DOCUMENT DE TREBALL XREAP2011-04

Generic drugs in Spain: price competition vs. moral hazard
Ivan Moreno-Torres (Competition Authority of Catalonia, CRES & GiM-IREA)

Generic drugs in Spain: price competition vs. moral hazard
Ivan Moreno-Torres∗
May 2011

Abstract This paper examines competition between generic and brand-name drugs in the regulated Spanish pharmaceutical market. A nested logit demand model is specified for the three most consumed therapeutic subgroups in Spain: statins (anticholesterol), selective serotonin reuptake inhibitors (antidepressants) and proton pump inhibitors (antiulcers). The model is estimated with instrumental variables from a panel of monthly prescription data from 1999 to 2005. The dataset distinguishes between three different levels of patients’ copayments within the prescriptions and the results show that the greater the level of insurance that the patient has (and therefore the lower the patient’s copayment), the lower the proportion of generic prescriptions made by physicians. It seems that the low level of copayment has delayed the penetration of generics into the Spanish market. Additionally, the estimation of the demand model suggests that the substitution rules and promotional efforts associated with the reference pricing system have increased generic market share, and that being among the first generic entrants has an additional positive effect. Keywords: pharmaceutical industry, generic competition, copayment, moral hazard JEL classifications: I11, I18, L13, L65

Competition Authority of Catalonia (ACCO), Generalitat de Catalunya; and research fellow of the Center for Research in Economics and Health (CRES), Universitat Pompeu Fabra, and of the Research Group on Governments and Markets (GiM), Universitat de Barcelona. E-mail: imorenot@gencat.cat

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Contents 1. Introduction ............................................................................................... 2 2. Generic competition .................................................................................. 3 3. The Spanish pharmaceutical market......................................................... 7 4. Data ...........................................................................................................10 5. Empirical strategy.....................................................................................14 6. Results.......................................................................................................24 7. Discussion.................................................................................................26 8. Concluding remarks .................................................................................29 Acknowledgments ........................................................................................31 References.....................................................................................................32 Figures and tables ........................................................................................37

1. Introduction Drug expenditure is one of the fastest growing components of health expenditure in most countries (OECD, 2006) and knowing more about the factors that affect generic competition, which is a potential way of reducing spending, is very useful for the design of pharmaceutical policies. One important feature of the Spanish pharmaceutical market is that it is heavily regulated. The maximum price of each medicine is established individually and since December 1999, there has been a reference pricing system by which the National Health System (NHS) sets the maximum reimbursement for drugs when generic versions are available. This allows the competition of generic medicines to be studied in a regulated context and, since the NHS funds most prescription drug consumption, it is especially interesting to analyse how generic competition is affected by the level of insurance or reimbursement.

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The main aim of this paper is thus to analyse to what extent market share depends on the level of patient copayment. The Spanish case, with three different levels of copayment (pensioner, non-pensioner and the chronically ill) is particularly interesting. Additionally, among other factors that may influence the success of generic competition, the effects of the application and modification of the reference pricing system and the order of entry into the market are of special concern. I contribute with empirical findings regarding these three important issues that affect generic competition. Most of the results I report could be extended to a great number of countries with similar institutional settings and pharmaceutical market characteristics: heavy regulation and a relatively low market penetration of generics. The outline of the paper is as follows. The next section highlights the idiosyncrasy of the pharmaceutical market and generic competition. The third section explains the main features of the Spanish pharmaceutical market. The fourth section describes the dataset. The section after that presents the demand model and considers estimation issues. In the sixth and seventh sections I present and discuss the estimation results, respectively. Finally, the last section offers the concluding remarks. 2. Generic competition When brand-name drugs are no longer protected by patents or other legal rules, generic equivalents can enter the market. Such drugs contain the same active

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ingredients and have proved bioequivalence1 with the original product, to the satisfaction of health authorities. However, the manufacturer and certain characteristics (such as colour, shape, inactive ingredients or packaging) may be different. Therefore, they may be considered substitutes for the brand-name drugs but not perfect substitutes: in other words, there is some degree of differentiation. The expected result of generics entering the market is an increase in the level of competition and possibly a reduction in prices and the brand-name product’s market share. It is also expected that when the number of generics on the market is considerable, prices tend towards the cost of production. However, for brand-name products, the American literature shows some evidence of prices increases after the entry of generics into the market (for instance: Grabowski and Vernon, 1992; Frank and Salkever, 1997): this has been called the “generic paradox” (Scherer, 1993). In the pharmaceutical industry, the institutional setting is very important and affects market competition. The dispensing process is complex and the physician, the pharmacist, the third-party payer (a public health insurer in most countries) and the patient all play a role. The physician and the pharmacist may be perfect agents for the patient in which case their choices (together with those of the patient) will maximize utility for the patient. In contrast, the physician or the pharmacist may be perfect agents for the third-party payer (ultimately tax-payers when there is a public

Pharmaceutical products are bioequivalent if they are pharmaceutically equivalent or pharmaceutical alternatives, and display comparable bioavailability, when studied under similar experimental conditions. Bioequivalence is considered proven if the bioavailabilities, in terms of peak and total exposure after administration of the same molar dose under the same conditions, are similar to such a degree that the effects of the studied products can be expected to be essentially the same.
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insurer) or even, in some countries, for themselves2. There is also room for intermediate situations. The process starts when the physician decides to prescribe a drug. He may choose to prescribe a generic or a brand-name drug. Hellerstein (1998) found that physicians play an important role in determining whether patients receive brandname or generic pharmaceuticals and that some are more likely to prescribe generics while others are more likely to prescribe brand-name products. The practitioner, as the patient’s agent, may choose a brand-name drug if he believes that it is of better quality and is not concerned about the cost of the drug. Moreover, the physician may prefer the brand-name drug because of his experience with the product over the period of exclusivity, during patent protection, or because there are no incentives to change prescription habits (López-Casanovas and Puig-Junoy, 2000). This brand-name loyalty of the practitioner may explain why, in some markets, brand-name products enjoy large market shares even when there are cheaper generic drugs available. Coscelli (2000) found that in addition to the physician, patient characteristics also affect the prescription decision. Since brand-name and generic drugs may differ in inert ingredients as well as in colour and shape, some patients may wish not to change from the brand-name drug they are used to taking to a generic version. This may be particularly true if they have doubts about quality and do not pay for the

Iizuka (2007) analyzes the trade-off for physicians between being good agents for themselves or for the patients in the Japanese pharmaceutical market.
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drug or make only a small copayment; the patient may have a preference to consume brand-name drugs and may influence the physician’s choice. Apart from the physician and the patient, the pharmacist may participate in the choice of drug, for instance by choosing which generic to dispense. When a generic drug is dispensed, there is competition between different generic products. As stated by Yu and Gupta (2008), the choice at this stage is mainly based on which generics are available at the pharmacy, that is, the pharmacist’s choice of a generic producer within the market defined by the active ingredient. The dispensing process follows a multi-level market structure, as shown in Figure 1. The first choice is between whether to dispense a drug from a therapeutic subgroup or to prescribe an alternative treatment for the condition, such as a drug from another related therapeutic subgroup, whether to take homeopathic products, whether to take exercise, and so on. If a therapeutic subgroup is chosen, the next step is to decide on an active ingredient. The third decision is to choose a specific presentation from all the possibilities for the molecule, in other words, to choose a combination of the strength and the number of units. The following stage is the choice between branded and generic drugs. And finally, there is the choice from among the different producers. Furthermore, the public payer may affect the decision by implementing obligatory substitution rules and requirements that limit reimbursements, such as the reference pricing system.

