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Multimarket Contact and Collusion in the Ecuadorian Pharmaceutical Sector (Paper)

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Barcelona GSE Master Project by Jerónimo Callejas and Igne Grazyte

Master Program: Competition and Market Regulation

About Barcelona GSE master programs: http://j.mp/MastersBarcelonaGSE

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Multimarket Contact and Collusion in the Ecuadorian Pharmaceutical Sector (Paper)

  1. 1. Multimarket Contact and Collusion in the Ecuadorian Pharmaceutical Sector ______________________________________________________________________________ Jerónimo Callejas & Igne Grazyte Master Project – Master in Competition and Market Regulation 2013/14 Abstract The paper analyses the effects of multimarket contact on prices in the Ecuadorian pharmaceutical sector and its capacity to serve as a tool to facilitate collusion. We estimate the effect that the multimarket contact has on firms’ price setting behaviour by applying multimarket contact models and simple econometric techniques. Our findings show that multimarket contact has a positive effect on multivitamin prices in Ecuador and could indeed be helping to sustain collusion between firms.
  2. 2. 2 INDEX 1 Introduction.........................................................................................................................3 2 Pharmaceutical Market in Ecuador: a quick overview.......................................................3 3 Market definition.................................................................................................................6 3.1ATC code...................................................................................................................................................6 3.2Case law......................................................................................................................................................7 3.3Relevant product and geographical markets ........................................................................................8 4 Multimarket contact............................................................................................................9 5 Empirical analysis ..............................................................................................................11 5.1Data ..........................................................................................................................................................11 5.2Variables...................................................................................................................................................12 5.3Reduced form analysis...........................................................................................................................15 5.4Results ......................................................................................................................................................18 6 Conclusions ....................................................................................................................... 20 7 References ......................................................................................................................... 22 8 Annexes ............................................................................................................................. 24 8.1Annex 1: Stata Commands....................................................................................................................24 8.2Annex 2: Full IV and panel data with FE Regressions ...................................................................25
  3. 3. 3 1 Introduction The first national competition law in Ecuador was ratified on 13 October 2011 and came into force on 7 May 2012. The fact that Ecuador is very new to antitrust regulations gives rise to legitimate suspicions that high degree of anticompetitive behaviour might be present in a large number of markets: firms will try to maximise their profits in any way possible and when collusion is left unpunished it becomes very likely that firms will divert to it. Of course, that does not necessarily mean that collusion will be present in any market, various collusion facilitating or aggravating factors might increase or decrease the likelihood of it actually occurring. Economic theory knows many factors that could facilitate collusion. They can either be structural, such as high concentration, significant barriers to entry, cross-ownership, regularity and frequency of orders, product homogeneity, symmetry and multimarket contact, or firms might find it easier to collude due to other market conditions, such as in our case – only recent introduction of antitrust laws an other market conditions specific to the pharmaceutical industry, such as Governmental rules favouring national firms. Given these specific circumstances, we suspect that there is a high likelihood of collusion in the Ecuadorian pharmaceutical sector. Since firms active in the pharmaceutical sector meet in many different markets, we well try to estimate to what extent these contacts affect firms’ pricing decisions and possibly lead to collusive outcomes. The effect that multimarket contacts might have on prices and firms’ incentives to collude have previously been analysed on several occasions, with results showing that multimarket contacts could indeed work as a collusion facilitating factor in asymmetric markets. This is achieved by introducing more symmetry between firms and allowing firms to achieve higher profits by pooling and relaxing incentive constraints of all the markets. We will try to evaluate our predictions by using the approach taken by Bernheim and Whinston (1990) and econometric techniques used by Ciliberto and Williams (2013) and Evans and Kessides (1994). 2 Pharmaceutical Market in Ecuador: a quick overview The market for pharmaceutical products in Ecuador can be divided into two large sectors: public and private. The competition in the private sector follows usual competition rules. The pharmaceuticals are bought and sold and the prices are negotiated on a contractual basis. The
