of attracting new medicines. Kyle (Kyle, 2007) also suggests that the effect of price controls is not restricted to an individual market, but affects the launch of a medicine in other markets as well. From this idea, Heurer et al. (Heuer et al., 2007) introduce the ERP as explanatory factor and find that countries using ERP to determine their prices present a significantly lower probability of launch within the first eight months. Specifically, the two forms of international comparison based on a formula of foreign prices – either determining prices directly from such a formula, or using it as an informal basis for the decision – have impacts that are negative but different. This difference may derive from the fact that, if a
42 Modeling Global Pricing and Launching of New Drugs (Ganslandt and Maskus, 2004, Richter, 2008). Furthermore, in the presence of informational spillovers on quality, the launch delay should occur in the non-aggressive country. Regarding the firm location, multinational firms should settled down in large countries in order to better compete with local multinational firms (Cabrales and JimenezMartin, 2007). On the other hand, the empirical literature collects an amount of econometric models which have identified and measured the influence of the most significant factors affecting prices and launches of medicines in different countries. The use of different samples may prevent from doing comparisons. Demographic and income country features, and regulation regimes, seem to be the most important factors affecting drug pricing and launching in the empirical literature. Moreover, the drug characteristics as strength, packsize and presentation forms are significantly related to the price, but it is the therapeutic value, which robustly affect pricing and launching. The firm location turns up an important factor for the launching decision but price premiums due to headquarters location appear ambiguous. Generic competition generally drives down prices; however, there are some different effects, which deserve some comments. In turn, the brand competition factors do not appear to exert competitive pressure on prices. The country size, the GDP per capita and income distribution are three country characteristics shown as the most significant factors influencing pricing and launching. Ceteris paribus, higher-income countries pay higher drug prices (Cabrales and JimenezMartin, 2007, Danzon and Epstein, 2008) and have a more rapid access to medicines than lower-income countries (Danzon et al., 2011, Heuer et al., 2007, Kyle, 2007, Lanjouw, 2005); furthermore, populated countries also enjoy a higher probability of launch (Heuer et al., 2007, Kyle, 2007, Lanjouw, 2005, Verniers et al., 2011). Additionally, an unequal income distribution positively affects the drug launch hazard in lower-income countries via a wealthy “elite”. On the other hand, a more equal distribution makes the same effect in high-income countries via a largest “middle class”. However, we should note that the country pricing policies might undermine the effects of these important factors. Contrary, interestingly, Cabrales and Jiménez-Martín observe lower prices in the US than in other countries as Canada, France or Italy, and contrary to the conventional wisdom about countries regulatory regimes (Cabrales and Jimenez-Martin, 2007).
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 43 In the empirical review, the most common finding is that price regulation tends to produce launch delay. Such as delays have been observed in countries with strict regulation and in traditional parallel exporters. Particularly, Heurer et al. show that countries using the ERP have a lower probability of launch (Heuer et al., 2007), and Stargardt and Schreyögg state that the use of ERP have positive direct and indirect impact on the referencing countries. At this regard, they propose to use as many countries as possible in the formula (Stargardt and Schreyögg, 2006), which partially coincides with the recommendations from the theoretical model of García-Mariñoso et al. (Garcia Mariñoso et al., 2011). However, Stargardt and Schreyögg further recommend avoiding countries using ERP, in order to prevent undue impacts; and integrate the market volumes of the referenced countries into the index in order to avoid high prices and launch delays in countries with small markets (Stargardt and Schreyögg, 2006). In turn, Kyle shows that there is evidence from the standpoint of attracting new medicines that indirect price controls such as RP may be preferable to the direct ones such as pharmacoeconomic evidence or price freeze (Kyle, 2007). Furthermore, the belonging to the EMA make launches more likely, particularly in higher-price countries (Kyle, 2007, Verniers et al., 2011). On the other hand, as explicit price regulation, only the explicit use of HTA has a significant effect on prices (Kanavos and Vandoros, 2011). The therapeutic quality, the strength, the pack size, the number of presentations number of the drug and the product life cycle are very significant factors on pricing. The therapeutic quality, the strength and the number of presentations make increase the price, the pack size and the product life cycle affect negatively (Cabrales and Jimenez-Martin, 2007, Coronado et al., 2007, Danzon and Chao, 2000, Kanavos and Vandoros, 2011, Timur et al., 2011, Danzon and Epstein, 2008). Also, the literature importantly considers the sequential launch of a drug and the effect on its price. Commonly, the higher are the prices previously set, the higher will be the drug price in a country. We note that when there is no average global price40, what can be interpreted as a drug innovative character, the price is higher than when previous prices exist (Cabrales and Jimenez-Martin, 2007). Therapeutic innovation also influences positively the launching. It has been observed that a prior launch in a high-income country has a stronger effect on launching in a low-income 40 There is no mean global price because there are no countries to be calculated.
44 Modeling Global Pricing and Launching of New Drugs country than vice versa. Also, the fact of launching first in high-price EU markets positively affects launch prices in the low-price ones via ERP (Danzon and Epstein, 2008). Although the papers analysed appear to agree on drug characteristics, differences in therapeutic categories (Danzon and Epstein, 2008, Danzon et al., 2005, Kyle, 2007, Lanjouw, 2005, Verniers et al., 2011). From the perspective of the firms, it has been shown that all multinational firms obtain a price advantage. By contrast, domestic firms tend to enter the market with short delays (Danzon and Epstein, 2008, Danzon et al., 2005, Kyle, 2006, Kyle, 2007, Verniers et al., 2011), however, it is not clear that they receive price premiums (Cabrales and Jimenez-Martin, 2007, Danzon and Epstein, 2008, Verniers et al., 2011). These last results do not support the theoretical model that predict that local-multinational firms receive price premiums compared to foreign multinational ones (Cabrales and JimenezMartin, 2007). Only Verniers et al. find that domestic firms obtain higher prices in their domestic markets, but they do not make differences between multinational and nonmultinational firms. In any case, the literature shows that the contact among firms in unregulated markets induces higher prices through price collusion (Coronado et al., 2007). Most papers find generic competition to negatively affect prices (Cabrales and Jimenez-Martin, 2007, Coronado et al., 2007, Danzon and Chao, 2000, Danzon and Epstein, 2008, Danzon et al., 2011, Timur et al., 2011). Interestingly, price regulation is found to undermine generic competition in strict regulatory systems. However, the presence of generics may increase the price of brand names products (Cabrales and Jimenez-Martin, 2007, Coronado et al., 2007). In general, except for the case of tendering procurement in MLICs, brand competition does not seem to exert a significant competitive pressure on prices, to the contrary, several authors find that brand competitor prices affect positively launch hazard (Danzon and Epstein, 2008, Kyle, 2006, Kyle, 2007). Only Danzon and Epstein indicate that competition occurs within subclasses rather than between subclasses, and that dynamic competition is driven by product characteristics other than price (Danzon and Epstein, 2008). Contrary to the theoretical model that predicts that the presence of PT reduces prices in high-income countries (Ganslandt and Maskus, 2004), the empirical literature
Chapter 1: Global Pricing and Launching of New Drugs: What Does the Theory Say? What Do the Empirical Models Show? 45 does not find robust effects on it (Kanavos P and Costa-Font J, 2005). By contrast, PT risk is more likely to lead to non-launch or launch delay in the parallel export while it has a lower impact on the importing country.
2 Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment. 2.1 Introduction Pharmaceuticals are sold on a global market. This characteristic gives rise to a specific bargaining procedure between pharmaceutical firms and countries’ health agencies. On the one hand, a firm makes strategic decisions to sequentially launch medicines in different countries and to maximize global profits; and on the other hand, countries’ health agencies implement pricing policies in order to control their pharmaceutical expenditure and yet guarantee access to medicines. Among existing drug pricing policies, most countries in the industrialized world have implemented either Cost-Effectiveness Analysis (CEA) or External Reference Pricing (ERP) at some point in time with the aim of controlling pharmaceutical expenditure but still ensuring access to medicines, mainly for on-patent medicines (Espin J et al., 2011, Rawlins, 2012). According to the OECD, ERP, also referred to as External Price Benchmarking or International Reference Pricing, is defined as “the practice of comparing pharmaceutical prices across countries” and it is further indicated that, “there are various methods applied and different country baskets used” (Paris et al., 2008). In this thesis, we use the ERP definition from Garcia-Mariñoso et al. (Garcia Mariñoso et al., 2011): “ERP consists of setting a price cap for pharmaceuticals, based on ex-manufacturer prices of identical or comparable products in other countries”. ERP is not applied homogeneously in every country. There are a wide variety of methods used to design a foreign price index (Leopold et al., 2012, Espin J et al., 2011). It
48 Modeling Global Pricing and Launching of New Drugs mainly depends on each country’s basket, date of prices41, the method used (the lowest price, the average price, a percentage of the previous ones, etc.) and whether a weighted-index42 is used or not. We also note that some countries take into account ERP as a complementary pricing policy together with other pricing policies to help to make the price decision, thus it is not exclusively applied as a blind pricing policy43. ERP is used because of its simplicity at technical or analytical level; it does not require a huge task to collect price information abroad. Furthermore, ERP users think that those prices taken as a reference are approximately right, suitable or fair. However, they mention that it is difficult to assess if the resulting prices are appropriate, efficient or optimal in accordance with any objective criterion. Therefore, if referencing countries set their prices too high or too low, then any country later applying the ERP method may run the risk of repeating the same mistake (Espin J et al., 2011). The basic trade-off faced by a pharmaceutical firm is the following. If a firm delays a launch in a referencing (low-price) country, it will also delay profits that could be derived from this country. However, on the positive side, it avoids this low price from overspilling into other countries due to ERP strategy or parallel trade (Danzon and Epstein, 2008, Danzon and Towse, 2003, Garcia Mariñoso et al., 2011). By contrast, when countries set a drug price, they risk the possibility of not providing the drug at the time they desire, which may have consequences for the health and the welfare of the population (Lichtenberg, 2005). The use of ERP by countries may make firms apply international pricing strategies that can harm countries’ welfare. On the one hand, a firm may set a single price44 which may benefit the high-priced45 countries but harm the low-priced ones, 41 Current price vs. price at launch 42 The most widely method used for new drugs is through non-weighted measures; such methods will not help to achieve the target of obtaining a comparable average level of prices. The application of weighted price indexes, comparable and useful as reference to the rest of countries, has been proposed DANZON, P. M. & CHAO, L. W. 2000. Cross-national price differences for pharmaceuticals: How large, and why? Journal of Health Economics, 19, 159-195.. 43 Espín et al. state that “regulators might not always be able or willing to “impose” a certain price, but instead use the price computed as a benchmark or reference for negotiations, often alongside other criteria, such as cost-plus, internal or therapeutic pricing” 44 Two factors contribute to price uniformity between different markets: a) threats of parallel imports, and b) the use of international reference pricing DANZON, P. M. & TOWSE, A. 2003. Differential Pricing for Pharmaceuticals: Reconciling Access, R&D and Patents. International Journal of Health Care Finance and Economics, 3, 183-205.. 45 In the long run, consumers from high price countries will be worse off if this lower price results in lower expected returns on R&D, and hence fewer new medicines than they would have been willing to pay for DANZON, P. M. 1997. Price Discrimination for Pharmaceuticals: Welfare Effects in the US and the EU. International Journal of the Economics of Business, 4, 310-322..
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 49 and on the other hand, the firm may either attempt to set high46 prices in first countries to avoid low prices in later launches via ERP, or delay launches in low-priced countries to avoid the spill-over effects. These strategies may harm low-priced countries, and they may even harm high-priced ones (Garcia Mariñoso et al., 2011). Also, another strategy exists for firms to avoid spill-over effects from ERP. This consists of setting high prices and granting confidential rebates or discounts to referenced countries. This strategy allows firms to guarantee lower prices in referenced countries and avoids information spill-overs of low prices to referencing countries (Espin J et al., 2011). Most countries use ERP as a pharmaceutical pricing strategy. The use of ERP as a mechanism to set pharmaceutical prices is quite widely applied: 24 of the 30 OECD countries (Espin J et al., 2011) and approximately 24 of the 28 EU Member States (Leopold et al., 2012) have used it. CEA in health economics aims to estimate the ratio between the cost of a healthrelated intervention and the benefit it produces in terms of the number of years lived in full health by the beneficiaries. Cost is measured in monetary units, while benefit needs to be expressed as a gain in health measured by quantitative values. However, unlike cost– benefit analysis, the benefits do not have to be expressed in monetary terms. In pharmaeconomics, it is usually expressed in quality-adjusted life years (QALYs)47 (National Insitute for Health and Care Excellence (NICE), 2010). The ICER is the ratio between the difference in costs and the difference in benefits of two interventions. So, an example in which the costs and gains, respectively, are 140,000 euros and 3.5 QALYs, would yield an ICER of 40,000 euros per QALY. Commonly, each country has a different threshold to pay for one QALY. If we suppose that such a country has a threshold of 30,000 euros per QALY, any drug which has an ICER of more than 30,000 euros per additional QALY gained is likely to be rejected and any drug which has an ICER of less than or equal to 30,000 euros per extra QALY gained is likely to be accepted as costeffective (WHO, 2003). However, drugs do not always yield a single ICER. We note that other authors have studied in depth CEA according to Bayesian models (Negrin and 46 This company strategy will not work if the high-price country revises its prices downwards after launch 47 The QALY is a measure of disease burden, including both the quality and the quantity of life lived. The QALY model requires utility independent, risk neutral, and constant proportional tradeoff behaviour. The QALY is based on the number of years of life that would be added by the intervention. Each year in perfect health is assigned the value of 1.0 down to a value of 0.0 for being dead. If the extra years are not lived in full health, for example if the patient looses a limb, or goes blind or has to use a wheelchair, then the extra life-years are given a value between 0 and 1 to account for this.