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3. The Spanish pharmaceutical market The Spanish NHS is funded from tax revenue and provides health care services with copayments for prescribed pharmaceuticals. The standard rate of copayment is 40%, but the average copayment is very low and accounts for less than 7% of the total expenditure on ambulatory prescription pharmaceuticals, since prescription drugs for pensioners and some other specific groups, such as the handicapped or people who have suffered occupational accidents and their dependents, have no charge; and drugs indicated specially for chronic diseases have a rate of only 10% with an upper limit. The prescription market dominates sales: the share of prescription drugs is 85.50% of volume and 92% of total sales, and over-the-counter (OTC) medicines account for the remaining 14.50% of volume and 8% of total sales (Costa-Font and PuigJunoy, 2005). Another important feature of the Spanish market is that there are a great number of different presentations of drugs due to there still being considerable numbers of copies and also to the entry of new drugs into the market. In fact, there are three types of prescription drugs in Spain: original brand-name drugs (which might be marketed either by the patent holder or by a licensee), copy brand-name drugs and generics. This is due to the fact that, although Spain joined the European Patent Convention in 1986, it did not grant product patent rights until 8 October 1992, due to a transitional period in accordance with Article 167 of that convention. Before 1992, there were only process patents. Thus, older drugs are marketed

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simultaneously in the form of branded original products, branded licensed products, branded copy products and generics. Although generic drugs were introduced into Spain in 1997, the market share of generic medicines was low at the end of the period analysed: only 14.60% in units and 7.90% in value (IMS Health, 2006b). Generics obtain market authorization when one has already been authorized in another European Union (EU) member state in which the original drug enjoyed product patent protection, or when ten years have passed since the original brand-name drug was released onto the Spanish market (the “ten-year rule”). Although Spain is a relatively low-price country with limited generic penetration, in December 1999 a reference pricing system was introduced for off-patent drugs with the same active ingredient (chemical equivalence). This policy has gradually been extended to reimburse payment for a growing list of active chemical ingredients. All versions of off-patent drugs, branded and generics, were included in their respective group of bioequivalent drugs and a group was created once there was at least one generic version of the respective active ingredient. The reference price was determined endogenously as a function of drug prices in the relevant market: for each group, a reference price was calculated as the weighted average selling price of the lowest-priced drug accounting for at least 20% of the market (year on year)3.

If the difference between this price and the highest price for the group was less than 10%, the reference price was the result of applying a 10% reduction to the highest price. If the difference between the calculated price and the highest priced product was more than 50%, the reference price was exactly 50% of the highest priced product. Whatever the situation, the reference price was never lower than the generic with the lowest price (López-Casasnovas and Puig-Junoy, 2000).
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This system established the maximum price that could be reimbursed by the NHS for any version of the same drug. Whenever the price of any prescribed drug was higher than the reference price, patients could opt for the prescribed drug by paying the difference between its price and the reference price. So, it implied a copayment that depended on the price of the chosen drug and that could be avoided if the drug was cheaper than the reference price4. However, since January 2004 the reference price has been calculated as the average of the three lowest costs per day of treatment, for each form of administration of an active ingredient, according to its defined daily dose (DDD)5. With this new system, if prescriptions specify drugs priced higher than the reference price, pharmacists are obliged to substitute them with the cheapest generic version. However, if prescriptions specify drugs priced equal to or lower than the reference price, pharmacists are not obliged to substitute them. When the prescription has been written using the name of the active ingredient, the pharmacist has to dispense the lowest-priced generic drug. In this way, reference pricing has become a system for establishing the maximum reimbursement price that a drug may have without being excluded from the list of publicly financed drugs, that is, a kind of price capping system.

In fact, the part of the price under the reference price may also involve a non-avoidable copayment. For instance, non-pensioners pay 40% of the price under the reference price. The avoidable copayment is the total amount above the reference price and is in addition to the nonavoidable copayment. 5 A defined daily dose is the average dose per day in adults for a drug when it is used to treat its main indication.
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Moreover, the maximum ex-factory price of all drugs (branded and generic) is set during the process of obtaining market approval, and usually the introduction price remains the maximum price for most of the life of the product (Borrell, 2003). The government uses a peculiar form of cost-based price regulation for branded drugs in which manufacturing, marketing and research costs, as well as an industrial profit on invested capital, are allocated to new drugs. However, this is rarely the final price since the legislation allows other factors to be considered, such as the price of the same product in other European countries, the price of drugs that can be considered substitutes or the therapeutic innovation of the medicine. In fact, the legal criteria are that the price has to reflect the therapeutic value of the drug as well as the cost of comparable treatments, the price of the same drug in other countries, and some other political issues such as the contribution to the national economy (Antoñanzas et al., 2007). The wholesalers’ and retailers’ mark-ups are also regulated6. 4. Data I use a dataset of monthly prescription drug consumption from 1999 to 2005, provided by the Directorate-General of Pharmacy and Health Products of the Spanish Ministry of Health and Consumer Affairs, which is complemented with data from the Nomenclator Digitalis of the NHS Health Information Institute (Ministerio de Sanidad y Consumo, 2005) and from the Base de Datos del Conocimiento

More details of Spanish regulations and reimbursement policies are available in Nonell and Borrell (2001), Puig-Junoy (2007) and Costa-Font and Puig-Junoy (2005).
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Sanitario 2005 - BOT PLUS (Consejo General de Colegios Oficiales de Farmacéuticos, 2005). I analyse outpatient data for non-paediatric oral prescription drugs containing only one active chemical ingredient. As Table 1 shows, the sample includes 15 market presentations for seven active ingredients in the three most consumed therapeutic subgroups in Spain: statins (HMG-CoA reductase inhibitors), selective serotonin reuptake inhibitors (SSRIs; antidepressants) and proton pump inhibitors (antiulcer agents). The data is not a sample but the entire market for these drugs: all the drugs sold in Spain and financed (at least partially) by the NHS. The active ingredients from the statin subgroup are lovastatin and simvastatin; from the antidepressant subgroup they are citalopram, fluoxetine, fluvoxamine and paroxetine; and from the antiulcer subgroup the active ingredient is omeprazole. As the Table 1 shows, the different active ingredients have several doses and are sold in different presentations, therefore, all the quantities sold are converted into common units. For each presentation I calculate the total milligrams of the active ingredient and transform this into patient days using the DDD, that is, I calculate the total number of DDDs per package7. Prices are calculated from the dataset by dividing volumes of sales in euros by the quantities sold. Then, for each product I calculate the price per DDD.

For this purpose I use the DDD of each active ingredient established by the World Health Organization (WHO) Collaborating Centre for Drug Statistics Methodology. The use of this international standard dosage enables standardization and comparison of drug quantities across therapeutic groups, active ingredients and presentations.
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Table 1 also shows the indications of each active ingredient together with the date of entry of the first generic drug and of the implementation of the reference pricing system for the first time, for each market presentation. Statins are used to lower cholesterol levels in people with, or at risk of, cardiovascular disease. SSRIs are a class of antidepressants mainly used in the treatment of depression, anxiety disorders and some personality disorders. Proton pump inhibitors are a subgroup of antiulcer drugs whose main action is a reduction of gastric acid production and they are used in the treatment of many conditions, such as peptic ulcer. The panel data is unbalanced since different drugs fulfilled the legal requirements of generic entry and entry happened at different times, however, each market presentation is observed for at least 35 months. There are a total of 23,584 observations involving 318 different presentation forms and 51 manufacturers. A total of 11,839 observations are of pensioners’ consumption, and the remaining 11,745 are from non-pensioners’ prescriptions. Of the latter, 4,410 observations correspond to the consumption of products indicated for chronic diseases8. Each separate market is formed of medicines that compete with other close but not perfect substitute medications. In the pharmaceutical industry, defining the market is not easy since medicines are indicated for treatment of different conditions. Although it is an imperfect approach, some authors (for instance, Aronsson, Bergman and Rudholm, 2001; Dalen, Strøm and Haabeth, 2006; Moreno-Torres,

All the medicines in the antidepressant class are considered for chronic treatment and have a reduced 10% rate of copayment for non-pensioners; the remaining drugs, in other therapeutic groups, have a 40% rate of copayment for non-pensioners.
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Puig-Junoy and Borrell, 2009) take the therapeutic active ingredient level according to the WHO’s Anatomical Therapeutic Chemical (ATC) classification as their approach. In order to tackle the non-linearity of the differently-sized presentation prices and to take advantage of variation among markets to identify parameters, I use the presentation (active ingredient with a specific dosage and size) as the market. Furthermore, the reference pricing system is frequently applied to different presentations, even for the same active ingredient, at different times and therefore these dynamics allow the effects of this system to be identified. Figure 2 shows the evolution of the number of generics in three representative markets, one in each therapeutic class. The usual pattern is a marked increase in the number of generic competitors during the first years after the loss of market protection and stabilization at the end of the series. Table 2 shows the evolution of the structure of the market at the end of each of the years analysed. There is a clear increase in the number of generic manufacturers for the whole dataset. The increase in their market share was also considerable until 2003; however, since then, the incorporation into the dataset of new markets with less generic penetration is reflected in a recovery of brand-name products. Moreover, there is a reduction in prices per DDD for brand-name and generic products; the latter always have lower prices than the former.