  4. 4. 4 purchasers on this market are private pharmacies, hospitals, insurance companies, and private practicians, from which the final consumers are later able to obtain the pharmaceuticals needed. The competition on the public market is organised via the process of public bidding auction where the firms bid for long time contracts to supply public hospitals. The final purchasers in this market are the public hospitals, which later use the drugs purchased to treat their hospitalised or day-care patients. The bidding process is organised as a first price auction – the firm that with the lowest price gains the right to provide the hospitals with the specific drug for a period of two years. The prices for each individual pharmaceutical product are set by the Ecuadorian Health Regulation Agency (Consejo Nacional de Revisión y Fijación de Precios de Medicamentos de Uso Humano) using cost-plus methodology. Ecuador’s pharmaceutical industry consists of a total of 266 registered establishments. Out of these, 70 are national and the remaining 196 are of international origin. During the last years the sector has exhibited steady growth: the growth rate of the private sector amounted to 11% on average if measured in total volume of sales and 16% if measured in US dollars between 2007 and 2011. Almost ¾ of the total sales were generated by the private sector. As seen in Figure 1, in 2011 the sales in the private sector amounted to 1.071 million US dollars and accounted for 71% of the total sales, whereas the public sector accounted for 21% (or 446 million US dollars) of total sales. Figure 1 Overall market demand by sector (in Million US dollars)
  5. 5. 5 In order to promote companies that offer products with national component and increase competitiveness of national establishments, Ecuadorian government has introduced in the public sector the following rule that gives strict preference to products with national component. The rule modifies the biding process in the public sector in the following way: 1. if only international or only national establishments are participating in the auction, the firm with the lowest bid wins; 2. if both international and national establishments are participating in the bidding process the priority is automatically given to the national firm. If there is only one national firm bidding in the auction, the contract is automatically given to it, no matter the price offered. If there are several international and several national firms, the contract is given to that national firm that offered the lowest price. Thus, the above-mentioned pricing rules in the public sector make the offers of the international manufacturers completely irrelevant in the situations where there are national firms participating in the auction. This could serve as a perfect environment for collusion – the current system provides incentives for the national firms to collude on sharing the markets for different pharmaceutical products and charge prices way above their competitive level. As a result of such policy, 53% of the amount allocated by the Government to the public sector for the purchase of drugs goes to the purchase of products with a national component (Figure 2). Figure 2 Award of winning bids in the public sector Public medicine auction 2011 Number of winning bids Amount Awarded (US Dollars) National Companies 133 234.893.135 International Companies 190 211.933.156 As seen from Figure 3, if both private and public markets are analysed jointly, medications by national producers are sold at significantly lower prices than the imported ones. However from Figure 2 we see that, although national firms have won a lower number of bids in the public sector, as compared with international firms, the amount awarded to them is on average USD 651 thousand higher per bid.
  6. 6. 6 Figure 3 Average prices in the pharmaceutical industry This further confirms our suspicions that national companies might be colluding to share the bids in the public sector, which could explain higher prices when bidding for public contracts. We presume that these contacts between firms could also be transferred to the private sector, thus leading to collusion and, as a result, higher prices in the private sector as well. 3 Market definition 3.1 ATC code Market definition in the pharmaceutical markets is not straightforward and will need to be defined on a case-by-case basis. Specific features of the pharmaceutical markets, such as regulation of prices and final consumer preferences, do not allow to properly determine substitutability between different pharmaceutical products. Therefore conventional delineation of relevant markets using the so-called “hypothetical monopolist”1 test is not always feasible in the pharmaceutical industries. As a result, in pharmaceutical industries markets are usually defined using the so-called Anatomical Therapeutic Chemical (ATC) Classification System recommended by World Health Organization (WHO) and European Pharmaceutical Marketing Research Association’s (“EphMRA”). The ATC system allows to classify drugs according to their the organ or system which they act on and 1 Also known as SSNIP (Small but Significant Non-transitory Increase in Price).