50 Modeling Global Pricing and Launching of New Drugs Vazquez-Polo, 2006, Negrin and Vazquez-Polo, 2008, Negrin et al., 2010, Moreno et al., 2010) A pharmaceutical firm knows this threshold48. It would also carry out a CEA and obtain the number of QALYs gained if its drug were provided in one country. Since the firm is aware of both threshold and number of QALYs, it offers the country the drug at a certain price just below the threshold. However, the firm may upwardly distort the number of QALYs to obtain greater profits. Then, it is the country that may revise the firm’s CEA applying its own CEA to reveal a fair price. However, this CEA requires resources and consequently an investment of money by the country. Previous literature has developed games based on bargaining models between pharmaceutical firms and countries’ health agencies to set drug prices in an international context. Garcia-Mariñoso et al. (Garcia Mariñoso et al., 2011) examine the effects of using ERP by referencing countries on referenced countries’ welfare via a bargaining model. They find that a country has an incentive to engage in ERP if its co-payment levels are high when compared to other countries. This preference decreases as the relative size of the country engaging in ERP increases. They also find that these effects harm referenced countries’ welfare. Furthermore, García-Mariñoso and Olivella (GarcíaMariñoso and Olivella, 2012) present a negotiation model based on a “take-it-or-leave-it” procedure that examines the conditions under which a firm can use launch delay and the consequent information spill-overs49 to reject low prices. The notion that low prices may overspill to other countries even in the absence of parallel trade or ERP is introduced here differently from previous research. Furthermore, other theoretical papers (Jelovac and Houy, 2013, Richter, 2008) deal with pharmaceutical firms’ strategies and countries’ pharmaceutical pricing policies but are not based on bargaining models. In this regard, Richter (Richter, 2008) proposes 48 We note that this threshold does not have to be a single threshold. There exists currently an interesting discussion about the social value of a QALY that determines such a threshold DONALDSON, C., BAKER, R., MASON, H., JONES-LEE, M., LANCSAR, E., WILDMAN, J., BATEMAN, I., LOOMES, G., ROBINSON, A. & SUGDEN, R. 2011. The social value of a QALY: raising the bar or barring the raise? BMC health services research, 11, 8, MASON, H., JONESLEE, M. & DONALDSON, C. 2009. Modelling the monetary value of a QALY: a new approach based on UK data. Health Economics, 18, 933-950, NIHR, H. 2010. Weighting and valuing quality-adjusted life-years using stated preference methods: preliminary results from the Social Value of a QALY Project. Health Technology Assessment, 14, PINTO-PRADES, J. L., LOOMES, G. & BREY, R. 2009. Trying to estimate a monetary value for the QALY. Journal of Health Economics, 28, 553-562, ROBINSON, A., GYRD-HANSEN, D., BACON, P., BAKER, R., PENNINGTON, M. & DONALDSON, C. 2013. Estimating a WTP-based value of a QALY: The ‘chained’approach. Social Science & Medicine, 92, 92-104. 49 Information spillovers are essentially the demand for lower prices in a country generated by the knowledge about lower prices in other countries.
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 51 a mathematical optimization problem for a firm with examples in which international price dependencies play an important role. This model can help countries to understand the implication of their ERP policies on a global repeated pricing game. On the other hand, Jelovac and Houy (Jelovac and Houy, 2013) analyse the timing decisions of pharmaceutical firms to launch a new drug in countries using ERP. When all countries reference the prices in all other countries and in all previous periods of time, then there is no withdrawal of drugs in any country, and in any period of time and there is no incentive to delay the launch of a drug in any country. However, these results do not hold when the countries only reference a subset of all countries or when the reference is only on the latest period prices. Concerning empirical studies, Danzon and Epstein (Danzon and Epstein, 2008) interestingly observe that launch prices increase with the level of the lowest price previously received in other high-price EU countries, whereas the effects of a previous launch in low-price EU countries are insignificant. This result is consistent with the hypothesis that launching first in high-price EU markets can influence prices in low-price ones. This evidence about sequential launch prices validates the theory that a launch delay in low-price markets may ultimately yield higher prices in these markets through spillovers from higher-price ones. This theory is shown by Stargardt and Schreyögg (Stargardt and Schreyögg, 2006). They analyse the direct and indirect impact of the use of ERP from the referenced to the referencing country. They estimate the impact of drug price changes in Germany50 on drug prices in other countries using ERP in the former EU-15. The authors use the formulas applied by each referencing country and then, they calculate the partial differential of these formulas with respect to a 1 euro price reduction in Germany. The authors state that the relationship between the direct and indirect impact of a price change depends mainly on the scheme applied to set prices. Thus, to avoid the negative effects of ERP and determine prices in order to reduce the direct and indirect impact of individual countries, a weighted formula of prices containing as many countries as possible should be used. Surprisingly, only one out of three studies finds a direct effect on pricing when ERP is applied. These results could be explained by the different databases collected. 50 Germany is chosen because it is one of the largest pharmaceutical markets in the world, it is characterised by relatively high prices and it is referenced by most cross-referencing countries schemes.
58 Modeling Global Pricing and Launching of New Drugs ( 2.7 ) ( 2.8 ) ( 2.9 ) ( 2.10 ) country C Assumption 2 There is price variability among the J countries where the drug has been already marketed. Therefore, the average international price is higher than the minimum international price. Assumption 3 Given a country i applying CEA, the QALYs revealed by the CEA carried out by the country will not be higher than that proposed by the firm. Particularly, if the firm is honest and, therefore, declares the true number of QALYs , then the QALYs revealed by the CEA carried out by the country is equal to that proposed by the firm. However, if the firm is not honest and therefore does not give the true number of QALYs, the QALYs resulting from the CEA is lower than that suggested by the firm. p F p F P I P I min YiCEA YF i f t h e f i rmdecl a rest h etruenum b er of Q ALYs YiCEA YF otherwise Y F Y iCEA
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 59 ( 2.11 ) ( 2.1 2 ) ( 2.13 ) Consequently, the price resulting from the application of CEA will not be higher than that proposed by the firm, p iCEA p iF i f t h ef i rmtru s ted b y t h e h e a l t h a gency p iCEA p iF otherwise Then, owing to the scientific evidence showed by the CEA, assuming that the marginal cost (mc) of producing the pharmaceutical is zero (mc=0) and given that the firm is profit maximizing, the firm will always sell at CEA price53. Assumption 4 Given both countries i applying CEA, the number of QALYs revealed by the research, YiCEA,, will be the same for both countries. Assumption 5 The countries’ beliefs about the firm’s honesty are the same for both countries i (i = C,D). Assumption 6 Under assumptions 3 and 5, the price expected by the country i (i = C,D) when applying CEA is, 53 We note that the firm knows the countries i (i = C,D) WtP, therefore if it is too low (below a given threshold), the firm does not even initiate negotiations to launch in that country. P r ( Y F Y iCEA ) , i Pr(Y F Y iCEA )1 , i
60 Modeling Global Pricing and Launching of New Drugs ( 2.14 ) ( 2.15 ) ( 2.1 6 ) ( 2.17 ) Then, under Assumption 1 and 5, the price expected by country D is larger than the price expected by country C, Assumption 754 If the price proposed by the firm is higher (lower) than the international reference price, , then the price discovered by country when applying CEA will also be higher (lower). Therefore, the CEA expected price will not be lower (higher) than the international reference price. Assumption 855 The average between the average international reference price and the country C price is approximately equal to the average international reference price. Therefore, it is assumed that J is large enough that the average price is not affected by a further observation. 54 This assumption guarantees the trade-off between choosing CEA and ERP. If country i applies CEA, it will pay a higher price to avoid having the drug launch delayed. 55 The Assumption 8 makes simpler the results and does not affect the conclusions obtained. p iF (1 ) p iCEA Ep i CEA Ep D CEA Ep C CEA p F p I h p iCEA Ep i CEA If p F p I h p iCEA p I h Ep i CEA p I h If p F p I h p iCEA p I h Ep i CEA p I h
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 61 ( 2.18 ) ( 2.19 ) Given p I p j j1 J J then, p j p C j1 J J1p I with p C p I min p F p CEA C Assumption 9 The price difference between the international reference price and the price proposed by the firm is the same regardless of the type of country. Thus, the incentive to apply ERP is also the same regardless of the type of country (high or low WtP), more formally The Firm The pharmaceutical industry is characterized by high fixed costs (F) (MestreFerrandiz, 2012, Mestre-Ferrandiz, 2013) and low mc (mc=0). Hence, F > 0 stands for the fixed costs of R&D, safety approval process and marketing the drug in all countries. They are fixed and independent of the number of people or countries that use the drug. The firm sells the drug to country i (i = C, D) at price pi. This price pi is the maximum price at which the firm and the health agencies agree56. If the country’s price comes from a CEA policy, under assumption 3, the firm will always sell at CEA price. However, if the country price comes from an ERP policy, the firm will be able to accept or refuse it. Should it refuse, the firm will delay launch in such a country. Therefore the firm commits to launching the drug and to satisfying the whole demand in this country (qi) at price pi. Selling this pharmaceutical product without subsidization is not considered as an option. Thus, we assume that the objective of a monopoly producer of a medicine is to maximize 56 We are aware that purchaser bodies such as hospital or pharmacy bodies may achieve discounts from this price pi, but they are not considered in this thesis. p I min p F p I p F
62 Modeling Global Pricing and Launching of New Drugs ( 2.20 ) the accumulated profits function during the length of the sales (two periods, stage 3 and 4, see timing), which can be written as, OF F p it q it F iC D t1 2 with q it 0 i f t h e dr u g i s n o t m a rk e t e d i n c o u n tr y i i n t i m e t Also, we assume that the firm is not located in any of the countries i (i = C,D). Timing The timing of this game is as follows. The game has 4 stages. In stage 1, countries C and D choose their pricing policies, CEA or ERP, and the firm proposes a price for the drug. In stage 2, countries communicate their prices according to their pricing policies. In stage 3, as launching is sequential (say launch first in C and then in D or vice versa), the firm chooses the country launch sequence, i.e., it chooses between delaying in country D or delaying in country C, and sells the drug in the first country of the sequence. In stage 4, the firm sells the drug in the second country. Since the firm is profit maximazing, the firm sells in both countries i (i=C, D). A priori, notice that country i (i=C, D) can choose between a pricing policy that eventually requires an investment of money, CEA, and another pricing policy with no charges, ERP. However, applying ERP, the country may have the drug launch delayed if the firm the international reference price is lower than de price proposed, whereas, applying CEA, the country risks not making a useful investment if the firm gives the true number of QALYs. In order to show the timing more clearly, see the decision tree in Appendix B.