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Figures 3 shows the evolution of market shares for the three representative markets and illustrates an interesting issue: in the prescriptions for pensioners, the generic market share is lower than in the prescriptions for non-pensioners. Figure 4 displays the evolution of the average brand-name and generic prices per DDD for the three same markets. Generic prices are almost always lower than brand-name prices although there is some convergence throughout the time. Finally, Figure 5 displays that the market shares of the first three (groups of) generics are usually greater than the average market share from the sixth entrant on for the same sample. 5. Empirical strategy I use a structural discrete choice model of product differentiation. In this model the utility for consumers depends on product characteristics and individual tastes; firms are modelled as price-setting oligopolists and endogenous market outcomes are derived from an assumption of a Nash equilibrium in prices. This kind of demand model has been applied to many products such as ready-to-eat cereals (Nevo, 2000a and 2001), yogurts (Di Giacomo, 2008), movie theatres (Davis, 2006) and cars (Berry, Levinsohn and Pakes, 1995; Verboven, 1996; or Petrin, 2002). They have also been used to analyse demand for pharmaceuticals by several authors such as Stern (1996), Cleanthous (2004), Dalen, Strøm and Haabeth (2006), Iizuka (2007), Yu and Gupta (2008), Kaiser, Mendez and Rønde (2010) and Coronado (2010). As my main interest is how copayment affects competition between brand-name drugs and generics, as well as considering competition between generics, I use a

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two-level nested logit model based on Berry (1994) and especially on the application of such a model to the pharmaceutical market by Yu and Gupta (2008)9. As stated by Berry (1994), in contrast to the simple multinomial logit model, the nested logit model allows consumer tastes to be correlated across products. There is correlation between the idiosyncratic shocks of products in the same segment of the market. This prevents the independence from irrelevant alternatives property and allows for reasonable substitution patterns. In comparison to the random coefficients alternative10, the nested logit model may be sensitive to the specification of the nest structure because the researcher chooses the options that are potentially close a priori11. However, in the case of pharmaceuticals this is not a crucial pitfall since it is possible to use the ATC classification to build the structure of the nests12. In this demand model drugs are grouped into mutually exclusive markets. In each market, for instance “omeprazole 20 milligrams 14 capsules”, there are the brandname and the generic nests. In each generic nest there is a set of drugs denoted j = 1, …, J. I group all the different brand-name drugs within a single nest (original brand-name and copy brand-name drugs) so there is only one possibility available in
An alternative approach is the multistage budgeting model applied by Ellison et al. (1997) to analyze the demand for cephalosporins. 10 The nested logit model can be interpreted as a special case of the random coefficients model with random coefficients only on group-specific dummy variables (Berry, 1994). 11 For those cases in which the order of the nests is important, Bresnahan, Stern and Trajtenberg (1997) developed the principles-of-differentiation general extreme-value model. 12 The therapeutic groups are formed on the basis of the 4th level of the ATC code that approximates the chemical, therapeutic or pharmacological group (for example, N06AB for SSRIs: antidepressants). The pharmaceutical presentations within a group can be considered close substitutes since they have the same active chemical ingredient (5th level of the ATC code: the group for the chemical substance; for instance, N06AB05 for paroxetine).
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this nest. The outside good represents the alternative to choose a generic or brandname drug and is assumed to be the only member of its own group. I assume that the physician chooses a unit of the drug that maximizes utility for the patient, even though the final consumption may be affected by the substitution rules and the availability of the product at the drugstore. The indirect utility function for consumer i of consuming the generic drug j in the period t is:
u ijt = δ jt + ∑ d jg ζ ig + (1 − σ )ε ijt
g

[

]

(1)

where δ jt is the mean utility level of product j, which is the same for all consumers, and ε ijt is an identically and independently distributed extreme value random variable that represents the distribution of consumer preferences about the mean utility. d jg is a dummy variable that is equal to one for drugs in a specific market, g, and zero otherwise; and so, ζ ig is common to all products in market g and has a distribution function that depends on the parameter σ, with 0 ≤ σ < 1. This parameter measures the within-nest correlation of utility levels and allows to include the correlation between groups of similar products. If σ approaches 0, the withingroup correlation of utility levels is low, and the model tends toward the multinomial logit (Besanko, Gupta and Jain, 1998). When σ tends toward 1, the within-nest correlation of utilities approaches 1. As Cardell (1997) proved, an

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additional property of this model is that if ε ijt is an extreme value random variable, [ ζ ig + (1 − σ )ε ijt ] is also an extreme value random variable. Let me define the indirect utility function for the consumer i of consuming a brandname drug as:
uibt = δ bt + ε ibt

(2)

From the derivation by Berry (1994) and following Yu and Gupta (2008), I obtain the following estimating equation:
ln( s jt / sbt ) = (δ jt − δ bt ) + σ ln( s j / g ,t ) + υ jt

(3)

where: ln(s jt / sbt ) is the log of the relative market share between the generic j and the group of brand-name products; ln(s j / g ,t ) is the log of the share of the generic j in the group of generic products in market g; and υ jt is an error term. I assume that the mean utility of the brand-name drugs depends on the quality that consumers perceive these drugs to have due to their being an incumbent product, and I expect that an increase in the average price per DDD reduces brand-name drugs’ market share. For the generic drugs, I assume that the mean utility depends on the months since it has been on the market and on the price per DDD. The former is expected to affect generics’ market shares positively, because generic producers need time to spread their products through markets; while the price is expected to be inversely related to the market share of generic products.

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Thus, the econometric specification to be estimated is:
RPI RPII ln(s jt / sbt ) = α + γ j + XΠ + σ ln(s j / gt ) + τ 1C0 + τ 2C10 + α1Dgt + α 2 Dgt + ωm + υ jt

(4)

The first term at the right-hand side is a constant and the second term represents an order-of-entry fixed effect13. The term X is the matrix of variables that explain the differences in the mean utilities of brands and generics, including the time each generic has been on the market and the logs of prices. Let X also include other variables that may affect utility for consumers and thus generic penetration, such as the number of indications of the active ingredient, the number of DDDs per unit, the number of units in the package and the number of different brand-name products on the market at the moment of patent expiry. Π is a vector of parameters to be estimated. σ is the parameter that measures the level of substitution between generics in a given market and ln(s j / g ,t ) is the log of the within-generic group market share. I incorporate two dummy variables related to copayment levels. The first copayment dummy variable, C 0 , is equal to one for pensioners’ consumptions, because pensioners do not have any out-of-pocket expenses. The second dummy variable,
C10 , is equal to one for the consumption of those products designated for the

chronically ill, in which non-pensioners have a reduced copayment rate of only 10%. Therefore, the group of comparison is formed of the products with the general 40% rate of copayment.
In the strict sense, these are not drug-specific fixed effects because in some cases more than one drug entered the market in the same month and these drugs are grouped into the same dummy variable. Moreover, all the products that enter the market after the 5th entrant (or group of entrants) are grouped together and act as the comparison group.
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My hypothesis is that physicians and pharmacists act as better agents for the patients than for the third-party payer (the NHS or tax-payers) and prescribe and sell more expensive brand-name products to those patients with larger levels of insurance coverage (i.e., lower copayments). Physicians and pharmacists may be influenced by their patients’ interests and thus introduce some degree of moral hazard. Thus, I expect to find larger generic market shares for the chronically ill, with a 10% copayment, and especially for the non-pensioners group, with no copayment reduction, since they have to contribute to the cost of the drug with a copayment rate of 40% and generics are generally cheaper. Some studies, such as Pavcnik (2002) or Regan (2008), find empirical evidence of the effect of copayment on prices and some theoretical papers try to explain the “generic paradox” by appeal to the level of insurance coverage (Kong, 2008; and Ferrara and Kong, 2008). Some papers find evidence of the effect of copayment on the demand for pharmaceuticals in the US market (such as Esposito, 2005; Coulson and Stuart, 1995; Coulson et al., 1995), although their results cannot be applied directly to Spain because the institutional setting is quite different. However, in the Swedish market, which is more similar to the Spanish market, Rudholm (2005) found that when the level of pharmaceutical insurance is greater, not only does the quantity consumed increase but so does the price of the products dispensed, and Lundin (2000) showed that patients with high insurance coverage consume more brand-name drugs (relative to of generics) than patients with lower insurance coverage. When the cost for the patient of a brand-name product