  7. 7. 7 their therapeutic and chemical characteristics. By using WHO’s ATC system drugs can be divided into 5 different levels, level 1 indicating the broadest and level 5 (or level 4 if EphMRA’s classification is used) – the narrowest level: i) ATC level 1 divides the frugs into 14 anatomical main groups; ii) ATC level 2 indicates the therapeutic main group; iii) ATC level 3 of the code indicates the therapeutic/pharmacological subgroup; iv) ATC level 4 of the code indicates the chemical/therapeutic/pharmacological subgroup; and v) ATC level 5 indicates the chemical substance.2 Similarly, EphMRA’s ATC system classifies medicines in 4 different groups where the 4th ATC level includes both chemical/therapeutic/pharmacological subgroups and the chemical substance. Thus, the ATC classification system allows to evaluate therapeutic substitution between different pharmaceutical products and define the relevant markets with regard to their therapeutical substitutability for threating a specific condition or a set of related conditions. 3.2 Case law As mentioned before, relevant product markets will usually be defined according to the ATC3 level, however which ATC level will be selected will depend on each particular case. When defining relevant markets, usually the 3rd ATC level, which allows to classify drugs by their therapeutic indications and their intended use, is taken as a starting point. Then, based on the circumstances of each case, it might be necessary to examine either broader or narrower ATC levels, in order to correctly assess competitive constraints that different types of drugs are exerting on each other. In order to assess the competitive constraints, various other factors can be taken into account. For instance, the European Commission in its decision in AstraZaneca4 case found that the 3rd ATC level 2 “Essential Medicines and Health Products Information Portal: A World Health Organization resource” <http://apps.who.int/medicinedocs/en/d/Js4876e/6.html>, accessed 30 June 2014. 3 It has to be noted, that the European Commission often defines relevant product markets using EphMRA’s ATC classification. Although the classifications maintained by EphMRA and WHO are very similar they are not exactly the same and should not be confused with each other. The WHO classification is based on active ingredients and serves a scientific, rather than commercial, purpose. For the purposes of this paper we are going to be using the EphMRA’s ATC classification system also used by the European Commission and IMS Health. 4 Case COMP/A.37.507/F3 AstraZeneca, Commission Decision dated 15 June 2005. The European Commission has used the 3rd ATC level as a starting point to define relevant markets in other cases as well, but also recognized the possibility to define the relevant product markets using other ATC levels, see e.g Case COMP/M.5295, Teva/Barr, Commission Decision dated 19 December 2008, Case No. COMP/M.5253, Sanofi-Aventis/Zentiva, Commission Decision dated 4 February 2009.
  8. 8. 8 included only one of the three main disease areas within the broad acid-related gastro-intestinal field and therefore was insufficient in order to correctly examine the competitive constraints between different drugs and correctly define the relevant market. Therefore the European Commission found it necessary to further examine such factors as product characteristics, products’ therapeutic uses, demand and price and non-price factors of competition and finally defined the relevant product market at the 4th ATC level.5 Similarly, in Novartis/Hexal6 the European Commission started at the 3rd ATC level but later found that market conditions indicated that the 4th ATC level was more appropriate to define the relevant product markets. Article 13 of Ecuadorian General regulation on pricing of medicines (Reglamento General de Fijación de Precios de Medicamentos) also suggests defining relevant markets at the 4th ATC level. 3.3 Relevant product and geographical markets The Ecuadorian pharmaceutical market is made up of 8,106 drugs in total, which, according to the 4th ATC level, can be further divided into 3.312 relevant markets, consisting of therapeutic classes, dosage forms, molecules or active ingredients and pharmaceutical concentration. For the purposes of this paper we have chosen to analyse the collusive effects of multimarket contact on the private multivitamin market. Multivitamins are commonly used as everyday dietary supplements to sustain normal bodily functions and could also be intended to deal with different nutritional needs of specific patient groups such as prenatal, children, geriatric, men's or women's. However, multivitamins are not aimed at assisting with specific dysfunctions. Compositional variation among brands and product lines allows substantial consumer choices. Based on the above, we define the relevant product market using the 4th ATC level and include all products in the following ATC4 segments: A11A1 Prenatal multivitamins with minerals; A11A2 paediatric multivitamins with minerals; A11A3 geriatric multivitamins with minerals; A11A4 Other prepared multivitamins products; A11B2 paediatric polyvitamins; and A11B4 other daily vitamins with no minerals. We believe that the 4th ATC level fits best the purposes of our analysis and allows to arrive at an easy classification based on multivitamins’ therapeutic purposes and intended use. 5 Case COMP/A. 37.507/F3 AstraZeneca (n 4), paras 372-408. 6 Case COMP/M.3751 Novartis/Hexal, Commission Decision dated 27 May 2005.