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 63 Assumption 10 Drug launching is sequential and the firm keeps selling the drug in stage 4 to the country where the drug has previously been launched. Assumption 11 If only one country i (i= C,D) applies ERP and the international reference price is lower than the price proposed by the firm, the firm will punish such a country i by delaying launch in it. If both countries i (i= C,D) apply ERP and the international reference prices are lower than the prices proposed by the firm, the firm will delay launch only in the country i (i= C,D) according to the country that offers the lowest income. 2.3 Price Setting and Sequential Launch Players maximize their objective function and we solve the game applying backward induction. For both cases, when the firm claims a number of QALYs above the true number of QALYs, and when it claims the true QALYs of the drug, then, we can solve for each country’s pricing policy: i) no countries apply ERP, ii) only country D applies ERP, iii) only country C applies ERP and, iv) both countries apply ERP, we calculate the conditions of the optimal country launch sequence for the firm, either first country C and second country D, or vice versa. The proof can be checked in Appendix B. In Section 4, we will compare the countries’ surplus for each pricing policy, given the optimal country launch sequence. 2.3.1 The firm is trusted by the health agency i) No countries apply ERP Since both countries C and D apply CEA, countries C and D will pay and respectively according to (2.11). Since the firm is trusted by countries i (i= C,D), under assumption 1, countries C and D will equivalently pay and correspondingly. p CCEA p DCEA p F p F
64 Modeling Global Pricing and Launching of New Drugs ( 2.21 ) ( 2.2 2 ) ( 2.2 3 ) Notice that, under assumption 11, the incomes of the country where the drug has been first launched are multiplied by two. Concerning the health agencies’ surpluses, we note that the price considered by countries C and D when applying CEA is an expected price according to assumptions 5 and 6, since both countries have uncertainty about the number of QALYs stated by the firm. the firm’s profits are, and the health agencies’ surplus are57, OF C (WtP C Y F Ep C CEA )q C a if C,D (WtP C Y F p F )q C arq C otherwise OF D (WtP D Y F Ep D CEA )q D arq D if C,D (WtP D Y F p F )q D a otherwise 57 Note that health agencies do not know if the firm has given the true number of QALYs or not. Therefore, they only know the expected price. OF 2p F q C p F q D F if C,D p F q C 2p F q D F otherwise
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 65 ( 2.24 ) PPR58 1 {C,D} if p F p F q D q C {C,D} otherwise If no countries apply ERP, the firm chooses to delay launch in country D, if and only if the price ratio country C to country D is greater than the size ratio country D to country C, otherwise the firm will delay launch in country C. Since the price ratio is less than unity, then a necessary condition for the sequence {C,D} is that country C must be larger than the D’s. ii) Only country D applies ERP Country D decides to apply ERP in stage 1. If the average international price is lower than the price proposed by the firm, i.e., , under assumption 11, the firm delays the launch in country D. Since the pricing policies are set ex-ante, if , country D will pay in any case. However, as country C applies CEA, it will pay . Since the firm gives the true number of QALYs, under assumption 1, it will equivalently pay . Concerning the health agencies’ surpluses, we note that the price considered by country C when applying the CEA is an expected price according to assumptions 5 and 6, since the country C has uncertainty about the number of QALYs given by the firm. the firm’s profits are, 58 Preliminary result. p I p F p I p F p I p CCEA p F
66 Modeling Global Pricing and Launching of New Drugs ( 2.2 6 ) ( 2.28 ) ( 2.2 7 ) ( 2.25 ) OF F 2p F q C p I q D F if C,D p F q C 2p I q D F otherwise and the health agencies’ surpluses are, PPR 2 {C,D} if p F p I q D q C {C,D} otherwise If only country D applies ERP, the firm will choose to delay launch in country D if and only if the price ratio country C to country D is larger than the size ratio country D to country C, otherwise the firm will delay launch in country C. Specifically, the price ratio is the ratio between the CEA price and the average international reference price. Since the price ratio is less than unity, then a necessary condition for the sequence {C,D} is that country C must be larger than the D’s. iii) Only country C applies ERP Country C decides to apply ERP in stage 1. If the minimum international price is lower than the price proposed by the firm, i.e., . The firm delays the launch in OFC(WtPCYFE[pC]CEA)qCa if C,D (WtPCYFE[pC]CEA)qCarqC otherwis e OFD(WtPDYFpI )qDrqD if C,D (WtPDYFpI )qD otherwise p Imin p F
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 67 ( 2.30 ) ( 2.29 ) ( 2.31 ) ( 2.3 2 ) country C. Since the pricing policies are set ex-ante, if , the country C will pay in any case. In turn, country D applies CEA and it will pay . Since the firm gives the true number of QALYs, under assumption 1, it will pay the equivalent of . Concerning the health agencies’ surpluses, we note that the price considered by the country D when applying CEA is an expected price according to assumptions 5 and 6, since country D is uncertain about the number of QALYs stated by the firm. the firm’s profits are, OFF2pI minqCpFqDF if C,D pI minqC2pFqDF otherwise and the health agencies’ surpluses are, PPR 3 {C,D} if pImin pF qD qC {C,D} otherwise pImin p F p I min p DCEA p F OF C (WtP C Y F p Imin )q C if C,D (WtP C Y F p Imin )q C rq C otherwise OF D (WtP D Y F E[p D ] CEA )q D arq D if C,D (WtP D Y F p F )q D a otherwise
74 Modeling Global Pricing and Launching of New Drugs d) If p F p I and p F p I min Figure 2.4 Optimal country launch sequence under d) Table 2.5. Optimal Country Launch Sequence in Figure 2.4 Region/ Policies A B X 62 E F i) No ERP {C,D} {C,D}{D,C} {D,C} {D,C} ii) D ERP {C,D} {C,D} {D,C} {C,D} {D,C} iii) C ERP {C,D} {D,C} {C,D} {D,C} {D,C} iv) Both ERP {C,D} {C,D} {C,D} {D,C} {D,C} 62 Under p I p F p F p I
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 75 2.3.2 The firm states the number of QALYs above the true value This part of the tree has a similar solution to that solved above. For reasons of brevity, we just highlight the differences. In this case, if the country applies ERP, as in the previous case, it will not be able to discover the real value of the drug and it will pay the international reference price , which may be higher or lower than the price revealed when applying CEA. However, if the country decides to apply CEA, it will reveal a lower number of QALYs than those proposed by the firm . Consequently, the country will pay a lower price than the price proposed by the firm . Therefore, on the one hand, it is now less likely for the firm to accept the international reference prices than in the case of being honest, which implies that countries applying ERP will be more likely to experience launch delays. Also, the regions under which the firm chooses its optimal launching sequence change (Figures 2.2, 2.3, 2.4 and 2.5). On the other hand, the expected value of the drug price for the countries will be higher when the firm states a number of QALYs above its true value than when it does not. The implications of this issue will be explained in section 4. 2.4 Comparing Policies: CEA vs. ERP In this section, given the optimal countries launch sequence by the firm under PPR 1, 2, 3 and 4, we compare the countries’ welfare under each pricing policy, CEA and ERP, to know which of them is more convenient for countries. Thus, since we have solved the problem by backward induction, we have carried out this comparison for each country given the optimal country launch sequence for the firm. We have made this comparison in three steps. Firstly, we have compared the best outcome for each country under the same pricing policy and under the same country launch sequence, i.e., using CEA (ERP) under {C, D} and {D, C}, respectively. Then, in a second step, we have compared the best outcomes between ERP and CEA for each country launch sequence. In a third step, we have compared the best pricing policy under each country launch sequence. Thus, we have Condition 1. The proof can be checked in Appendix B. p I h Y iCEA Y F p iCEA p F p I h E[ p i ] CEA
76 Modeling Global Pricing and Launching of New Drugs ( 2.38 ) ( 2.39 ) ( 2.40 ) Condition 1 If country i does not suffer from delay launch under any pricing policy or, under both pricing policies, country i will be better off applying ERP when the unitary cost of carrying out CEA is higher than the difference between the international reference pricing and the expected price of country i under CEA (henceforth, the price difference). In addition, the smaller the population size is, the more attractive will be the use of ERP, since the unitary cost of CEA increases. If country i suffers from delay launch when applying ERP but not under the use of CEA, the delay cost (r) will make CEA more attractive, Analogously, if country i suffers from delay launch when applying CEA but not under ERP, the delay cost (r) will make ERP more attractive, In order to show graphically Condition 1, we plot the following figure, p I h a q i p Ih E[p i ] CEA a q i p I h E[p i ] CEA r a q i p I h E[p i ] CEA r
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 77 Figure 2.5. ERP vs. CEA According to Figure 2.5, we observe under which conditions regarding the unitary cost of applying CEA and the price difference, the country chooses either ERP or CEA. Either if both pricing policies (CEA and ERP) are applied without experiencing any delay launch, or both suffering from a delay launch, the country will be better off applying ERP only if the unitary cost of applying CEA is higher than the price difference. Intuitively, we note that since the unitary cost of applying CEA decreases, to keep the application of ERP beneficial, the price difference should be smaller, either because the international reference price is lower or the expected price of the country i under CEA increases. On the other hand, if the unitary cost of applying CEA increases, to maintain the benefits of applying CEA, the price difference should be larger, either because the
78 Modeling Global Pricing and Launching of New Drugs international reference price goes up or the expected price of the country i under CEA decreases. However, when we compare both pricing policies, on the one hand, if only the country applying CEA suffers from launch delay, even though the price difference is higher than the unitary cost of applying CEA, the unitary delay cost associated with CEA may compensate this higher difference and make ERP more worthwhile. On the other hand, if only the country applying ERP experiences launch delay, and the unitary cost of applying CEA is higher than the price difference, the delay cost of applying ERP may offset a high unitary cost of CEA and make CEA more attractive than ERP for the country. Then, when the price difference is negative, i.e., the expected price of the country i under CEA is higher than the international reference price and there is also a launch delay when applying CEA, ERP will always be chosen by the country. Similarly, if there is no delay applying CEA, intuitively, the unitary cost of applying CEA must be higher than the unitary delay cost induced by applying ERP to offset the negative price difference and therefore to keep ERP attractive, despite the delay launch. Importantly, we note that the expected price will be higher when the firm declares a number of QALYs above its true value than when it does not, which implies that ERP will be more attractive for the countries when the firm is not trusted by countries i (i = C, D). 2.5 Conclusions Using a model where one firm sells an on-patent drug to two countries, which differ in their WtP (ICER), population size, pricing policy (ERP vs. CEA) and ERP formula, one of our main results is that the optimal country launch sequence depends on the relative price and the relative country size. The relative price depends on the countries’ pricing policy (ERP or CEA) and the ERP formula. Given the optimal country launch sequence, our other overall result is that a country is better off applying ERP instead of CEA if the unitary cost of CEA is higher than the price difference, i.e., the difference between the international reference price and the expected price of the country under CEA. The cost of delaying may affect this decision if only one of the pricing policies is applied with delay. Thus, if ERP is applied with delay, E [ p i ] CEA
Chapter 2: External Reference Pricing and Pharmaceutical Cost-Containment 79 the delay cost will make it less attractive with respect to CEA, and analogously, the same applies for CEA with respect to ERP. Basically, the higher the cost of CEA and the lower the international reference price is, then the more attractive the use of ERP is. In brief, we have compared two pricing policies: one of them, ERP, does not require any investment, and the other, CEA, needs an investment of money. In these terms, the smaller the population size is, the more attractive the use of ERP will be, since the unitary cost of CEA increases. The application of ERP will be more attractive than CEA when the firm is not honest, however, it will be more likely to experience launch delays when it is not honest. Therefore, the convenience, in this case, will depend on how many more QALYs above the true number the firm states its drug has and the delay cost. We accept that a wider variety of factors than already used in the model that may affect the bargaining process. For example, the formulas used to apply ERP may be other than the average or the minimum, more than two periods could be considered, firms may offer one single price or launch simultaneously and the effectiveness revealed by CEA could be different among countries. Also, we accept that assumption 9 constrains the firm strategy since one price pF is set, the other is implicitly set as well. Besides, other factors such as the population age structure or the lobbying activity of the pharmaceutical industry (Abraham, 2002) may also need to be considered. However, we consider that this chapter has provided insights into the way a country’s WtP and its pricing policies affect the optimal launch sequence of a firm. Additionally, given an optimal launch sequence, we propose under what conditions, regarding country size and pricing policy, it is better off applying CEA or applying ERP for country i (i = C, D).
3 Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 3.1 Introduction Pharmaceuticals are sold in a global market that involves a specific bargaining procedure between pharmaceutical firms and countries’ health agencies. On the one hand, firms sequentially launch medicines in different countries to maximize global profits, therefore pricing and launching are their major strategic decisions. On the other hand, countries’ health agencies implement pricing policies to control their pharmaceutical expenditure and to guarantee access to medicines. Indeed, pharmaceutical price regulation is high on policy agendas in several countries, either because countries have just reformed, intend to reform or question their practices (see Chapter 1 section 1.1). Among existing drug pricing policies, most countries in the industrialized world have implemented ERP at some time with the aim of controlling their pharmaceutical expenditure and ensuring access to medicines, mainly in on-patent medicines (see Chapter 2 section 2.1). In this chapter, we aim to analyze the trade-off between pricing and launching and the impact of ERP policy on pricing and launching from an empirical point of view. We develop a model that focuses on both issues, controlling for molecules, regulation and country characteristics. We replicate the study of Danzon and Epstein, published in 2008, and the study of Verniers et al. published in 2011. Thus, we aim to test how the situation has changed applying the same methodology to more recent data, and in the case of the second study, to a different list of countries. The previous literature concerning the trade-off between pricing and launching has been already discussed in detail in Chapter 1 in Section 1.2.3 at a theoretical level and in Section 1.2.4 from an empirical point of view. Furthermore, literature concerning the
82 Modeling Global Pricing and Launching of New Drugs impact of ERP policy has also been theoretically and empirically discussed in Chapter 1 in sections 1.2.2 and 1.4.2, respectively. Additionally, in chapter, 2 we developed a theoretical model that analyses the convenience of applying ERP as an alternative to CEA as a cost-containment policy on pharmaceutical expenditure. Particularly, chapter 2, section 2.1, provides insights into the implementation of ERP policy. In this chapter, we develop a two-equation empirical model consisting of a launch delay equation and a relative launch price equation. Previously, we replicate two studies (Danzon and Epstein, 2008, Verniers et al., 2011) using our database to compare their results with those obtained from our updated data and different list of countries. We use data from IMS Health database on 56 new molecules launched in 20 countries belonging to 11 therapeutic classes, all of them approved through the centralised procedure by the EMA, during the study period, 2004-2010. We have collected yearly inpatient and outpatient sales in euros at ex-manufacturer price and unit volume (IMS SU). Our contribution to the previous literature analysed in Chapter 1, section 1.3, consists of the analysis of data at presentation level63, the consideration of the relative launch price64 as an endogenous variable in the relative launch price equation, the study of the launch delay as a duration time variable and the analysis of the inpatient market. Additionally, we introduce country size and country purchasing power as additional explanatory variables. 3.2 Data description In Tables 3.1, 3.2, 3.3, 3.4 and 3.5, we show the descriptive statistics of our database. 75% of the countries belong to the EMA and 70% of the countries apply ERP. No all molecules have been launched in all countries. In the retail market countries belonging to the EMA experience shorter launch delays and pay lower relative launch price on average than countries out of the EMA. In the hospital market, the pattern of launch delays is similar to the retail market, while countries belonging to the EMA and 63 We define two products with the same presentation when both products belong to the same molecule i and have the same quantity of active ingredient per standard unit (see definition of standard unit in Chapter 3 section 3.3.2). 64 Defined later in Appendix C.3.