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increases in comparison to the generic version, it is more likely that the doctor will prescribe the generic. Another important issue addressed by Yu and Gupta (2008) is the effect of the order of market entry of generics on their market shares. In the American pharmaceutical industry there is some evidence that earlier entrants make larger profits (for instance Caves, Whinston and Hurwitz, 1991; Berndt et al., 1995; Cook, 1998). In fact, the “generic paradox” may be interpreted as brand-name loyalty derived from a first-mover advantage. In the pharmaceutical market, first-mover advantage brings pricing power and allows the manufacturer to retain a considerable market share as the number of competitors increases. This advantage also seems to hold for the first generic entrants; which retain a considerable market share and have higher prices than later generic entrants. Yu and Gupta (2008) and Kalyanaram (2008) in the US and Hollis (2002) in Canada found evidence of larger market shares for the first or earlier generic entrants. This may be interpreted as consumers obtaining more utility from the first generics on the market. That is why I include fixed effects relating to the order of entry. This is also a relevant issue for competition policy, since some brand-name drug producers preempt the generic segment of the market with their own “branded generics” (also known as “pseudo generics” or “authorized generics”). In other words, incumbent brand-name manufacturers sometimes also manufacture the first generic product (Hollis, 2005; Reiffen and Ward, 2007).

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In Spain, as Reiffen and Ward (2005) indicated in the US market, the timing of entry into the market is not generally under the control of manufacturers. The date of approval by health authorities is not known with certainty, or indeed even if they will obtain the approval, and neither do they know how many other applications for that market will be approved, or when this may happen. Thus, order-of-entry can be considered as exogenous.
RPI I include a dummy variable, Dgt , that is equal to one from the moment the

reference price is applied to the market until December 2003, and equal to zero before the implementation of the reference price (or when it is not implemented)
RPII and after December 2003. A second dummy variable, Dgt , is equal to one when

the reference price is applied to the market from January 2004 onwards, and equal to zero before this moment (or when the reference price is not implemented). In this way, there are three periods for each product: the period previous to the implementation of reference pricing, during which the price capping regulation works; the period in which the first reference price system is applied; and the period of the second reference price system. If a product is not affected by the reference price system, the price capping regulation sets its maximum ex-factory price. The first period acts as the comparison group. Other policies implemented during the period analysed, such as obligatory reductions of ex-factory prices, reductions of pharmacy and wholesaler price margins, or revisions of reference prices, have no dummy variables since all these effects act through the variation in the prices included in the specification.

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It should be considered that, in addition to its effects through the variation of relative prices and copayments, reference pricing is accompanied by specific substitution rules and promotion of generic prescriptions by health authorities. Thus, since prices are introduced in the econometric specification, the parameters
α1 and α 2 will only capture the effects that are additional to those due to price

variations. For instance, the introduction of reference pricing can increase generic market share if it means that brand-name products are relatively more expensive than generics (effect through the prices) but it can also increase generic market share because of obligatory substitution (an effect which is in addition to the price changes). I also introduce firm-specific fixed effects in the model, ω m ; that is, a variable that is equal to one for a specific firm across drugs and markets and to zero otherwise. The role of these fixed effects is to control for time invariant factors in addition to order-of-entry fixed effects, such as product quality or promotional effort, that are usually common to all the products a firm manufacturers. In fact, market shares of competing generic drugs are driven by pharmacists’ choice of a generic producer within the market and generic manufacturers build a reputation and develop idiosyncratic skills in launching and delivering drugs. The quality of the product together with promotional and marketing activity expenses are observable to the consumer but not to the researcher (that is, they are not totally captured by the variables included). These unobserved factors are correlated with the drug price and with the log of a generic drug’s share within the

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group of generics. To partially overcome this pitfall, I use the aforementioned firmspecific dummy variables. These fixed effects capture the mean quality of a drug and the marketing effort invested in it, leaving the time-specific and product-specific deviations as part of the error term. The potential remaining inconsistency in estimation arises from factors that change over time or variation among products from the same firm. To deal with this remaining endogeneity I use instrumental variables. Following the empirical industrial organization literature (Berry, Levinsohn and Pakes, 1995) and papers that consider the pharmaceutical industry (Iizuka, 2007; Stern, 1996), I considered as possible instruments: the number of products a firm and its competitors manufacture; the product characteristics; and the sums of these at different levels of aggregation. Of these available instruments I chose the set with the strongest correlation with the endogenous variables that did not reject the null hypothesis of exogeneity according to the Sargan-Hansen test (Sargan, 1958; and Hansen, 1982). These instruments are the number of other products from the same firm and the number of products from other firms with the same active ingredient and the sum of the variable time-in-market and the sum of a dummy reference price variable for the therapeutic class. I use a random-effects generalized two-stage least squares method to estimate equation (4). This method is applied by Yu and Gupta (2008) and also in other papers that study generic competition in the pharmaceutical industry, such as Reiffen and Ward (2005) or Regan (2008). Since some important explanatory variables in the dataset, such as the level of insurance, are constant over time, I

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cannot use the alternative fixed effects approach. Hence, the error term, υ jt , is formed of an individual effect, ε j , and an error term, η jt , which is assumed to follow a normal distribution with a mean of zero and variance σ η2 . In addition, following Berry (1994), I use the methodology that has been most used in the empirical industrial organization literature: the (two-stage) generalized method of moments. The results of the two estimations are highly analogous. Table 3 shows the definitions and the descriptive statistics of the variables that I use in the regression analysis. 6. Results Table 4 shows the estimation of the demand model. The generalized two-stage least squares random effects and the two-stage generalized method of moments estimations are quite similar. The within-generic market share is clearly significant and its coefficient is around 0.50. This result validates the use of the nested logit model instead of a simple multinomial logit model, since the model is consistent with the random utility maximization only when this parameter is significant and between 0 and 1. Prices per DDD are also significant and have the expected signs. A 1% increase in the generic price reduces the relative market share, that is, the weight of the generic producer market share over the brand-name products market share, between 5.86% and 5.98%. In contrast, a 1% increase of the average price of the brand-name products raises the relative market share by between 3.97% and 4.26%.

24

I also control for the time the generic product has been on the market, the number of indications of the active ingredient, the number of DDDs per tablet, the number of tablets per package, the number of brand-name presentations in the period before generic entry and for therapeutic subgroups and firm-specific fixed effects. Most of these factors are significant and have the expected signs. For instance, in those markets in which there were a larger number of brand-name drugs, the generics market share is lower. Interestingly, generics gain greater market shares in smaller packet markets (fewer units or lower dosage per unit). There is a significant and negative effect of the dummy for consumptions without copayment; that is, the mean relative generic market share is smaller in prescriptions dispensed to pensioners. The difference in comparison to the group of nonpensioners, who have a 40% rate of copayment, is a relative market share that is between 68.32% and 69.41% smaller. I find a similar result, but a lower impact, for non-pensioner consumptions of products to treat chronic conditions, which have a reduced rate of copayment of only 10%. In this group, the mean relative market share is roughly 40% smaller than in the group of non-pensioners with the 40% rate of copayment. The introduction of the reference price system in Spain contributed to the penetration of generic products beyond its effect through prices. Thus, the effect of the implementation of the first system was an increase of the relative market share of 62%-63% in comparison to the period with the price capping regulation. The change in the system, from 2004 on, brought with it a larger effect. The application