  9. 9. 9 This is also in line with European Commission’s practice and recommendations issued by the Ecuadorian Government. Multivitamins are also generally available as over-the-counter (OTC) medicines, i.e. no medical prescription is needed in order to purchase them. This allows us to simplify our analysis and avoid the necessity to define two different product markets depending on whether they are sold as OTC or prescription medicines.7 As the European Commission stated in its Teva/Barr decision, even if active ingredients are the same, medical indications, side effects, legal framework, distribution and marketing tend to differ between these drug categories. Usually, OTC pharmaceuticals are chosen by consumers themselves and purchases are not reimbursed8 . Ecuador’s pharmaceutical sector is characterised by high barriers to entry, requiring potential entrants to fulfil numerous legal requirements with regard to manufacturing, production and distribution of pharmaceutical products, such as compulsory registration with the Sanitary and Pricing Registry (Registro Sanitario y la Fijación del Precio) before they can start operating on the market. Therefore for the purposes of our analysis we will presume that the Ecuadorian market forms a distinct geographic market. 4 Multimarket contact For quite some time economists have been arguing that multimarket contacts might be capable of softening competition and facilitating collusion between firms that are competing with each other in more than one market. The idea of multimarket contacts as a collusion facilitating factor was first analysed by Edwards (1955) who introduced the idea that contacts between firms in multiple markets could influence them to avoid industry wide price competition9 . The main underlying theory on how multimarket contacts can facilitate collusion is that such contacts between firms can restore 7 The European Commission has in the past considered that OTC and prescription drugs normally belong to a different product markets. See, e.g. Case COMP/M.1846 Glaxo Wellcome/SmithKline Beecham, Commission decision dated 8 May 2000; Case COMP/M.1878 Pfizer/Warner-Lambert, Commission decision dated 22 May 2000; Case COMP/M.3751 Novartis/Hexal (n 6); Case COMP/M.5295 Teva/Barr (n 4). 8 Case COMP/M.5295 Teva/Barr (n 4), para 12. 9 However, the reasoning behind Edwards’ original idea has been somehow incorrect. Edwards argues that when multimarket contacts between firms are present collusion becomes more sustainable as in case of deviation firms would now be punished in all the markets at the same time. However, it does not take into account that the firm can also deviate in all of them at the same time and thus also gain more from deviation. As Bernheim and Whinston (1990) note, this could simply mean that “increasing the number of markets over which firms have contacts may simply proportionately raise the costs and benefits of an optimal deviation”.