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 83 countries out of it pay the same relative launch prices on average. Both the launch delays and the relative launch prices show high variability. Countries pay higher relative launch prices in the hospital market than in the retail one, however, no correlation have found between relative launch prices for the retail and hospital market. In both retail and hospital markets, we do not find statistical significant differences in relative launch prices neither between the countries that apply ERP and countries that do not, nor between countries belonging to the EMA and countries that do not. However, statistical significant differences are found when statistical differences in launch delays are analysed. Then, countries applying ERP present significant longer launch delays on average while countries belonging to the EMA experience significant shorter launch delays on average. Table 3.1 Descriptive statistics. Retail market Retail market Number of Molecules Launched Mean Relative Price SD Relative Price Mean Delay in Months SD Delay in Months ERP EMA 5.470597 51.26997 11.82316 11.73209 Austria 48 7.50811 12.50254 * Belgium 24 0.9734428 19.43561 * Czech Republica 32 6.151763 18.99198 * Denmark 52 7.387367 10.90712 Finland 36 5.091636 11.36712 * France 31 4.115586 17.85784 * Germany 58 11.03968 9.974775 Hungarya 33 1.468164 18.55194 * Italy 19 4.34083 20.33394 * Netherlands 31 1.011494 6.705952 * Norway 35 1.883906 8.131455 * Polanda 28 1.959413 13.76601 * Spain 23 0.873803 18.75988 * Sweden 46 10.72532 6.21954 United Kingdom 37 1.449288 8.15 Non EMA 7.450721 40.28605 16.31287 16.42291 Australia 34 11.41437 22.50628 * Canada 41 8.664639 16.12424
90 Modeling Global Pricing and Launching of New Drugs products. Also, the order in which a molecule is launched in each country-subclass influences positively the launch price. However, as in the launch equation, we have not been able to include these variables in our updated model as we explain in detail in Appendix C.1. In both models, launch prices increase with the minimum price previously set in other high-price EU country. However, the effect on launch prices of the minimum price previously set in high-price non-EU countries is different under each model, positive under the D&E model and negative under ours. Only our updated model reports a significant and positive effect from a minimum price set in low-price EU countries. Furthermore, if prices in high-price EU countries are missing, it also affects positively the launch price. This could indicate that countries setting the launch price with no reference in the EU may pay high launch prices. We note that when we estimate with random effects, the effects from low-price EU and high-price non-EU countries become statistically significant. Then, we observe that the lowest price previously set in other low-price EU country affects positively the launch price but more slightly than the effect from the high-price EU country price. Interestingly, both the minimum price previously set in a high-price non-EU country and the absence of a minimum price from high-price non-EU countries affect negatively the launch price. This may indicate that spillover effects also occur either between EU and non-EU countries or among non-EU countries. Furthermore, since the effect of the former is greater, it may show that the net effect on the launch price of a previous launch in at least one high-price non-EU country is positive. On the other hand, the absence of a minimum price from low-price EU countries means countries do not have a reference from this type of countries, and therefore it cannot be included into their reference basket, which results in paying higher prices than if prices from low-price EU countries were available. The results from the updated model with clustered standard errors support the occurrence of spillover effects from high-price EU countries to low-price EU countries, and therefore, that the ERP only concerns EU countries. However, the updated model with random effects supports the suggestion that spillover effects occur in all directions. In both models, per capita income does not seem to statistically affect the launch price. Concerning the type of firms, under our updated model the drugs sold by a Solo Licensee firm obtain lower launch prices than drugs sold by other types of firms. D&E do not find any significant effect related to the type of firm.
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 91 Furthermore, regarding the product’s characteristics, the effect of strength, as expected, is slightly statistically significant and positive, while the packsize affects negatively the launch price. Under the updated model, strength seems to not have any significant effect on the launch price. However, we have not included the variables related to packsize in the updated model as we explain in detail in the Appendix C.1. When introducing the administration route, the more robust result in both models is that injectable drugs are statistically more expensive than other types such as oral solid formulations. In the launch price equation, both models found some launch price differences among countries. The observed pattern is that all significant coefficients are negative; Germany seems to present higher prices on average than the rest of the countries. The random effects in the D&E model show that some country dummies are positive. However, our updated model does not support the hypothesis that firms sell drugs at single price in order to avoid spillovers effects. As mentioned earlier, D&E do not include the therapeutic class fixed-effects due to collinear problems with variables. Since our updated model does not include either the competitor prices variable or the order of entry within class variable, there will not be any collinear problems. We have been able to include the therapeutic class effects in our updated model. We have taken the ATC-A (Alimentary tract and metabolism) as reference and we have found some significant fixed-effects; however, in none of the models year fixed-effects were significant. 3.4 Replicating Verniers et al. (2011) In this section, the methodology conducted in a study published by Verniers et al. in 2011 (Verniers et al., henceforth) (Verniers et al., 2011) is applied to our database to compare whether results have changed due to the use of more recent data (2010 vs. 2008) and if the results are still robust using a different choice of the list of countries. They applied their model to a set of a large set of countries, rich and poor, and we restrict our application to developed countries. 3.4.1 The Verniers et al. model Verniers et al. consider, on the one hand, the launch window of drug i in country j
92 Modeling Global Pricing and Launching of New Drugs ( 3.1 ) ( b ) (), defined as the difference, in months, between the first worldwide launch and the subsequent launch in the specific country j. The launch price is defined as the naturallogarithm-transformed of the ex-manufacturer price at launch per gram of drug i in country j ( ). Verniers et al. consider that censoring occurs for drug-country combinations for which we do not observe a launch at the end of the observation window. Censoring time (C ij ) is defined as the time between the drugand country-specific launch date and the end of the observation period. Since the actual values of and are not observed because right censoring is present, observed values are denoted by and such that, Moreover, we only observe the observations for which and thus . The structural equations are: LW ij * 1 LP ij * 2 (LP ij* ) 2 ' Z ij1 u ij1 LP ij * 1 LW ij* 2 (LW ij* ) 2 ' Z ij2 u ij2 where Z ij1 and Z ij1 are defined as additional explanatory variables. Z ij1 comprises the country size, the health expenditure per capita and the use of certain pricing policies such as the ex-manufacturer price regulation, the profit control, the ERP, the internal RP and the pharmaco-economic regulation. Also, it comprises the strength of patent protection, the EMA and the firm’s home country variables. Additionally, it comprises the four dimensions identified by Hofstede (Hofstede, 1984, Hofstede, 2001): uncertainty avoidance, masculinity, individualism and power distance. The variable of competition and the variable of summer are also comprised in this Z ij1 variable. Z ij2 includes the same variables of Z ij1 except from the summer and the EMA variable. However, it further includes the inflation rate and the daily dosage (DDD). LW ij * LP ij * LW ij * LP ij * LW ij LP ij LW ij * C ij LP ij LP ij* ( a ) ( 3. 2 ) ( b ) ( a ) ** ij ij ij ij ij ij L WLWifLWC LW C otherwise
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 93 Following Garen (1984) (Garen, 1984), Verniers et al. consider the launch window and the launch price as endogenous variables. Therefore, the firm and the regulator may both decide a launch window with the goal of influencing the launch price and select the level of launch price also with the goal of influencing the launch window. The omitted variables in the error terms of the launch window and launch price equations include nonobservable strategic variables used by the firm and the regulator to select the optimal value for the launch window and launch price, respectively. These strategic variables would be expected to correlate with the launch price and the launch window, correspondingly. Verniers et al., to account for the endogeneity between the launch prices and launch window, estimate a system of simultaneous equations using a three-stage least squares (3SLS) procedure, as in Bayus et al. (Bayus et al., 2007). Additionally, the authors correct for right-censoring and selectivity using the procedure described in Vella (Vella, 1993) or Wooldridge (Wooldridge, 2002). Random country effects are included in the equations to account for the fact that there are repeated observations across countries for most drugs. On the one hand, to estimate the structural launch window equation, they first estimate the reduced form of the launch price equation by a Tobit regression of the second type (to account for the fact that we only observe prices if the drug has already been launched). This launch price equation contains two variables that influence launch price but not launch window, namely the defined DDD and the inflation rate, which serve as instruments for the launch price in the launch window equation. The generalized residuals of the reduced launch price equation are added to the launch window equation as a correction term. However, we only use one instrument in our updated model, since we have not been able to calculate DDDs in our database71. On the other hand, to estimate the structural launch price equation, Verniers et al. first estimate the reduced form of the launch window equation by a Tobit regression of the first type (to account for right censoring). This launch window equation contains two variables that influence launch window but not launch price, namely, summer and ema, which serve as instruments for the launch window in the launch price equation. In this case, we have 71 For some molecules of our database, the DDD depends on patient characteristics. Therefore, a unique DDD for each molecule could not be used.
94 Modeling Global Pricing and Launching of New Drugs included both instruments in our model. The generalized residuals of the reduced launch window equation as a correction term are added to the launch price equation. 3.4.2 Data Verniers et al. collect data from the IMS Health database on drugs in 50 countries (see Table C.3 in Appendix C.2) for 5 therapeutic classes, all of which experienced a launch during the study period, 1994-2008. They have collected yearly data on outpatient sales at ex-manufacturer prices. Price per gram in US dollars for each drug has been calculated. To make drug prices comparable across countries, the drug prices in local currencies were converted to US dollars using the currency conversion rate at launch. We also use data from IMS Health database. However, we only consider the new launch drugs in 20 developed countries for 11 therapeutic classes during the study period 2004-2010, all of them approved by the centralised procedure of the EMA. We have also collected outpatient sales yearly at ex-manufacturer price. Since we have collected the prices in euros, euros have been converted into US dollars applying the exchange rates from the IMF. Finally, the drug price has been calculated as done by Verniers et al. Thus, we also use the price per gram in US dollars for each in order to make results comparable. In Appendix C.2, we report the variable definitions. We distinguish among those variables that we define as in Verniers et al and those that we cannot use or we define differently. In Table C.4, we show the results from both models to be compared. 3.4.3 Comparison of results 3.4.3.1 Launch window equation The sample of Verniers et al. is larger than our sample (1711 vs. 505) because their study period is longer and the sample of countries is larger (50 vs. 20). As expected, in both models, launch price affects negatively the launch window, i.e., the higher the price a country pays for a drug, the shorter the delay the country will suffer. Also, both models find a positive quadratic effect that offsets the above mentioned negative effect (U-shaped effect). Furthermore, both models find a positive and significant coefficient of the selectivity variable, suggesting endogeneity of prices in the launch equation. This
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 95 finding may indicate that health regulators act strategically in delaying market access for expensive drugs, which is against the interests of the drug company. Concerning the regulation variables, although researchers have not examined the direct effect of profit control on launch window, Verniers et al. argue that it may slow market access. However, in our updated model, profit control regulation seems to affect the launch window negatively. Indeed, the only country applying profit control in our dataset is the UK, which does not suffer particularly from long launch delays. ERP72 may show counterintuitive effects (Hunter, 2005). First, when a country applies ERP, firms will try to gain market access as early as possible to minimize the number of reference countries. Second, ERP may push prices upward rather than downward. Typically, regulators that seek early drug access are more willing to agree to higher prices. Thus, the likelihood of a reference country having a high price is higher early in the life cycle than it is later on, as we discussed in chapter 2. Consequently, the reference set of a country is likely to contain a greater number of countries with high prices early in the life cycle as compared to later in the life cycle. Our updated model shows a significant and negative coefficient for this regulation variable; therefore, it confirms the hypothesis proposed by Verniers et al. As said by Verniers et al., typically, therapeutic referencing delays launch because the administrative procedure requires an examination of therapeutic similarities, delaying market access. However, and contrary to the results obtained by Verniers et al., our updated model reports a negative and significant coefficient for this variable. Therefore, those countries using therapeutic reference pricing experience shorter launch delays. Pharmacoeconomic evidence, in addition to the clinical evidence required to gain therapeutic approval from institutes such as the FDA (Food and Drug Administration) or EMA, also requires evidence on the cost effectiveness of the drug in the local population, and it must be submitted according to complicated administrative procedures. This requirement often causes a delay in market access similar to therapeutic reference pricing (Wilking et al., 2005) as we discusses in chapters 1 and 2. Results reported by our updated model seem to support the results and the hypothesis proposed by Verniers et al. 72 This variable is named by Verniers et al.as Cross-country reference pricing.