25

of the second reference pricing system raised the relative market share by between 91% and 107%. The five dummy variables for the first five groups of generic entrants are significant and have a positive coefficient. That means that the first generic entrants in the market have a greater market share than the sixth or later groups of entrants. The coefficients decrease, although not linearly, and in fact the first two groups of entrants have a mean relative market share that is between 84.50% and 93.00% greater than the sixth and later entrants, whereas the following three groups of entrants are only between 26.46% and 35.03% greater than the sixth and later entrants. The coefficients are quite similar in the generalized method of moments estimation, although only the first three groups have a significant coefficient. 7. Discussion The coefficient of the within-generic market share is significant and has a value of 0.51. However, it is lower than expected: if generic drugs are close substitutes, it should be close to 1. This value is slightly lower than the value of approximately 0.60 found by Yu and Gupta (2008); it seems that there is not only product differentiation between brand-name and generic products, but also among generic medicines. As expected, prices have a negative effect on the demand for drugs and the effect of high generic prices is greater than the effect on brand-name products. This may be explained by price being a more decisive factor in the consumption of generics,

26

whereas for brand-name products some other characteristics, such as experience or reputation, are more relevant. The other market and drug characteristics also have the expected effects. The fact that generics gain greater shares in markets of fewer units or lower dose may be due to physicians or patients trusting brand-name products more for intense treatments. As expected, there is some moral hazard effect. For those patients with greater insurance, that is, with no or a lower rate of copayment, the consumption of generics is lower than for patients with the general rate of copayment. In other words: the greater the level of insurance, the lower the proportion of generic prescriptions. This result seems to confirm the hypothesis that physicians and pharmacists are better agents for the patients than for the NHS or tax-payers and prescribe and sell more expensive brand-name products to patients with higher levels of insurance coverage, or are influenced by the moral hazard of patients. Therefore, the low level of copayment in Spain has affected the penetration of generics negatively. This result is coherent with those of Rudholm (2005) and Lundin (2000). The positive and clearly significant coefficients of the two reference pricing system dummies indicate that, beyond the effects of these regulations through the variation of relative prices and copayments, the substitution rules and the promotion of generic prescriptions by health authorities has been successful in promoting generic penetration into the market. Moreover, with the second reference pricing system the

27

increase of generic market shares increased. This may be the result of intensified promotion of generics. Finally, I find that the order of market entry of generic manufacturers has the expected effect. This confirms that earlier-mover advantage seems to be true for the first generic entrants, which are able to obtain a considerable market share. Similarly to Yu and Gupta (2008), Kalyanaram (2008) or Hollis (2002), I find evidence of larger market shares for the first generic entrants, which is probably explained by the loyalty derived from market experience due to being the first generics. Furthermore, it seems that in Spain part of the competition occurs in the form of discounts to official prices, as in the cases of France (Kanavos and Taylor, 2007) and the Netherlands (Danzon and Ketcham, 2004). This situation generates profits for pharmacies and wholesalers but no savings to patients or the NHS (Puig-Junoy, 2009; and Borrell and Merino-Castelló, 2007). In this case, first generic entrants, with higher prices than later entrants (since generic prices generally depend on the order of entry14), have a first-mover advantage twice: that obtained from actually being the first generics on the market, and a higher price that allows them to offer higher rebates to pharmacies. Precisely one of the limitations of this analysis is the lack of information about possible rebates from generic producers and wholesalers to the pharmacies. As mentioned above, a key factor in competition among generic producers is
The NHS, a monopsony, uses its buying power to negotiate a reduction in the maximum official price of new additional generics. This is similar to the case of New Zealand (Danzon and Ketcham, 2004).
14

28

pharmacist choice of a specific generic producer within the market of an active ingredient. However, I only have information about the official prices that were paid by the NHS to pharmacies. This is overcome partially since rebates are part of the promotional efforts of the firms and the endogeneity that this lack of information generates was addressed through the use of firm-specific fixed effects and instrumental variables. Moreover, the consumption data is aggregated for the entire Spanish market, whereas some regions apply different policies regarding the promotion of generic medicines or active ingredient prescribing. Therefore, it is not possible to control for this geographical heterogeneity. Finally, I use copayment dummy variables instead of the real copayments due to the difficulties in finding appropriate instrumental variables when the econometric specification includes both the part of the total price paid by the NHS and the part paid by the patient for both the average brand-name product and the generic. This is an issue that will be addressed in future research. 8. Concluding remarks This paper examines competition between generics and brand-name drugs and among generics in the most consumed Spanish prescription pharmaceutical products: statins (HMG-CoA reductase inhibitors), SSRI antidepressants and proton pump inhibitors (antiulcers), from 1999 to 2005.

29

The main result is that patients with greater insurance coverage consumed fewer generics than patients with a higher rate of copayment: the greater the level of insurance, the lower the proportion of generic prescriptions. This result seems to confirm the hypothesis that physicians and pharmacists are better agents for the patient than for the third-party payer (tax-payers in Spain) or are influenced by the moral hazard of patients and prescribe and dispense more expensive brand-name products to those patients who have lower rates of copayment. From this result, it seems that the low level of copayment in Spain has affected the penetration of generics negatively. Secondly, the reference pricing system has had a positive impact on the market shares of generic drugs beyond the effects of these regulations through the variation of relative prices and copayments. This result indicates that the substitution rules and the promotion of generic prescriptions by health authorities have been successful in encouraging generic penetration. Finally, the order of market entry of generics has important competitive effects. The results confirm that an earlier-mover advantage seems to exist for the first generic entrants, which are able to obtain higher market shares.

30

Acknowledgments I thank Jaume Puig-Junoy and Joan-Ramon Borrell-Arqué for their guidance and support, and Sergi Jiménez, Javier Coronado, Carme Piñol and the participants of the XXVIII Jornadas de Economía de la Salud 2008 at Salamanca and of the CRES Seminar May 2008 at Barcelona for helpful comments. I am grateful to the Spanish Health Economics Association (AES) for financial support from the unrestricted Research Fellowship on Health Economics financed by Química Farmacéutica BAYER. S.L. and to the Directorate-General of Pharmacy and Health Products of the Ministry of Health and Consumer Affairs for access to the data used in this paper. Financial support is also acknowledged from the Spanish Ministry of Education and Science under grant SEJ2007-66133. The opinions expressed are those of the author and do not necessarily reflect the opinions of the research sponsors.

31

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36

Figures and tables Figure 1. Drug market

37

Figure 2. Evolution of the number of generic manufacturers.
35

30

25

20

15

10

5

0
49 45 41 53 13 17 21 25 29 33 37 57 61 65 69 73 77
78

Months Fluoxetine Omeprazole Simvastatin

Note: Data from non-pensioner consumption in 3 representative markets is used (fluoxetine 20 mg 14 capsules, omeprazole 20 mg 14 capsules, and simvastatin 40 mg 40 tablets).

Figure 3. Evolution of generic and brand-name market share.
Fluoxetine
1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0

1

5

9

70

58

74

46

34

30

22

38

50

18

26

14

Non-pen gen market share Pen gen market share

42

Months Non-pen brand market share Pen brand market share

38

54

62

66

82

81

1

5

9

Omeprazole
1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0
64 43 46 49 52 55 58 10 13 16 19 22 25 28 31 34 37 40 61 67
44

Months Non-pen gen market share Pen gen market share Non-pen brand market share Pen brand market share

Simvastatin
1 0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0
10 12 16 20 26 18 22 24 14 28 32 30 34 38 36 40 42 46 0 2 4 6 8

Months Non-pen gen market share Pen gen market share Non-pen brand market share Pen brand market share

Note: Data from consumption in 3 representative markets is used (fluoxetine 20 mg 14 capsules, omeprazole 20 mg 14 capsules, and Simvastatin 40 mg 40 tablets).