  10. 10. 10 symmetry in otherwise significantly asymmetric markets. Indeed, as shown by Bernheim and Whinston (1990), multimarket contacts indeed do not affect firms’ incentives to collude in perfectly symmetric markets. Only when asymmetries are present such contacts multimarket contacts might lead to collusive outcomes. Bernheim and Whinston (1990) show that multimarket contacts can significantly affect strategic environment and pool incentive constraints of all the markets, thus relaxing binding incentive constraints and leading to higher collusive profits. These changes in incentive compatibility constraints (ICC) can be easily demonstrated with a simple model10 . Suppose that there are two firms (! = 1,2) both operating on two separate markets (! = !, !). The firms are asymmetric in size on the two markets separately: firm 1 has market share ! in market !  and 1 − ! in market B respectively and firm 2 has a market share of 1 − ! in market ! and ! in market !, where ! > 1/2. If the markets are considered in isolation, the ICC for the firm ! = 1,2 in market ! = !, !  is !! ! (!! − !)!(!!) 1 − ! − !! − ! ! !! ≥ 0 From this we can see that, if each firm decides whether to collude on each market or not separately, the collusion in each market will arise if  ! ≥ !, where ! > 1/2. ICC in the market !  for the firm 1 is !   ≥  1 − !    and !   ≥ !   for firm 2. In market B, the ICCs are !   ≥ ! and !   ≥  1 − !  for firms 1 and 2 respectively.  !   ≥ !   is the binding ICC, as firm 1 is the small firm in market !. However, if we take into account the fact that firms, when deciding whether to collude or not, will take into account the fact that they are operating on both markets simultaneously, the ICC for the firm ! = 1,2  now becomes !! ! !! − !  !(!!) 1 − !   + !! ! (!! − !)!(!!) 1 − ! − 2 !! − ! ! !! ≥ 0 10 Motta, “Competition Policy: Theory and Practice” (2004), New York: Cambridge University Press, p 165.
  11. 11. 11 Both incentive constraints can be simplified to ! + 1 − !   ≥ 2(1 − !), meaning that the collusion will arise when ! ≥ 1/2. Comparing this with the previous result, we can clearly see that multimarket contact acts as a collusion facilitating factor, as the critical discount factor now is lower. Further empirical analysis also supports the idea that markets characterised by multimarket contacts more often display higher prices and are arguably more prone to collusion11 . For instance, Evans and Kessides (1994) show that airline fares are higher on those routes where carriers with multimarket contacts operate; Parker and Röller (1997) also find higher prices on US mobile telephone markets where multimarket contacts exists; finally, a very recent study by Ciliberto and Williams (2013) of the US airline industry confirm the previous findings by Evans and Kessides (1994). 5 Empirical analysis In this section, we will try to empirically test our hypothesis that multimarket contact facilitates collusion in the Ecuadorian pharmaceutical industry. To do this we will be using a reduced form analysis, in which the price is regressed on the average multimarket contact index (MMC) and some other control variables. We expect to get a positive coefficient for MMC after controlling for possible endogeneity in the model by using control variables. We will mostly base our analysis on a model introduced by Bernheim and Whinston (1990) and econometric techniques used by Ciliberto and Williams (2013) and Evans and Kessides (1994). 5.1 Data To carry out the empirical analysis proposed, we will focus on the private multivitamin market in Ecuador. As mentioned in section 3, the market is defined using the 4th ATC level and includes all products in the following ATC4 segments: A11A1 Prenatal multivitamins with minerals; A11A2 paediatric multivitamins with minerals; A11A3 geriatric multivitamins with minerals; A11A4 Other prepared multivitamins products; A11B2 paediatric polyvitamins; and A11B4 other daily vitamins with no minerals. The data used for the purposes of our analysis comes from several sources: part of 11 Although the studies find correlation between higher prices and multimarket contacts, it is still unclear whether these high prices were due to collusion or other factors.