96 Modeling Global Pricing and Launching of New Drugs Concerning the strength of patent protection, it is known that high strength of patent protects the firm from bio-equivalent price competition. Thus, a higher strength of patent protection in a country may yield quicker access to drugs. In both models, those countries with strong patent protection show shorter launch delays. Other country characteristics considered are population size, health expenditure per capita and dummies for the firm’s home country. The bargaining power given by the population size is shown in our updated model since the effect of this variable is significant and negative. Therefore, it supports the hypothesis and the results shown by the Verniers et al. model. Regarding health expenditure per capita, Verniers et al. propose that firms may be more eager to launch in countries with high health expenditures per capita, as these countries may have a more favourable attitude towards new drugs. However, higher health expenditures per capita could lower health regulators’ aspirations to provide quick market access to new drugs (Comanor and Schweitzer, 2007). In both models, the effect of health expenditure per capita on the launch window is significant and positive, supporting the second idea proposed by Verniers et al. Concerning a firm’s location, under the Verniers et al. model, firms with a greater familiarity with the home market's therapeutic needs or health regulators' favouritism toward these firms may lead to a faster launch (Kyle, 2006). In this case, both models support the hypothesis described above. A variable exclusively affecting the launch window is the dummy equal to one if the country belongs to the EMA. According to Verniers et al., belonging to the EMA should affect negatively the launch window. Although market access and price negotiations take place at country level, the drug approval process in Europe is centralized. It is expected that launch windows in EMA countries are shorter than those in nonEMA countries because of differences in administrative efficiencies. Again, the Verniers et al. model supports this hypothesis while our updated model shows a high significant positive effect (countries not belonging to the EMA enjoy shorter launch delays than countries belonging to the EMA). This could be due to the differences in the sample of countries. In our database, only countries not belonging to the EMA are high-price countries. In the Verniers et al. database there are a lot of low-price countries and very low-price countries. Out of the four dimensions identified by Hofstede (Hofstede, 1984, Hofstede, 2001)) concerning a country’s national cultureuncertainty avoidance, masculinity,
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 97 individualism and power distance (defined in Appendix C.2) – only the masculinity and the power distance present the same effects in both models and support the hypothesis stated by Verniers et al., the more masculine the society is and the more bureaucratic it is (higher power avoidance), the longer the launch window is. Verniers et al. expected and showed that, on the one hand, low subjective health perceptions (high level of uncertainty avoidance) may encourage health regulators to allow prompt access to new drugs and to be less price sensitive, and on the other hand, countries showing a greater satisfaction toward health care and spending more money on healthcare (high level of individualism) enjoy shorter launch windows than collectivist countries. However, our updated model presents the opposite effects. Finally, the drug therapeutic fixed-effects (not reported) seem to be statistically significant under the Verniers et al. model and in our updated model. 3.4.3.2 Launch price equation Regarding the explanatory factors of the launch price equation, under our updated model, the launch window does not seem to affect the launch price, while the Verniers et al. model finds a significant and positive effect (negative for the quadratic term) as expected under their hypothesis. Indeed, Verniers et al. propose an inverted U-shaped effect of launch window on launch price in which launch price is highest for moderate launch windows. For these moderate launch windows, a firm can still make money under patent protection if the price is high enough to make up for local market entry expenditures. For very short launch windows, a firm will accept a lower launch price more easily because the drug enjoys a full lifetime under patent protection, so the firm can recover R&D expenditure and gains resources for international market access immediately. For very long launch windows, a firm and a health regulator will agree more easily on a relatively low launch price as a prelude to generic competition. Regarding the regulation variables, neither of the models find any significant effect on launch prices, except from the strength of patent protection, where only the Verniers et al. model finds a negative and significant effect as expected: stronger patent protection may impose a downward pressure on launch prices because pharmaceutical firms can be more lenient on prices if there is sufficient time left under patent protection to recover R&D expenditure.
98 Modeling Global Pricing and Launching of New Drugs Among other country characteristics considered such as population size, health expenditure per capita and the firm home’s country, only the firm home’s country seems to be statistically significant and positive, but only under the Verniers et al. model, supporting their hypothesis. As previously mentioned, a greater familiarity with the home market's therapeutic needs or health regulators' favouritism toward these firms may lead to a higher launch price (Wagner and McCarthy, 2004). The drug therapeutic fixed-effects (not reported) are not statistically significant. 3.5 New Pricing and Launching Model (NPLM) 3.5.1 The Model We estimate the launch delay and the relative launch price equations separately, and each of them is estimatedfor retail and for hospital distribution channels. We have also tried to estimate a system of both equations to account for endogeneity. However, the available instrument of the relative launch price equation is weak73. We use a parametric duration model of the hazard of launching in time t, given the observed explanatory variables, with right-censored data to model the launch delay of the molecule i in country j, which is defined as the time elapsed in months from the first global launch of the molecule i and its launch in country j. We have specified the shape of the hazard rate, i.e. its time-dependency, with a Weibull distribution that assumes a monotonic hazard with respect to time. Since we have not been able to observe all variables affecting the launch delay, we have controlled for the unobserved heterogeinity introducing a gamma frailty distribution for the random error term, The model selection has followed the method proposed by Kiefer (Kiefer, 1988) (see Appendix C.4). We estimate a right-censored model since all the drugs in our data set were launched between January 2004 and December 2010; however, not all drugs had been launched in all 20 countries by the end of our observed period. Therefore, our data contain rightcensored observations. Our parametric duration model for the hazard of launching does not allow the use of time-varying covariates74; instead we have used the data collected in 73 We selected the variable inflation as the instrument for the launch price in the launch equation. The correlation between inflation and launch price was weak (0.02), therefore, we should not use it as instrument. 74 Since we reject the null hypothesis of the log-rank test, then the assumption of proportional hazard is not satisfied, we should not neither incorporate time-varying nor use the standard Cox regression. Furthermore, the extended Cox model allows incorporating time-varying covariates but we should not use it since it does not allow incorporating censoring.
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 99 the base year75. Thus, we have: where , the subindex i=molecule, j=country, h is the hazard rate of launching, Xij are the covariates, the covariates’ parameters, t the time elapsed until the launch of molecule i occurs in country j, p the shape parameter76, U a random variable and the variance of the frailty77 (Keele, 2007, Jenkins, 2008). The covariates Xij are the relative launch price at molecule level, the logarithm of the country size (population), the logarithm of the public health expenditure per capita, the logarithm of the pharmaceutical expenditure per capita, the dummy for the firm’s headquarters location in the launching country, the dummy for belonging to the EMA and therapeutic fixed-effects at ATC-1 level. These variables are defined in detail in Appendix C.3. Regarding the relative launch price equation, we use OLS with moleculepresentation-clustered standard errors to model the log of the relative launch price of molecule i, product k in country j at the time t, conditional on launching. The relative launch price is defined as the price ratio between the launch price of molecule i, product k in country j at the time t, and the launch price of molecule i, product k in country g at the first global launch time 0. To account for unobserved molecule characteristics, we also report results from a GLS (Generalized Least Squares) random effects estimator. To account for possible selection bias produced by the correlation between the propensity to launch and the launch price, we also estimate a Heckman selection model with a firststage probit regression (Heckman, 1979). Then, we have the probit selection equation: 75 For these covariates, such as country population, the GDP per capita, health and pharmaceutical expenditure per capita variables, we use the data for the base year (2004). These covariates are in the model to control for differences in country sizes, wealth and expenditure, which are well represented with the data collected for the base year 2004. 76 The shape parameter p determines whether the hazard is increasing, decreasing, or constant over time. 77 By testing the hypothesis = 0 using a likelihood ratio test, we determine whether we need to worry about unobserved heterogeneity. h(t,X) p( t) p1 [U] ij e X ij (3.3)
106 Modeling Global Pricing and Launching of New Drugs Table 3.7. Relative launch price equation of the NPLM Retail Hospital OLS w/ Robust Clustered SEs Normal Random Effects OLS w/ Robust Clustered SEs Normal Random Effects Delay -0.0722 0.0053 -0.0726 0.0137 [0.1296] [0.1199] [0.1695] [0.1490] Delay*delay 0.0023 0.0008 0.0000 1.41e-06 [0.0021] [0.0008] [0.0000] [6.87e-06] Log of Country size (population) -8.6599* -5.8246*** -10.1434** -4.2420* [4.7556] [1.9964] [4.4531] [2.4337] Log of GDP per capita -9.5648 -2.6522 7.7304 1.7151 [9.7722] [16.5960] [12.6634] [19.4184] Log of Health Expenditure per capita 24.6213* 14.3926*** 18.0517* 4.1666 [13.9677] [12.7490] [9.8605] [14.9830] Log of Pharmaceutical Expenditure pc 50.7958* 37.3478 70.6921** 29.2789 [27.4052] [13.0590] [31.7399] [18.2923] IRP -5.8320 -2.7864 -8.9012 -4.8740 [4.1272] [3.3182] [6.0281] [4.3355] Firm’s home country -4.1632 -4.1037 -19.7583* -5.1026 [3.5854] [5.8000] [11.5931] [8.3115] Year 2004 RC RC RC RC RC RC RC RC 2005 0.6371 -2.8228 3.6965 -1.4028 [2.3904] [7.3313] [3.3774] [9.1615] 2006 -0.9426 -4.1618 4.4947 1.0742 [2.8771] [8.1672] [4.3652] [10.8644] 2007 -3.6626 -6.0497 -3.1937 -0.3632 [5.1028] [8.4271] [5.5596] [11.4744] 2008 -4.4736 -8.0594 -1.3471 1.0891 [5.0712] [8.8661] [5.6821] [12.3694] 2009 -2.1013 -8.1843 14.1190 1.3525 [3.6430] [9.2742] [11.2567] [13.2468] 2010 -16.3579 -16.5441 -7.6186 -4.3132 [10.4757] [10.2073] [9.7540] [14.6237] IMR 60.7956* 45.6041*** 80.9075** 25.3029 [33.2356] [16.1445] [36.1181] [23.8379] Constant -19.4495 -303.1349** -648.5746* -210.4026 [87.4215] [140.4957] [329.3245] [207.2856] Observations 1334 1334 1369 1369
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 107 Retail Hospital OLS w/ Robust Clustered SEs Normal Random Effects OLS w/ Robust Clustered SEs Normal Random Effects Number of Molecule-presentation-level Clusters 69 69 70 70 R-squared 0.1776 0.1744 0.1952 0.1952 Significance(sign.)levels(two‐sided):*:p<0.10;**:p<0.05;***:p<0.01.;[]:standarderror;n.r.:no‐reported;‐:no‐included;RC:referencecategory.Seedefinitionofvariablesin AppendixC.3.