39

70

1

4

7

Figure 4. Evolution of generic and brand-name prices per DDD
Fluoxetine
1,2

1

0,8

0,6

0,4

0,2

0
1 5 9 74 18 70 26 46 14 34 22 30 38 42 50 58 54 62 66 78 82

Months gen average price brand average price

Omeprazole
2 1,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0,2 0
13 16 19 10 22 25 37 28 31 40 1 34 43 4 46 7 49 61 64 52 55 58 67 70

Months gen average price brand average price

40

Simvastatin
0,9 0,8 0,7 0,6 0,5 0,4 0,3 0,2 0,1 0
13 15 11 17 19 1 25 5 39 33 23 29 21 27 37 41 31 45 35 43 47 3 7 9

Months gen average price brand average price

Note: Are used weighted average prices from non-pensioners consumptions of 3 representative markets (fluoxetine 20 mg 14 capsules, omeprazole 20 mg 14 capsules, and Simvastatin 40 mg 40 tablets).

Figure 5. Evolution of average generic market share by order of entry
Fluoxetine
0,18 0,16 0,14 0,12 0,1 0,08 0,06 0,04 0,02 0

74

78

50

30

54

58

38

18

14

22

1st gen market share 4th gen market share

2nd gen market share 5th gen market share

42

26

Months 3rd gen market share 6th+ gen market share

46

41

62

34

66

70

82

1

5

9

Omeprazole
0,16 0,14 0,12 0,1 0,08 0,06 0,04 0,02 0 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 Months 1st gen market share 4th gen market share 2nd gen market share 5th gen market share 3rd gen market share 6th+ gen market share 70 1 4 7

Simvastatin
0,09 0,08 0,07 0,06 0,05 0,04 0,03 0,02 0,01 0

1

3

5

7

9

11

13

15

21

23

17

25

19

27

29

31

33

35

37

39

41

43

45

Months 1st gen market share 4th gen market share 2nd gen market share 5th gen market share 3rd gen market share 6th+ gen market share

Note: Data from non-pensioner consumption of 3 representative markets is used (fluoxetine 20 mg 14 capsules, omeprazole 20 mg 14 capsules, and Simvastatin 40 mg 40 tablets).

42

47

Table 1. Sample of drugs
Therapeutic subgroup (ATC code) Proton Pump Inhibitors/ Antiulcer drugs (A02BC) Active Ingredient (ATC code) Omeprazole (A02BC01) Indications Gastroesophageal reflux disease, peptic ulcer, nonsteroidal antiinflammatory drug–induced peptic ulcer, Helicobacter pylori infection and Zollinger-Ellison syndrome. Dyslipidemia, hypercholesterole mia, hypertriglyceridemi a, Atherosclerotic, Ischemic cardiomyopathy and cardiovascular prevention. Dyslipidemia, hypercholesterole mia, hyperlipoproteine mia and atherosclerotic. Depression, bulimia nervosa and obsessive compulsive disorder. Depression, panic disorder and obsessive compulsive disorder. Depression, panic disorder, social phobia disorder, generalized anxiety disorder, obsessive compulsive disorder and posttraumatic stress disorder. Depression and obsessive compulsive disorder. First generic entry January 2000 Reference price intro December 1999 Market presentation 20 mg 14 Capsules

December 1999

May 2002

20

mg

28

Capsules

Statins (C10AA)

Simvastatin (C10AA01)

January 2002 January 2002 January 2002 November 2000 December 2000 January 1999 January 1999 August 2002 August 2002 October 2002

January 2004 January 2004 January 2004 May 2002 May 2002 December 1999 December 1999 January 2004 January 2004 January 2004

10

mg

28

Tablets

20

mg

28

Tablets

40

mg

28

Tablets

Lovastatin (C10AA02)

20 40

mg mg

28 28

Tablets Tablets

SSRIs Antidepressants (N06AB)

Fluoxetine (N06AB03)

20 20 20 20 20

mg mg mg mg mg

14 28 14 28 14

Capsules Capsules Tablets Tablets Tablets

Citalopram (N06AB04)

Paroxetine (N06AB05)

October 2002

January 2004

20

mg

28

Tablets

Fluvoxamine (N06AB08)

January 2003 January 2003

May 2003 May 2003

50 100

mg mg

30 30

Tablets Tablets

43

Table 2. Market structure at the end of each year.
Year Number of markets analysed Number of brand-name manufacturers Number of generic manufacturers Average brandname price per DDD 1999 2000 2001 2002 2003 2004 2005 3 6 6 13 15 15 15 30 62 74 113 117 119 115 18 44 95 186 228 252 292 0.9770354 0.9637711 0.8467622 0.8786461 0.8335743 0.4494228 0.4256382 0.7391375 0.9653865 0.6955624 0.5932166 0.5467671 0.3489683 0.315965 0.080517 0.2409376 0.3567254 0.3427948 0.4720702 0.6268138 0.6724371 0.9194829 0.7590624 0.6432745 0.6572052 0.5279298 0.3731862 0.3275629 Average generic price per DDD Generic market share Brandname market share

Table 3. Summary statistics for the demand model variables.
Variable Definition Generic drug’s market share divided by the brand-names’ market share Generic drug’s market share divided by the total generic market share Price per DDD of the generic drug Average price per DDD of the brandname drugs Number of months since the entry of the generic drug Dummy equal to one for a pensioner market; 0 otherwise Dummy equal to one for drugs to treat chronic diseases; 0 otherwise Dummy equal to 1 from the moment the reference price is applied to the market and until December 2003; 0 before and after Dummy equal to 1 from the moment the reference price is applied to the market: December 2003; 0 before Obs. Mean Standard Deviation 0.127 Minimum Maximum

Market share ratio

23,584

0.058

0.000

3.249

Within-generic share Generic price per DDD Average brand-name price per DDD Time on the market

23,584 23,584 23,584 23,584

0.068521 0.5725827 0.6836283 25.22278

0.1239926 0.2435079 0.2874202 17.36645

0.000 0.1881696 0.2871252 1

1 1.594401 1.912889 84

No copayment

23,584

0.5019929

0.5000066

0

1

Small copayment

23,584

0.1869912

0.3899127

0

1

Reference pricing I

23,584

0.2962178

0.4565979

0

1

Reference pricing II

23,584

0.5153494

0.4997749

0

1

44

Number of indications DDDs per tablet Units

Presentations

1st generic entrant

2nd generic entrant

3rd generic entrant 4th generic entrant

5th generic entrant

Number of indications for the active ingredient Number of DDDs per tablet or capsule Number of tablets per package Number of different kinds of presentations in the active ingredient market at the moment of patent expiry Dummy equal to one for the first generic entrant; 0 otherwise Dummy equal to one for the second generic entrant; 0 otherwise Dummy equal to one for the third generic entrant; 0 otherwise Dummy equal to one for the fourth generic entrant; 0 otherwise Dummy equal to one for the firth generic entrant; 0 otherwise

23,584 23,584 23,584

3.516494 1.132802 24.01942

1.156198 0.4936907 6.332285

2 0.5 14

6 2.666667 30

23,584

8.19492

8.28148

1

25

23,584

0.1662992

0.3723569

0

1

23,584

0.1283497

0.3344859

0

1

23,584 23,584

0.089637 0.077171

0.2856671 0.2668682

0 0

1 1

23,584

0.0474474

0.2125984

0

1

45

Table 4. Demand model estimation results.
Coefficient Std. Error Log(Within-generic 0.5096438*** 0.03938 0.4858693*** 0.116312 share) Log(Generic price per -5.97638*** 0.406752 -5.851831*** 1.151446 DDD) Log(Average brandname price per 3.974557*** 0.3919272 4.260158*** 1.086799 DDD) No copayment -0.683159*** 0.0283507 -0.6940663*** 0.0825252 Small copayment -0.3951118*** 0.0410342 -0.41057*** 0.1044407 Reference pricing I 0.6243972*** 0.0296523 0.6296013*** 0.1348306 Reference pricing II 0.9088608*** 0.0866061 1.071127*** 0.2287922 Time on the market 0.0091641*** 0.0009548 0.0128474*** 0.0031299 Number of 0.2252446*** 0.0203143 0.1968905*** 0.0527988 indications DDDs per tablet -0.8450448*** 0.0591827 -0.7988118*** 0.1610417 Units -0.0433392*** 0.0033594 -0.0418655*** 0.0085255 Presentations -0.1463136*** 0.0217165 -0.1647776*** 0.0621652 1st generic entrant 0.8449423*** 0.0881888 0.8205683*** 0.2240418 2nd generic entrant 0.9300263*** 0.0800414 0.8936855*** 0.2072066 3rd generic entrant 0.3425538*** 0.0496196 0.2654443** 0.1202887 4th generic entrant 0.2645661*** 0.0515613 0.1998162 0.1393082 5th generic entrant 0.3502969*** 0.058836 0.2468072 0.2066256 Constant -1.571498*** 0.446079 -1.672343* 0.9943072 Observations 23,584 23,584 R2 0.5494 0.4729 Sargan-Hansen 1.129 1.032 statistic (p-value) (0.288) (0.310) Durbin-Wu-Hausman 12488.85 4484.80 tests (0.0000) (0.0000) Note: Therapeutic subgroups and firm-specific fixed effects are included. *, **, *** = significant at the 10%, 5%, and 1% level, respectively. G2SLS Random-Effects with IV Coefficient Std. Error 2SGMM Pooled with IV