  12. 12. 12 it is generated by the Ecuadorian Regulatory Agencies and another part of it comes from IMS Health Database. The main source of information is IMS Health database of medicines traded in Ecuador ranging from December 2007 to November 2012 (60 months). We use this database to get monthly information on the total amount of sales in units and in US dollars at retail level. The database also provides the name of each product, firm producing it, ATC4 group, which it belongs to, number of units per presentation12 , dosage form13 , active principle agent of the product, whether the product is sold as an over-the-counter or a prescription medicine, and whether the product is a generic or a branded product. As mentioned in Section 2, the Ecuadorian Health Regulation Agency is in charge of setting individual price caps per each specific product. Designation of a price cap is a legal requirement prior to the commercialization of any medicine in Ecuador. The Ecuadorian Health Regulation Agency’s database provides information on the price cap per product, the launch date and the daily doses. Finally, from the Ecuadorian Institute of Intellectual property (Instituto Ecuatoriano de Propiedad Intelectual) we obtain information on whether a certain medicine has a registered trademark or not. With this information we are able to construct a database on all six multivitamin markets. Our data includes 143 different medicines produced by 53 corporations within a time span between December 2007 and November 2012, giving us a total of 6.330 observations. 5.2 Variables Our variable of interest is the price of the daily dose (P_D) of all the products belonging to the same relevant product market. The price of the daily dose is constructed by dividing the total amount sales measured in US dollars by the total amount sales measured in number of presentations sold. The resulting number is further divided by the number of units in each presentation, and finally, we divide this number by the recommended daily dose per unit. The price per daily dose allows us to 12 In this paper, we define presentation as the minimum amount of product which a consumer has access to in a single purchase, i.e. a bottle or a package of 30 units. 13 Term established by the European Pharmacopoeia Commission that refers to the form of the medicine, e.g. tablet, powder, liquid or injectable formulation.
  13. 13. 13 compare the prices of different medicines that contain different concentrations of active principle agent and are sold in different forms. Another variable of interest is the Average Multimarket Contact Index (MMC). As in Ciliberto and Williams (2013), Evans and Kessides (1994) and Coronado (2010), we use a simple version of the multimarket contact index: a contact in time t occurs when a firm i and its competitor firm j, who meet in the target market m, are also competitors in the contact market k. For the purposes of our analysis, the target market will include any of the six multivitamins markets defined above and the contact market will be any market for any other type of medicines defined at the 4th ATC level that have been sold in the private pharmaceutical sector in Ecuador between December 2007 and November 2012. The multimarket contact index, denoted as !!"!",!" ! , will have the value of 1 when the two firms compete in the contact market, and 0 otherwise. The average multimarket contact index across all markets for a specific firm i in the target market m will be calculated as follows: !!"!" ! = 1 (!! ! − 1) !!"!",!" ! !!!!!! where !! ! is the total number of firms that are present in market m at time t14 . In our analysis, we use variable pricecap to denote the maximum price at which certain medicine can be offered in the market place. The price cap is set by Ecuadorian Health Regulation Agency, for each specific presentation of each product, using the cost-plus methodology. This variable will give us an idea of the behaviour of a firm when setting prices and how close to the maximum possible price are the actual prices. Other variables used in our analysis are launch_t, which refers to the number of months that have passed from the launch date of a specific product to time t. Numpres, which refers to the number of presentations that a firm has in a given target market. numpro represents the number of products belonging to a firm in all of the target markets. EP is a dummy variable that takes the value of 1 if 14 Considering the size of the database (observations accounting for 60 months, 53 firms, 6 target markets and 313 contact markets), the amount of data that had to be processed in order to calculate this index was exponentially big. Therefore, in order to correctly estimate the average multimarket contact for each firm in each target market at each period of time we used open source programming software known as R.