108 Modeling Global Pricing and Launching of New Drugs Furthermore, other country characteristics such as pharmaceutical and health public expenditure per capita do seem to affect significantly the relative launch price. Indeed, as expected, countries with high pharmaceutical and public health expenditures per capita pay higher relative launch prices. In addition, the results show that countries with a high bargaining power, since they have a large population, pay lower relative launch prices on average (see Chapter 2 section 2.1). As we account for unobserved molecule characteristics, we also report results from a GLS random effects estimator. The results slightly change when we estimate this alternative specification. Particularly, only the country size and public health expenditure per capita remain as significant factors influencing the relative launch prices. When we analyse the hospital sales, we observe that, compared to the retail market, significant results remain. Furthermore, in this analysis, the firm’s headquarters’ location has a slightly significant and negative effect on the relative launch price. Therefore, drugs launched by firms with their headquarters in the launching country set lower prices than drugs launched by firms with their headquarters outside the launching country. This unexpected effect will be discussed later on in this chapter, and it can be compared with the results reported in the literature in Chapter 1 Section 3.2.5. Similar to the retail market, when we report the results from a GLS random effects estimator, the only significant effect that remains is the country size, the rest of variables affecting the relative launch price become insignificant. Even, the IMR does not affect significantly the relative launch price, only being significant in the retail market for both specifications and in the hospital market for the OLS molecule-clustered estimate. 3.5.4 Discussion Our contribution to the previous literature analysed in Chapter 1, sections 2 and 3, firstly consists of the analysis of the database at presentation level, the analysis of the relative launch price as endogenous variable in the launch price equation, the study of the launch delay as a duration time variable and the analysis of the inpatients market. In this chapter, we have carried out an analysis of the trade-off between pricing and launching and the impact of ERP policy on both pricing and launching. In this regard, we have observed that the launch delay does not significantly affect the relative launch price; however, the relative launch price does affect launch delay, but the extent of the influence is quite low. In addition, the results show that the use of ERP
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 109 makes countries experience longer launch delays but does not lead to paying lower relative launch prices. These results may have several implications on the bargaining process. Indeed, we may think that firms do not want to play the game in which, countries reject a firm’s offer knowing that over the time they will obtain lower prices. Besides, we observe that firms delay launches in countries using ERP policy; however, these countries do not necessarily pay lower prices. This last result may indicate that ERP policy is not effective in “pricing terms” but it is in “launching terms”. It seems that firms do not accept lower prices in exchange for delaying launches from countries applying ERP. These results may suggest that firms basically delay launches because countries probably cannot afford to have the product available straight from the global launch, and even not having the product. In contrast to previous literature, where firms sometimes delay launch to avoid spillover effect, in our study, we show that firms may use a more aggressive strategy, which does not allow countries to have the products available with a launch delay in exchange for paying lower relative launch prices. Under this strategy, firms would avoid the spillover effects from ERP policy and PT, though they would lose profits from sales in those countries where the molecule is not ultimately launched. Furthermore, among other country characteristics, we observe that the bargaining power of country size is effective to obtain lower prices; however, this country characteristic does not seem to be an influencing factor on achieving shorter launch delays, even more, unexpectedly, countries with a large country size find lower probabilities to have a product launched. In the same line, we have observed that GDP per capita does not affect the relative launch price; however, other country characteristics, more specifically ones affecting pharmaceutical consumption, such as pharmaceutical and health public expenditure per capita, affect positively the relative launch price. Exactly the opposite effect occurs for the launch delay. The pharmaceutical and the public health expenditure do not seem to result in countries experiencing shorter launch delays. Indeed, what does make countries have products available in the short-term is a high level of wealth per capita. We may say that wealthy countries have the products available in the short-term, and the countries that ultimately pay high relative launch prices are those that allocate large budgets to public health and the pharmaceutical expenditure. On the basis of the results, firms neither make discounts nor launch in the shortterm in those countries where they have their headquarters. Only in the hospital market do we observe that countries obtain lower prices from this type of firms than from foreign
110 Modeling Global Pricing and Launching of New Drugs ones. So far, the previous literature has either not found any significant price premiums for local firms or has found a positive significant and expected effect. Note that these studies collected data not only in high-income but also in low-income countries where firms’ headquarters are not usually located, therefore, the positive effect found could be due not exclusively to the firm’s location but also to the country’s wealth. Furthermore, countries belonging to the EMA enjoy shorter market access on average than countries outside of the EMA regime. 3.6 Conclusions Conclusions from replicating the D&E model The updated model presents some differences from the D&E model. We must note that our sample starts in 2004, immediately after the D&E sample finishes. Also, the lists of countries and products are not exactly the same in both models. This may justify differences in results. The D&E model is robust among different specifications, while our updated model presents some alterations. The most remarkable differences in results between the D&E model and the updated one concern the spillover effects and the effects of the type of firms. The updated model finds that the number of low-price EU countries affects negatively the propensity of launch, which may confirm that low-price countries are suffering longer launch delays, while the D&E model does not find any significant effect. Furthermore, the updated model finds that the minimum price set in the low-price EU affects positively the launch price, which may show that spillover effects also exist among low-price countries, while the D&E model does not. Moreover, the minimum price set in high-price non-EU countries presents a negative effect on the launch price (positive in the D&E model) which may indicate that spillover effects occur among EU and high-price non-EU countries. To have no reference prices from low-price EU and high-price non-EU countries only seems to have effect in the updated model. No references from low-price EU countries yields a positive effect due to two different situations, either that spillover also occurs among low-price countries, or that being first means paying higher prices. No references from high-price non-EU countries may indicate either that these countries are also taken as reference by EU countries or that launch prices in the EU could be higher than the launch price out of the EU.
Chapter 3: Global Pricing and Launching of New Drugs. An Econometric Approach 111 Furthermore, according to the D&E model, Local Corporations enjoy a higher propensity to launch, but our model does not find any significant effect from this characteristic. The other way around occurs for the launch price, where the D&E model does not indicate any significant effect on launch prices while the updated model shows that Solo Licensee firms obtain lower launch prices. Both models present significant country-fixed effects in the launch and launch price equation. Conclusions from replicating the Verniers et al. model The same data treatment and methodology conducted by Verniers et al. have been implemented for our database. Differences in the list of countries and drugs studied, and in the time period covered may justify some of the differences in the results above. Verniers et al. find evidence of endogeneity of both launch prices and launch delays. We only find the effect in a single way; launch delay is negatively affected by launch prices but not the other way around. The effects from some regulatory policies do not seem to coincide. Some regulatory policies traditionally positively affecting the launch window, such as profit control or the therapeutic reference pricing, are not significant in the updated model. However, both models show that the use of pharmaco-economic evidence regulation leads to longer launch delays. Also, in both models, the stronger the strength of patent is, the shorter launch delays are. Only our updated model presents a significant and expected effect for the use of ERP regulation. On the other hand, the regulatory policies do not show significant effects on the launch price under any models, except for the strength of patent protection; stronger patent protection imposes a downward pressure on launch prices. Results from other country characteristics like population size and health expenditure per capita, when statistically significant (only in the launch window equation) are concordant in both models. Among other country characteristics, countries that host a firm’s headquarters of the firm launching the drug experience shorter launch delays under both models. This effect is not significant in the launch price equation under the updated model but positive under the Verniers et al. model, supporting their hypothesis that firms settled in the launch country enjoy higher prices. Furthermore, belonging to the EMA shows the opposite effects. According to Verniers et al. countries belonging to the EMA
112 Modeling Global Pricing and Launching of New Drugs show shorter launch delays, however, our updated model reports that these countries experience longer launch delays than in countries outside of the EMA. Finally, both models present significant therapeutic class fixed-effects. However, the four dimensions identified by Hofstede (Hofstede, 1984, Hofstede, 2001) concerning a country’s national culture present different effects in both models. Conclusions of the NPLM Under the NPLM, the pricing and launching seem to be no longer related to each other. Differences exist in prices across countries but not due to the launch delay. Firms do not accept lower prices in exchange for delaying launches, even from countries applying ERP policy, therefore, ERP policy seems to not be effective in “pricing terms” but is in “launching terms”. These results may lead to several implications in the bargaining process. We suggest that firms basically delay launches because countries probably cannot afford to have the product available straight from the global launch, and ultimately end up not having the product launched. While the firms often delay launch to avoid spillover effects, under our study, we show that the firms may conduct a more aggressive strategy that does not allow countries to pay lower prices in exchange for experiencing longer launch delays. Under this strategy the firms would avoid the spillover effects from IRP policy and PT, but they would also lose profits from sales in countries where the molecule is not ultimately launched. Regarding other country characteristics, our study shows that wealthy countries have the products available in a shorter period, but the countries that ultimately pay high relative launch prices are those that allocate large budgets to public health and pharmaceutical expenditure. Countries belonging to the EMA seem to enjoy shorter launch delays than the countries outside of it; however, there are no significant price differences between countries under the EMA regime and countries outside of it. In general, the results in the retail market and the hospital market do not show huge differences, but we highlight the firm with headquarters in the launching country bargains lower prices with the country concerned just in the hospital market.
Conclusions and further research Our systematic review shows that demographic and income country features, and regulation regimes, seem to be the most important factors affecting drug pricing and launching. However, price regulation can undermine the effects of these important factors. Drug characteristics like strength, packsize and presentation forms are significantly related to the price. Also, supported by previous studies, belonging to the EMA’s therapeutic category robustly affects the launch delay and the launch price. The therapeutic value is shown in the previous literature as a robust factor-influencing drug pricing and launching. Additionally, firm location turns out to be an important factor for the launching decision; however, price premiums due to headquarters location appear ambiguous. When replicating the D&E model with more recent data, we find some new patterns compared with the D&E results concerning the spillover effects and the type of firms. Therefore, we determine that low-price countries suffer longer launch delays and spillover effects also exist among low-price countries: spillover effects also occur between EU and high-price non-EU countries. Furthermore, Local Corporations do not have a higher propensity to launch any longer; however Solo Licensee firms do obtain, nowadays, lower launch prices. Replicating Verniers et al. with more recent data and a different list of countries yields new outcomes. The most important outcome is that the endogeneity of both launch prices and launch delays found by Verniers et al. is no longer found; only the launch delay is negatively affected by the launch price. The effects of some regulatory policies on the launch delay do not generally seem to coincide. Again, similar to the D&E, in our updated model, firm location loses its effect on drug launching and pricing. One of the most important conclusions of this doctoral thesis is that, in contrast to previous models, pricing and launching seem to be no longer related to each other.
114 Modeling Global Pricing and Launching of New Drugs Differences in prices exist across countries but not due to the launch delay. Firms do not accept lower prices in exchange for delaying launches, even from countries applying ERP policies, therefore, ERP seems to not be effective in “pricing terms” but it is in “launching terms”. These results may hold several implications for the bargaining process. We suggest that firms basically delay launches because countries probably cannot afford to have the product available straight from the global launch, or ultimately not having the product launched. While firms used to delay launch to avoid spillover effects, in our study, we show that firms conduct a more aggressive strategy that does not allow countries to pay lower prices in exchange for experiencing longer launch delays. Under this strategy, firms would avoid the spillover effects from ERP and PT, but they would also lose profits from sales in countries where the molecule is not ultimately launched. Regarding other country characteristics, being a large country helps to have more rapid market access and obtain lower prices. Furthermore, wealthy countries have the products available within a shorter period, but the countries that ultimately pay high relative launch prices are those that allocate large budgets to public health and pharmaceutical expenditure. Firm location no longer affects the price and neither does the launch delay. Countries belonging to the EMA seem to enjoy shorter launch delays than the countries outside of it; however, there are no significant price differences between countries inside the EMA’s regime and countries outside of it. In general, the results in the retail market and the hospital market do not show huge differences, but we highlight the firm with headquarters in the launching country bargains lower prices with the country concerned just in the hospital market. In view of the overall perspective concerning the main factors influencing launch prices and launch of new drugs based on theoretical studies, we mainly distinguish two types of factors. Firstly, we have observed factors that directly affect drug pricing and launching, such as the presence of PT, the firm’s characteristics and the regulation pricing policies, for example ERP, internal RP or MES + PC. Secondly, our review shows other determinants that not only impact directly but also indirectly, affecting the measures of the first types of factors that influence drug pricing and launching, such as country size and the level of co-payments. According to our theoretical model, given the optimal country launch sequence, we conclude that the smaller the population size is, the more attractive the use of ERP will