46

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

2006

CREAP2006-01 Matas, A. (GEAP); Raymond, J.Ll. (GEAP) "Economic development and changes in car ownership patterns" (Juny 2006)

CREAP2006-02 Trillas, F. (IEB); Montolio, D. (IEB); Duch, N. (IEB) "Productive efficiency and regulatory reform: The case of Vehicle Inspection Services" (Setembre 2006)

CREAP2006-03 Bel, G. (PPRE-IREA); Fageda, X. (PPRE-IREA) "Factors explaining local privatization: A meta-regression analysis" (Octubre 2006)

CREAP2006-04 Fernàndez-Villadangos, L. (PPRE-IREA) "Are two-part tariffs efficient when consumers plan ahead?: An empirical study" (Octubre 2006)

CREAP2006-05 Artís, M. (AQR-IREA); Ramos, R. (AQR-IREA); Suriñach, J. (AQR-IREA) "Job losses, outsourcing and relocation: Empirical evidence using microdata" (Octubre 2006)

CREAP2006-06 Alcañiz, M. (RISC-IREA); Costa, A.; Guillén, M. (RISC-IREA); Luna, C.; Rovira, C. "Calculation of the variance in surveys of the economic climate” (Novembre 2006)

CREAP2006-07 Albalate, D. (PPRE-IREA) "Lowering blood alcohol content levels to save lives: The European Experience” (Desembre 2006)

CREAP2006-08 Garrido, A. (IEB); Arqué, P. (IEB) “The choice of banking firm: Are the interest rate a significant criteria?” (Desembre 2006)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

CREAP2006-09 Segarra, A. (GRIT); Teruel-Carrizosa, M. (GRIT) "Productivity growth and competition in spanish manufacturing firms: What has happened in recent years?” (Desembre 2006)

CREAP2006-10 Andonova, V.; Díaz-Serrano, Luis. (CREB) "Political institutions and the development of telecommunications” (Desembre 2006)

CREAP2006-11 Raymond, J.L.(GEAP); Roig, J.L.. (GEAP) "Capital humano: un análisis comparativo Catalunya-España” (Desembre 2006)

CREAP2006-12 Rodríguez, M.(CREB); Stoyanova, A. (CREB) "Changes in the demand for private medical insurance following a shift in tax incentives” (Desembre 2006)

CREAP2006-13 Royuela, V. (AQR-IREA); Lambiri, D.; Biagi, B. "Economía urbana y calidad de vida. Una revisión del estado del conocimiento en España” (Desembre 2006)

CREAP2006-14 Camarero, M.; Carrion-i-Silvestre, J.LL. (AQR-IREA).;Tamarit, C. "New evidence of the real interest rate parity for OECD countries using panel unit root tests with breaks” (Desembre 2006)

CREAP2006-15 Karanassou, M.; Sala, H. (GEAP).;Snower , D. J. "The macroeconomics of the labor market: Three fundamental views” (Desembre 2006)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

2007

XREAP2007-01 Castany, L (AQR-IREA); López-Bazo, E. (AQR-IREA).;Moreno , R. (AQR-IREA) "Decomposing differences in total factor productivity across firm size” (Març 2007)

XREAP2007-02 Raymond, J. Ll. (GEAP); Roig, J. Ll. (GEAP) “Una propuesta de evaluación de las externalidades de capital humano en la empresa" (Abril 2007)

XREAP2007-03 Durán, J. M. (IEB); Esteller, A. (IEB) “An empirical analysis of wealth taxation: Equity vs. Tax compliance” (Juny 2007)

XREAP2007-04 Matas, A. (GEAP); Raymond, J.Ll. (GEAP) “Cross-section data, disequilibrium situations and estimated coefficients: evidence from car ownership demand” (Juny 2007)

XREAP2007-05 Jofre-Montseny, J. (IEB); Solé-Ollé, A. (IEB) “Tax differentials and agglomeration economies in intraregional firm location” (Juny 2007)

XREAP2007-06 Álvarez-Albelo, C. (CREB); Hernández-Martín, R. “Explaining high economic growth in small tourism countries with a dynamic general equilibrium model” (Juliol 2007)

XREAP2007-07 Duch, N. (IEB); Montolio, D. (IEB); Mediavilla, M. “Evaluating the impact of public subsidies on a firm’s performance: a quasi-experimental approach” (Juliol 2007)

XREAP2007-08 Segarra-Blasco, A. (GRIT) “Innovation sources and productivity: a quantile regression analysis” (Octubre 2007)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

XREAP2007-09 Albalate, D. (PPRE-IREA) “Shifting death to their Alternatives: The case of Toll Motorways” (Octubre 2007)

XREAP2007-10 Segarra-Blasco, A. (GRIT); Garcia-Quevedo, J. (IEB); Teruel-Carrizosa, M. (GRIT) “Barriers to innovation and public policy in catalonia” (Novembre 2007)

XREAP2007-11 Bel, G. (PPRE-IREA); Foote, J. “Comparison of recent toll road concession transactions in the United States and France” (Novembre 2007)

XREAP2007-12 Segarra-Blasco, A. (GRIT); “Innovation, R&D spillovers and productivity: the role of knowledge-intensive services” (Novembre 2007)

XREAP2007-13 Bermúdez Morata, Ll. (RFA-IREA); Guillén Estany, M. (RFA-IREA), Solé Auró, A. (RFA-IREA) “Impacto de la inmigración sobre la esperanza de vida en salud y en discapacidad de la población española” (Novembre 2007) XREAP2007-14 Calaeys, P. (AQR-IREA); Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA) “Fiscal sustainability across government tiers” (Desembre 2007) XREAP2007-15 Sánchez Hugalbe, A. (IEB) “Influencia de la inmigración en la elección escolar” (Desembre 2007)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

2008

XREAP2008-01 Durán Weitkamp, C. (GRIT); Martín Bofarull, M. (GRIT) ; Pablo Martí, F. “Economic effects of road accessibility in the Pyrenees: User perspective” (Gener 2008) XREAP2008-02 Díaz-Serrano, L.; Stoyanova, A. P. (CREB) “The Causal Relationship between Individual’s Choice Behavior and Self-Reported Satisfaction: the Case of Residential Mobility in the EU” (Març 2008) XREAP2008-03 Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP) “Car ownership and access to jobs in Spain” (Abril 2008) XREAP2008-04 Bel, G. (PPRE-IREA) ; Fageda, X. (PPRE-IREA) “Privatization and competition in the delivery of local services: An empirical examination of the dual market hypothesis” (Abril 2008) XREAP2008-05 Matas, A. (GEAP); Raymond, J. L. (GEAP); Roig, J. L. (GEAP) “Job accessibility and employment probability” (Maig 2008) XREAP2008-06 Basher, S. A.; Carrión, J. Ll. (AQR-IREA) Deconstructing Shocks and Persistence in OECD Real Exchange Rates (Juny 2008) XREAP2008-07 Sanromá, E. (IEB); Ramos, R. (AQR-IREA); Simón, H. Portabilidad del capital humano y asimilación de los inmigrantes. Evidencia para España (Juliol 2008) XREAP2008-08 Basher, S. A.; Carrión, J. Ll. (AQR-IREA) Price level convergence, purchasing power parity and multiple structural breaks: An application to US cities (Juliol 2008) XREAP2008-09 Bermúdez, Ll. (RFA-IREA) A priori ratemaking using bivariate poisson regression models (Juliol 2008)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