Conclusions and further research 115 be, since the unitary cost of CEA increases. Note that ERP does not require any investment, however CEA does. Therefore, the use of ERP is helpful to relatively small countries compared to the use of CEA. This result confirms some statements on this issue that have not been previously shown. ERP is a low-cost pricing policy; however, we have now more information about why countries apply this type of pricing policy fully aware that it may be not fair. We also conclude that the optimal country launch sequence depends on the relative prices and the relative country sizes. There is a trade-off between price and volume, which affects the country launch sequence. In addition, the relative price depends on the countries’ pricing policies, ERP and CEA, and subsequently, on the ERP formula. Particularly, a country is better off applying ERP instead of CEA, if the difference between the international reference price and the expected price of country i under CEA is not higher than the unitary cost of CEA. This result is affected by the delay cost if only one of the pricing policies is applied with delay. From the perspective of the regulator, concerning the application of ERP, the previous theoretical literature recommends only small countries to engage in ERP and/or apply ERP based on prices in large countries (or large group of countries); the same applies if one substitutes “large country” by “small co-payment country” and vice versa. Also, a minimum RP level is recommended to avoid major increases in price level with respect to the average RP. Furthermore, an MES policy together with a PC should be applied when the welfare loss due to high-type drug buyers is not large enough to outweigh the welfare gained due to the low-type drug. From the perspective of the firm, the literature recommends that the loss of income coming from PT should be taken into account and the firm should set a higher price than without PT. However, since ERP is widely used by countries and PT does exist, the markets are inseparable. Therefore, the highest drug price is not always the best option for the firm and the lowest drug price is not always the best option in a given country. What may have been an optimal pricing strategy in a single country is no longer optimal when considering ERP and PT. Our theoretical model shows that there is a trade-off between prices and volumes affecting the country launch sequence. Also, it provides information about ERP, an issue
122 Modeling Global Pricing and Launching of New Drugs Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc observed in the previous year) * (+) Dummy of absence of a global price of reference * (+) Average global price of the molecule in US real $ * (+) A dummy taking one if the corporation is local-non multinational A dummy taking one if the corporation is local but multinational Number of identified generics in the market Berry index (it measures the degree of specialization of the corporation) * (+) Fpc (Fraction of public consumption in GDP) GDP per capita (Fraction of public consumption in GDP) * (+) GDP per capita in US dolars Reg (Level of regulation: 1 low, 2 medium, 3 high) * (-) Fpc*Reg2 * (-) Fpc*Reg3 * (+) GDP*Reg2 * (+) GDP*Reg3 * (-) Molage (Time elapsed since the molecule was launched to December 31, 2003) Log of the number of market a molecule is present Cesnormol (A dummy taking one if the molecule was launched before January 1, 1991)
Appendix A 123 Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc Censorlag (Censormol_lag_1) * (-) Generic (A dummy taking one if the product is generic) Coronado et al. (2007)** Price level (ex-factory) Regulation HHI (Herfindähl-Hirschman Index) *(+) Firm size lag *(-) New product lag * (+) Global Price in USD for product j belonging to firm i Binary variable, taking 1 if product j is a compound of molecules * (+) Market share of product j in market k * Number of generic products in market k Time elapsed up to 2003 since molecule (market) k was launched Corporation share in market k excluding product j's share Binary variable, taking 1 if molecule age is censored in the sample Binary variable, taking 1 if product j was launch date is censored in the sample Binary variable, taking 1 if product j is a generic) Weighted average multimarket contact variable for firm i in market k Alternative weighted average multimarket contact variable in market k All products at ATC-4. All type of products (NCE, Patent, off-patent,generics) 1998-2004 IMS HEALTH x Danzon and Chao (2000a)b Price level (ex-factory) Drug and Brand Competition Strength Molecule Age (months from the first product launch each country to September 1992) Form codes (The number of distinct 171 molecules / 5690 products All type of products (NCE, Patent, off-patent,generics) 10/1991-09/1992 IMS HEALTH
124 Modeling Global Pricing and Launching of New Drugs Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc formulation of strengths in the molecule) Global penetration (number of countries in which the molecule is available out of the seven countries in sample) Packsize Number of manufacturers of the molecule Therapeutic substitute molecules (ATC3) Therapeutic substitute molecules entry lag ( lag in months between each molecule launch date and the first launch of the molecule, ATC3) Danzon and Chao (2000b)b Price level (ex-factory) Brand Competition and Regulation Strength Molecule Age (months from the first product launch each country to September 1992) Form codes (The number of distinct formulation or strengths in the molecule) Packsize Number of manufacturers of the molecule Generic Entry Lag (lag in months between the product’s own launch date and the launch date of the first product in the molecule) Therapeutic substitute molecules (ATC3) Products per Therapeutic Substitute Molecule Therapeutic substitute molecules entry lag ( lag in months between each molecule launch date and the first launch of the molecule, ATC3) 171 molecules / 5690 products All type of products (NCE, Patent, off-patent,generics) 10/1991-09/1992 IMS HEALTH Danzon and Epstein (2008)** Price at launch (ex-factory) and Launch window Brand Competition and Regulation S=Superior ; I=Inferior * (+) S Expected Drug Price Superior Brands (lag) Superior Brand’s Price Missing 375 molecules All type of products (NCE, Patent, off-patent,generics) QI/1992QIV/2003 IMS HEALTH
Appendix A 125 Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc * I (-) Expected Drug Price Inferior Brands (lag) Inferior Drug Price Missing Expected Drug Volume (lag) Number of Generic Manufacturer in Superior Subclass Number of Generic Manufcaturer in Inferior Subclass * I(-) Generics’ Price Missing * I(+)First Brand Launch in Country-Subclass * S(+) I(+) Second Brand Launch in CountrySubclass * S(+) I(+) Third or Fourth Brand Launch in Country-Subclass * S(+) Min Own Price in High-price EU Missing * S(+) I(+) Min Own Price in High-Price EU Min Own Price in Low-price EU Missing Min Own Price in Low-Price EU * I(-) Min Own Price High-price non-EU Missing * S(+) Min Own Price in Hi-Price non-EU Number of low-price EU countries a molecule has already launched Number of high-price EU countries a molecule has already launched Number of high-price non-EU countries a molecule has already launched Number of molecules in Superior Subclass
126 Modeling Global Pricing and Launching of New Drugs Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc * S(-) Number of molecules in Inferior Subclass PIc Share in Subclass * S(+) GDP per Capita * S(-) Country-Specific Quarterly Producer Price Index * S(+) Strenght * S(-) I(-) Pack size Form: Oral Solid Delayed * S(+) I(+) Form: Injectable Form: Other * S(-) I(-) Time since Global Launch * S(+) I(+) Time since Global Launch Squared * I(+) First Global Launch before 1990 First Global Launch in [1996-end] * S(+) I(+) Launch by Local Originator Corporation * S(+) I(+) Launch by Solo Licensee Corporation * S(+) I(+) Launch by Local Co-marketer Corporation Exchange rate ( US to EUR) * S() I() Country fixed effects (n.a.) ATC fixed effects Danzon et al. (2005) Hazard Launch Regulation *(+) Expected Drug Price *(+) Expected Drug Volume 85 molecules Only NCE (New Chemical Entities) 09/1994-09/1998 IMS HEALTH x
Appendix A 127 Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc *(+) Firm’s Global Launch Experience (sales) *(+) The originator firm’s home country *(+) GDP per capita * Country fixed effects * ATC fixed effects Danzon et al. (2011)** Price level (ex-factory) Regulation and Country * (-) IMS*GENERIC indicator ( IMS: a dummy taking one if the drug is sold through standard retail channels; GENERIC: a dummy taking one if the the generic is present in a country-year) * (-) GPRM*BRAND indicator (GPRM: a dummy taking one if the drug is procured by NGOs; a dummy taking one if the drug is the originator is present in a country-year) * (-) GPRM*GENERIC indicator ( GPRM: a dummy taking one if the drug is procured by NGOs; a dummy taking one if a generic is present in a country-year) * (+) Per capita income country * (-) GINI coefficient GINI missing indicator HIV prev. (HIV country prevalence rate) * (-) The number of tender generic products in the same therapeutic class-country-year * (-) The number of retail generic products in the same therapeutic class-country-year The number of originator products in the same therapeutic class-country-year * (+) Originator molecule flag All type of products (NCE, Patent, off-patent,generics) 01/2004-06/2008 IMS HEALTH / GPRMc
128 Modeling Global Pricing and Launching of New Drugs Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc * (+) Generic molecule flag Heuer et al. (2007)** Hazard Launch Regulation Country GDP per capita Size of the country population A dummy taking one if the country use ERP A dummy taking one if the country use other direct price controls such as cost-effectiveness, etc.) A dummy taking one if the country use RP * (-) A dummy taking one if the country use ERP explicitily * (-) A dummy taking one if the country use ERP as a basis for their decision making criteria 35 molecules Only NCE 01/1995-12/2005 IMS HEALTH Kanavos and Costa-Font (2005) Price level (wholesale) Regulation Total market size, defined as sales for all products Market share of each PI product within each product market and each importing country Average Euclidean Distance of latitude and longitude between each importing and exporting country capitals * (-) Exchange rate $ * (-) Purchasing Power Parities in importing country * (-) Market shares of generics consumption in a country (i ) * (-) Dummy variable for introduction of the clawback; Price regulation (Dummy variable for price regulation defined as the intervention of third party payer (national insurance company) or the government in terms of setting price of each product ( j ) 19 molecules All type of products (NCE, Patent, off-patent,generics) QI/1997QIV/2002 IMS HEALTH x Kanavos and Price level (ex-factory and * (+) Number of years since molecule’s launch in 68 molecules / 100 products 2004,2007 IMS HEALTH
Appendix A 129 Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc Vandoros (2011) retail) the local market Age squared generics (dummy variable 1 if there is a generic competitor present in the market) * (+) Dummy variable for United States * (+) Dummy variable for United Kingdom * (+) Dummy variable for Mexico Dummy variable indicating the impact of Health Technology Assessment being explicitly used as a policy measure Dummy variable. Indicates the presence of reference pricing * (+) Dummy variable. Indicates the presence of free pricing Dummy variable. Indicates the explicit use of ERP) Exchange rate * () Therapeutic fixed effects All type of products (NCE, Patent, off-patent,generics) Kyle (2006) Hazard Launch Firms * (+) Drug importance (drug’s share of stock of Medline citations for class) * (+) Number of countries launched (Number of countries where the molecule has been launched) * (-) Number of countries launched squared * (+) Multinational (Firm has launched drugs in 10+ countries) * (+) Domestic firm (taking 1 if headquarters are located in the country) * (-) Portfolio (total number of firm’s drug) 1482 molecules Only NCE and patent products 1980-2000 PJB Pc
130 Modeling Global Pricing and Launching of New Drugs Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc * (+) Common language Common border Common regulations * (+) Country experience (Count of firm’s other drugs launched in country) Country-class experience (count of firm’s drug in country-class market) * (+) Experience years (number of years firm has marketed in country) * (-) Price controls (dummy variable: country using price controls) Population (country population) Population squared GDP per capita * (-) Number of new drug in the market (Count of drugs in market launched less than 5 years ago) * (+) Number of new drug in the market squared (Count of drugs in market launched less than 5 years ago) * (-) Number of old drugs in market (Count of drugs in market launched more than 5 years ago) * (+) Number of old drugs in market squared (Count of drugs in market launched more than 5 years ago) Number of potential competitors (Count of drugs launched in class elsewhere in the world) Number of domestic incumbents Number of foreign incumbents Therapeutic fixed effects
Appendix A 131 Reference Dependent Variable Focus EffectaIndependent Variables Sample Period Data Source TFc (n.a.) Country fixed effects (n.a.) Year fixed effects Kyle (2007) Hazard Launch Regulation * (+) Number of drugs in the market (Count of drugs in therapeutic classcount market) Number of potential entrants * (+) Drug importance (Drug's share of stock of Medline citations for class) * (+) Number of countries launched in ( Number of countries where the molecule has been launched) * (+) Prior launch in a high-price country * (+) Prior launch in a low-price country * (-) Firm is headquartered in a price-controlled country * (-) Portfolio ( total number of firm’s drug) * (+) Domestic firm (taking 1 if headquarters are located in the country ) * (+) International experience (Count of countries in which firm has launched any drugs) * (+) Country experience ( Count of firm’s other drugs launched in country) * (-) Price freeze * (-) Price controls * (-) Supply-side controls * (-) Price Rank * (+) Price Rank*post-1995 period Prescribing budgets 1444 molecules Only NCE and patent products 1980-1999 PJB P
References ABRAHAM, J. 2002. The pharmaceutical industry as a political player. Lancet, 360, 14981502. ATELLA, V., BHATTACHARYA, J. & CARBONARI, L. 2012. Pharmaceutical Price Controls and Minimum Efficacy Regulation: Evidence from the United States and Italy. Health Services Research, 47, 293-308. BARDEY, D., BOMMIER, A. & JULLIEN, B. 2010. Retail price regulation and innovation: reference pricing in the pharmaceutical industry. J Health Econ, 29, 303-16. BAYUS, B. L., KANG, W. & AGARWAL, R. 2007. Creating Growth in New Markets: A Simultaneous Model of Firm Entry and Price*. Journal of Product Innovation Management, 24, 139-155. BERNDT, E. R., COCKBURN, I. M. & GRILICHES, Z. 1996. Pharmaceutical Innovations and Market Dynamics: Tracking Effects on Price Indexes for Antidepressant Drugs. Brookings Papers on Economic Activity: Microeconomics 1996,. BERNDT, E. R., DANZON, P. M. & KRUSE, G. B. 2007. Dynamic competition in pharmaceuticals: cross-national evidence from new drug diffusion. Managerial and Decision Economics, 28, 231-250. BORRELL, J. R. 2007. Pricing and patents of HIV/AIDS drugs in developing countries. Applied Economics, 39, 505-518. CABRALES, A. & JIMENEZ-MARTIN, S. 2007. The Determinants of Pricing in Pharmaceuticals: Are U.S. Prices Really Higher than Those of Canada? SSRN eLibrary. CLAXTON, K., MARTIN, S., SOARES, M., RICE, N., SPACKMAN, E., HINDE, S., DEVLIN, N., SMITH, P. C. & SCULPHER, M. 2013. Methods for the Estimation of the NICE Cost Effectiveness Threshold. York: Centre for health Economics, University of York. COCKBURN, I. M. & ANIS, A. H. 1998. Hedonic Analysis of Arthritis Drugs. National Bureau of Economic Research Working Paper Series, No. 6574. COMANOR, W. S. & SCHWEITZER, S. O. 2007. Determinants of drug prices and expenditures. Managerial and Decision Economics, 28, 357-370. CORONADO, F. J., JIMÉNEZ-MARTÍN, S. & MARÍN, P. L. 2007. Multimarket Contact in Pharmaceutical Markets. SSRN. DANZON, P. M. 1997. Price Discrimination for Pharmaceuticals: Welfare Effects in the US and the EU. International Journal of the Economics of Business, 4, 310-322. DANZON, P. M. & CHAO, L. W. 2000. Cross-national price differences for pharmaceuticals: How large, and why? Journal of Health Economics, 19, 159-195. DANZON, P. M. & EPSTEIN, A. J. 2008. Effects of Regulation on Drug Launch and Pricing in Interdependent Markets. National Bureau of Economic Research, Inc, NBER Working Papers: 14041.