XREAP2008-10 Solé-Ollé, A. (IEB), Hortas Rico, M. (IEB) Does urban sprawl increase the costs of providing local public services? Evidence from Spanish municipalities (Novembre 2008) XREAP2008-11 Teruel-Carrizosa, M. (GRIT), Segarra-Blasco, A. (GRIT) Immigration and Firm Growth: Evidence from Spanish cities (Novembre 2008) XREAP2008-12 Duch-Brown, N. (IEB), García-Quevedo, J. (IEB), Montolio, D. (IEB) Assessing the assignation of public subsidies: Do the experts choose the most efficient R&D projects? (Novembre 2008) XREAP2008-13 Bilotkach, V., Fageda, X. (PPRE-IREA), Flores-Fillol, R. Scheduled service versus personal transportation: the role of distance (Desembre 2008) XREAP2008-14 Albalate, D. (PPRE-IREA), Gel, G. (PPRE-IREA) Tourism and urban transport: Holding demand pressure under supply constraints (Desembre 2008)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

2009

XREAP2009-01 Calonge, S. (CREB); Tejada, O. “A theoretical and practical study on linear reforms of dual taxes” (Febrer 2009) XREAP2009-02 Albalate, D. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA) “Exploring Determinants of Urban Motorcycle Accident Severity: The Case of Barcelona” (Març 2009) XREAP2009-03 Borrell, J. R. (PPRE-IREA); Fernández-Villadangos, L. (PPRE-IREA) “Assessing excess profits from different entry regulations” (Abril 2009) XREAP2009-04 Sanromá, E. (IEB); Ramos, R. (AQR-IREA), Simon, H. “Los salarios de los inmigrantes en el mercado de trabajo español. ¿Importa el origen del capital humano?” (Abril 2009) XREAP2009-05 Jiménez, J. L.; Perdiguero, J. (PPRE-IREA) “(No)competition in the Spanish retailing gasoline market: a variance filter approach” (Maig 2009) XREAP2009-06 Álvarez-Albelo,C. D. (CREB), Manresa, A. (CREB), Pigem-Vigo, M. (CREB) “International trade as the sole engine of growth for an economy” (Juny 2009) XREAP2009-07 Callejón, M. (PPRE-IREA), Ortún V, M. “The Black Box of Business Dynamics” (Setembre 2009) XREAP2009-08 Lucena, A. (CREB) “The antecedents and innovation consequences of organizational search: empirical evidence for Spain” (Octubre 2009) XREAP2009-09 Domènech Campmajó, L. (PPRE-IREA) “Competition between TV Platforms” (Octubre 2009)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

XREAP2009-10 Solé-Auró, A. (RFA-IREA),Guillén, M. (RFA-IREA), Crimmins, E. M. “Health care utilization among immigrants and native-born populations in 11 European countries. Results from the Survey of Health, Ageing and Retirement in Europe” (Octubre 2009) XREAP2009-11 Segarra, A. (GRIT), Teruel, M. (GRIT) “Small firms, growth and financial constraints” (Octubre 2009) XREAP2009-12 Matas, A. (GEAP), Raymond, J.Ll. (GEAP), Ruiz, A. (GEAP) “Traffic forecasts under uncertainty and capacity constraints” (Novembre 2009) XREAP2009-13 Sole-Ollé, A. (IEB) “Inter-regional redistribution through infrastructure investment: tactical or programmatic?” (Novembre 2009) XREAP2009-14 Del Barrio-Castro, T., García-Quevedo, J. (IEB) “The determinants of university patenting: Do incentives matter?” (Novembre 2009) XREAP2009-15 Ramos, R. (AQR-IREA), Suriñach, J. (AQR-IREA), Artís, M. (AQR-IREA) “Human capital spillovers, productivity and regional convergence in Spain” (Novembre 2009) XREAP2009-16 Álvarez-Albelo, C. D. (CREB), Hernández-Martín, R. “The commons and anti-commons problems in the tourism economy” (Desembre 2009)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

2010

XREAP2010-01 García-López, M. A. (GEAP) “The Accessibility City. When Transport Infrastructure Matters in Urban Spatial Structure” (Febrer 2010) XREAP2010-02 García-Quevedo, J. (IEB), Mas-Verdú, F. (IEB), Polo-Otero, J. (IEB) “Which firms want PhDs? The effect of the university-industry relationship on the PhD labour market” (Març 2010) XREAP2010-03 Pitt, D., Guillén, M. (RFA-IREA) “An introduction to parametric and non-parametric models for bivariate positive insurance claim severity distributions” (Març 2010) XREAP2010-04 Bermúdez, Ll. (RFA-IREA), Karlis, D. “Modelling dependence in a ratemaking procedure with multivariate Poisson regression models” (Abril 2010) XREAP2010-05 Di Paolo, A. (IEB) “Parental education and family characteristics: educational opportunities across cohorts in Italy and Spain” (Maig 2010) XREAP2010-06 Simón, H. (IEB), Ramos, R. (AQR-IREA), Sanromá, E. (IEB) “Movilidad ocupacional de los inmigrantes en una economía de bajas cualificaciones. El caso de España” (Juny 2010) XREAP2010-07 Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB) “Language knowledge and earnings in Catalonia” (Juliol 2010) XREAP2010-08 Bolancé, C. (RFA-IREA), Alemany, R. (RFA-IREA), Guillén, M. (RFA-IREA) “Prediction of the economic cost of individual long-term care in the Spanish population” (Setembre 2010) XREAP2010-09 Di Paolo, A. (GEAP & IEB) “Knowledge of catalan, public/private sector choice and earnings: Evidence from a double sample selection model” (Setembre 2010)

SÈRIE DE DOCUMENTS DE TREBALL DE LA XREAP

XREAP2010-10 Coad, A., Segarra, A. (GRIT), Teruel, M. (GRIT) “Like milk or wine: Does firm performance improve with age?” (Setembre 2010) XREAP2010-11 Di Paolo, A. (GEAP & IEB), Raymond, J. Ll. (GEAP & IEB), Calero, J. (IEB) “Exploring educational mobility in Europe” (Octubre 2010) XREAP2010-12 Borrell, A. (GiM-IREA), Fernández-Villadangos, L. (GiM-IREA) “Clustering or scattering: the underlying reason for regulating distance among retail outlets” (Desembre 2010) XREAP2010-13 Di Paolo, A. (GEAP & IEB) “School composition effects in Spain” (Desembre 2010) XREAP2010-14 Fageda, X. (GiM-IREA), Flores-Fillol, R. “Technology, Business Models and Network Structure in the Airline Industry” (Desembre 2010) XREAP2010-15 Albalate, D. (GiM-IREA), Bel, G. (GiM-IREA), Fageda, X. (GiM-IREA) “Is it Redistribution or Centralization? On the Determinants of Government Investment in Infrastructure” (Desembre 2010) XREAP2010-16 Oppedisano, V., Turati, G. “What are the causes of educational inequalities and of their evolution over time in Europe? Evidence from PISA” (Desembre 2010) XREAP2010-17 Canova, L., Vaglio, A. “Why do educated mothers matter? A model of parental help” (Desembre 2010)

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2011

XREAP2011-01 Fageda, X. (GiM-IREA), Perdiguero, J. (GiM-IREA) “An empirical analysis of a merger between a network and low-cost airlines” (Maig 2011) XREAP2011-02 Moreno-Torres, I. (ACCO, CRES & GiM-IREA) “What if there was a stronger pharmaceutical price competition in Spain? When regulation has a similar effect to collusion” (Maig 2011) XREAP2011-03 Miguélez, E. (AQR-IREA); Gómez-Miguélez, I. “Singling out individual inventors from patent data” (Maig 2011) XREAP2011-04 Moreno-Torres, I. (ACCO, CRES & GiM-IREA) “Generic drugs in Spain: price competition vs. moral hazard” (Maig 2011)

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