236 Modelling Global Pricing and Launching of New Drugs DANZON, P. M., MULCAHY, A. W. & TOWSE, A. K. 2011. Pharmaceutical Pricing in Emerging Markets: Effects of Income, Competition and Procurement. National Bureau of Economic Research Working Paper Series, No. 17174. DANZON, P. M. & TOWSE, A. 2003. Differential Pricing for Pharmaceuticals: Reconciling Access, R&D and Patents. International Journal of Health Care Finance and Economics, 3, 183-205. DANZON, P. M., WANG, Y. R. & WANG, L. 2005. The impact of price regulation on the launch delay of new drugs - evidence from twenty-five major markets in the 1990s. Health Economics, 14, 269-292. DJANKOV, S., LA PORTA, R., LOPEZ-DE-SILANES, F. & SHLEIFER, A. 2002. The Regulation of Entry. Quaterly Journal of Economics, 117. DONALDSON, C., BAKER, R., MASON, H., JONES-LEE, M., LANCSAR, E., WILDMAN, J., BATEMAN, I., LOOMES, G., ROBINSON, A. & SUGDEN, R. 2011. The social value of a QALY: raising the bar or barring the raise? BMC health services research, 11, 8. EKELUND, M. & PERSSON, B. 2003. Pharmaceutical pricing in a regulated market. Review of Economics and Statistics, 85, 298-306. ESPIN J, ROVIRA J & OLRY DE LABRY A 2011. WHO/HAI Project on Medicine Prices and Availability. Review Series on Pharmaceutical Pricing Policies and Interventions. Working Paper 1: External Reference Pricing. FELDMAN, R. & LOBO, F. 2013. Competition in prescription drug markets: the roles of trademarks, advertising, and generic names. European Journal of Health Economics, 14, 667-675. GANSLANDT, M. & MASKUS, K. E. 2004. Parallel imports and the pricing of pharmaceutical products: Evidence from the European Union. Journal of Health Economics, 23, 1035-1057. GARCIA MARIÑOSO, B., JELOVAC, I. & OLIVELLA, P. 2011. External referencing and pharmaceutical price negotiation. Health Economics, 20, 737-756. GARCÍA-MARIÑOSO, B. & OLIVELLA, P. 2012. Informational spillovers and strategic launch delay of pharmaceutical drugs. Mimeo. GAREN, J. 1984. The returns to schooling: A selectivity bias approach with a continuous choice variable. Econometrica: Journal of the econometric society, 1199-1218. GINARTE, J. C. & PARK, W. G. 1997. Determinants of patent rights: A cross-national study. Research Policy, 26, 283-301. GRABOWSKI, H., VERNON, J. & DIMASI, J. A. 2002. Returns on research and development for 1990s new drug introductions. Pharmacoeconomics, 20, 11-29. HECKMAN, J. J. 1979. Sample selection bias as a specification error. Econometrica: Journal of the econometric society, 153-161. HEUER, A., MEJER, M. & NEUHAUS, J. 2007. The National Regulation of Pharmaceutical Markets and the Timing of New Drug Launches in Europe. Kiel advanced studies working papers, No. 437. HOFSTEDE, G. 1984. Culture's consequences: International differences in work-related values, sage. HOFSTEDE, G. 2012. Hofstede Centre [Online]. Available: http://geert-hofstede.com/thehofstede-centre.html. HOFSTEDE, G. H. 2001. Culture's consequences: Comparing values, behaviors, institutions and organizations across nations, Sage. HOTELLING, H. 1929. Stability of competition. Economic Journal, 39, 41-57.
References 237 HUNTER, D. 2005. Guaranteed future pain and suffering: The recent research on drug price controls. Heritage Foundation WebMemo. JELOVAC, I. 2008. On the relationship between the negotiated price of pharmaceuticals and the patients’ co-payment. Center for Economic Development, Hong Kong University of Science and Technology WP 0208. JELOVAC, I. & HOUY, N. 2013. Drug launch timing and international reference pricing. GATE Lyon St Etienne WP 1301. JENKINS, S. P. 2008. Survival Analysis . Unpublished manuscript: Institute for Social and Economic Research, University of Essex, Colchester. Ohio State University. KANAVOS P & COSTA-FONT J 2005. Pharmaceutical parallel trade in Europe: stakeholder and competition effects. Economic Policy, 20, 751-798. KANAVOS, P. G. & VANDOROS, S. 2011. Determinants of branded prescription medicine prices in OECD countries. Health Economics Policy and Law, 6, 337367. KEELE, L. 2007. Cross validation tests for frailty models. Unpublished manuscript, Ohio State University. KIEFER, N. M. 1988. Economic duration data and hazard functions. Journal of economic literature, 26, 646-679. KLEIN, J. P. & MOESCHBERGER, M. L. 1997. Statistics for biology and health. Survival analysis: techniques for censored and truncated data. KYLE, M. K. 2006. The role of firm characteristics in pharmaceutical product launches. RAND Journal of Economics, 37, 602-618. KYLE, M. K. 2007. Pharmaceutical price controls and entry strategies. Review of Economics and Statistics, 89, 88-99. LANCASTER, T. 1992. The econometric analysis of transition data, Cambridge university press. LANJOUW, J. O. 2005. Patents, Price Controls, and Access to New Drugs: How Policy Affects Global Market Entry. National Bureau of Economic Research Working Paper Series, No. 11321. LEE, L.-F. 1983. Generalized econometric models with selectivity. Econometrica: Journal of the econometric society, 507-512. LEOPOLD, C., VOGLER, S., MANTEL-TEEUWISSE, A. K., DE JONCHEERE, K., LEUFKENS, H. G. & LAING, R. 2012. Differences in external price referencing in Europe: a descriptive overview. Health Policy, 104, 50-60. LICHTENBERG, F. R. 2005. The Impact of New Drug Launches on Longevity: Evidence from Longitudinal, Disease-Level Data from 52 Countries, 1982–2001. International Journal of Health Care Finance and Economics, 5, 47-73. LOBO, F. 2014. La Intervención de Precios de los Medicamentos en España, Madrid, Springer. LOBO, F. & FELDMAN, R. 2013. Generic Drug Names and Social Welfare. Journal of Health Politics Policy and Law, 38, 573-597. LOPEZ-CASASNOVAS, G. & PUIG-JUNOY, J. 2000. Review of the literature on reference pricing. Health Policy, 54, 87-123. LU, Z. J. & COMANOR, W. S. 1998. Strategic pricing of new pharmaceuticals. Review of Economics and Statistics, 80, 108-118. MASON, H., JONESLEE, M. & DONALDSON, C. 2009. Modelling the monetary value of a QALY: a new approach based on UK data. Health Economics, 18, 933-950. MESTRE-FERRANDIZ, J. 2003b. Reference prices: The Spanish way. Investigaciones Económicas, 27, 125-149.
238 Modelling Global Pricing and Launching of New Drugs MESTRE-FERRANDIZ, J. 2012. The R&D Cost of a New Medicine London: Office of Health Economics. MESTRE-FERRANDIZ, J. 2013. Increasing R&D costs for new medicines. Chemistry & Industry, 77, 44-44. MIRALDO, M. 2009. Reference pricing and firms' pricing strategies. J Health Econ, 28, 176-97. MOHER, D., LIBERATI, A., TETZLAFF, J., ALTMAN, D. G. & GROUP, A. T. P. 2009. Reprint—Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. Physical Therapy, 89, 873-880. MORENO, E., GIRON, F. J., VAZQUEZ-POLO, F. J. & NEGRIN, M. A. 2010. Optimal healthcare decisions: Comparing medical treatments on a cost-effectiveness basis. European Journal of Operational Research, 204, 180-187. MORENO-TORRES, I., PUIG-JUNOY, J. & BORRELL, J. R. 2009. Generic Entry into the Regulated Spanish Pharmaceutical Market. Review of Industrial Organization, 34, 373-388. MUTHOO, A. 1999. Bargaining Theory with Applications, Cambridge University Press. NATIONAL INSITUTE FOR HEALTH AND CARE EXCELLENCE (NICE). 2010. Measuring effectiveness and cost effectiveness: the QALY [Online]. NICE. Available: http://www.nice.org.uk/newsroom/features/measuringeffectivenessandcosteffectiv enesstheqaly.jsp. NATIONAL INSTITUTE FOR HEALTH AND CARE EXCELLENCE (NICE). 2010. Measuring effectiveness and cost effectiveness: the QALY [Online]. NICE. Available: http://www.nice.org.uk/newsroom/features/measuringeffectivenessandcosteffectiv enesstheqaly.jsp. NEGRIN, M. A. & VAZQUEZ-POLO, F. J. 2006. Bayesian cost-effectiveness analysis with two measures of effectiveness: the cost-effectiveness acceptability plane. Health Economics, 15, 363-372. NEGRIN, M. A. & VAZQUEZ-POLO, F. J. 2008. Incorporating model uncertainty in costeffectiveness analysis: A Bayesian model averaging approach. Journal of Health Economics, 27, 1250-1259. NEGRIN, M. A., VAZQUEZ-POLO, F. J., MARTEL, M., MORENO, E. & GIRON, F. J. 2010. Bayesian Variable Selection in Cost-Effectiveness Analysis. International Journal of Environmental Research and Public Health, 7, 1577-1596. NIHR, H. 2010. Weighting and valuing quality-adjusted life-years using stated preference methods: preliminary results from the Social Value of a QALY Project. Health Technology Assessment, 14. OECD 2011. Health at a Glance 2011: OECD Indicators. Paris. PARIS, V., DOCTEUR, E. & MOÏSE, P. 2008. Pharmaceutical pricing polices in a global market. OECD Health Policy Studies. Paris. PARK, W. G. & WAGH, S. 2002. Index of patent rights. Economic freedom of the world: 2002 annual report, 33-43. PINTO-PRADES, J. L., LOOMES, G. & BREY, R. 2009. Trying to estimate a monetary value for the QALY. Journal of Health Economics, 28, 553-562. PUIG-JUNOY, J. 2007. The impact of generic reference pricing interventions in the statin market. Health Policy, 84, 14-29. PUIG-JUNOY, J. 2010a. Policies encouraging price competition in the generic drug market: Lessons from the European experience. Gaceta Sanitaria, 24, 193-199.
References 239 PUIG-JUNOY, J. 2010b. Impact of European Pharmaceutical Price Regulation on Generic Price Competition A Review. Pharmacoeconomics, 28, 649-663. PUIG-JUNOY, J. 2012. Lecturas sobre Economía del Sector Biofarmacéutico, Madrid, Springer Healthcare. PUIG-JUNOY, J. & GONZÁLEZ LÓPEZ-VALCÁRCEL, B. 2014. Launch prices for new pharmaceuticals in the heavily regulated and subsidized Spanish market. Health Policy, Forthcoming. RAWLINS, M. D. 2012. Crossing the fourth hurdle. Br J Clin Pharmacol, 73, 855-60. RICHTER, A. 2008. Assessing the impact of global price interdependencies. Pharmacoeconomics, 26, 649-659. ROBINSON, A., GYRD-HANSEN, D., BACON, P., BAKER, R., PENNINGTON, M. & DONALDSON, C. 2013. Estimating a WTP-based value of a QALY: The ‘chained’approach. Social Science & Medicine, 92, 92-104. RUBINSTEIN, A. 1982. Perfect Equilibrium in a Bargaining Model. Econometrica, 50, 97109. SCHERER, F. M. 1993. PRICING, PROFITS, AND TECHNOLOGICAL-PROGRESS IN THE PHARMACEUTICAL-INDUSTRY. Journal of Economic Perspectives, 7, 97115. SCHERER, F. M. 2004. The pharmaceutical industry-Prices and progress. New England Journal of Medicine, 351, 927-932. STARGARDT, T. & SCHREYÖGG, J. 2006. Impact of Cross-Reference Pricing on Pharmaceutical Prices: Manufacturers’ Pricing Strategies and Price Regulation. Applied Health Economics and Health Policy, 5, 235-247. STATACORP 2009. Stata Statistical Software. College Station, TX: StataCorp LP. SUSLOW, V. Y. 1996. Measuring Quality Change in the Market for Anti-Ulcer Drug. In: HELMS, R. M. & (ED) (eds.) In Competitive Strategies in the Pharmaceutical Industry. Washington, DC: American Enterprise Institute: AEI Press. TIMUR, A., PICONE, G. & DESIMONE, J. 2011. Has the European union achieved a single pharmaceutical market? International Journal of Health Care Finance and Economics, 11, 223-244. VELLA, F. 1993. A simple estimator for simultaneous models with censored endogenous regressors. International Economic Review, 441-457. VERNIERS, I., STREMERSCH, S. & CROUX, C. 2011. The global entry of new pharmaceuticals: A joint investigation of launch window and price. International Journal of Research in Marketing, 28, 295-308. WAGNER, J. L. & MCCARTHY, E. 2004. International differences in drug prices. Annu. Rev. Public Health, 25, 475-495. WHO 2003. Making Choices in Health: WHO Guide to Cost-Effectiveness Analysis Geneva, World Health Organization. WHO. 2012. ATC/DDD Index 2012 [Online]. Available: http://www.whocc.no/atc_ddd_index/. WILKING, N., JÖNSSON, B. & I STOCKHOLM, H. 2005. A pan-European comparison regarding patient access to cancer drugs. Karolinska Institutet. WOOLDRIDGE, J. M. 2002. Econometric analysis of cross section and panel data, The MIT press.