  14. 14. 14 the product is sold as an OTC medicine and 0 if a prescription is needed. TM is another dummy variable that takes the value of 1 if the name of the product has been registered as a trademark and 0 otherwise. This variable serves as a proxy to measure the degree of firm’s expenditure on publicity for a certain product. numtm is a variable accounting for the total number of trademarked products in the target market excluding the observed product. The variable fsales represents the total number sales by firm, measured in number of daily doses in all the target markets, excluding the observed target market. The variable HHI-1 represents the Herfindhal Hirschman Index in the observed target market, excluding in the market share of the observed company. And finally, the variable units, represents the number of units per presentation for each of the analysed products. Figure 4 displays the summary statistics of the aforementioned variables. Figure 4 Summary statistics Market (ATC 4) Statistic P_D MMC pricecap launch_t numpres numpro fsales A11A1 Mean 0,20 7,22 0,22 110,73 1,00 1,19 296.431 N. Obs. 639 639 571 571 639 639 639 S.D. 0,07 2,99 0,11 30,32 0,00 0,39 343.700 A11A2 Mean 0,29 3,03 3,16 91,44 1,40 1,81 666.000 N. Obs. 330 330 255 255 330 330 330 S.D. 0,15 1,27 3,39 26,61 0,49 0,55 609.579 A11A3 Mean 0,22 21,84 0,29 93,05 1,47 1,47 279.922 N. Obs. 148 142 148 148 148 148 148 S.D. 0,06 1,63 0,13 13,30 0,50 0,50 170.762 A11A4 Mean 0,26 4,90 1,77 100,10 2,17 3,10 216.162 N. Obs. 3357 3357 3114 3114 3357 3357 3.357 S.D. 0,12 2,83 2,90 29,57 1,62 1,87 291.064 A11B2 Mean 0,13 4,04 2,63 114,19 1,58 1,60 210.872 N. Obs. 500 500 438 438 500 500 500 S.D. 0,11 3,21 1,95 41,28 0,83 0,83 316.095
  15. 15. 15 A11B4 Mean 0,25 10,02 1,83 93,65 2,05 2,26 48.436 N. Obs. 1356 1356 1300 1300 1356 1356 1.356 S.D. 0,21 3,38 1,50 27,53 1,21 1,15 81.213 Total Mean 0,24 6,44 1,72 100,20 1,92 2,50 212.859 N. Obs. 6330 6324 5826 5826 6330 6330 6.330 S.D. 0,15 4,36 2,49 30,49 1,39 1,65 321.604 5.3 Reduced form analysis As stated by Bernheim and Whinston (1990), a collusive strategy can be sustained between firms that compete in several markets if they realise that a deviation from the collusive path will automatically trigger retaliation by its rivals in the entire set of contact markets existing among them. This will only happen if the firms competing in a target market m will positively value the average multimarket contact when defining their pricing policy, that is, we are expecting to get a positive coefficient for MMC. Therefore the initial approach to test the hypothesis put forward by Bernheim and Whinston (1990) will be to verify the impact that the average multimarket contact index has on the pricing decisions of each firm participating in the target market. To measure the effect that multimarket contact has on the price of a particular product we use the following regression: ln !!"# ! = !! + !!"!" ! !!" + ln !"#$%$&!!"# !! + ln !"#$%"!"!! ! !! + !!!!! + !"#$!!! + !"!!! + !"#$%!!! + !"#$%&!"!! ! !! + !"#$%&'!" ! !! + !!"# ! The variables not defined before are FF – a discrete variable representing the dosage form of the analysed product that helps to control the price of a different dosage form, and Mark – a dummy variable that helps to introduce fixed effects across different markets. As mentioned before, our variable of interest is the price ln !!"# !  of a product i produced by the firm j that belong to the target market m at time t, and we regress it against the average multimarket
  16. 16. 16 contact index (!!"!" ! ) of firm j at time t using market m as the target market, the price cap ln(!"#$%$&!!"#) of product i, the total sales ln(!"#$%"!"!! ! )  of firm j in all the target and contact markets different from market m and other control variables defined in the last section. We have chosen to use a log – linear model, since the log transformation in the variables price, fsales and pricecap allows us to better estimate the differences in smaller values. As can be seen from the proposed model, there are two endogenous variables: prices and the average multimarket contact among firms present in a certain target market15 . The inclusion of these variables in the proposed model could create an endogeneity problem in the regression, since the average multimarket contact index (MMC) may be correlated with the error term, thus giving us biased estimates. The endogeneity problem between the price and the average multimarket contact might be caused by omitted variable bias. To address this issue, we will estimate the regression using two different techniques. First we will estimate the model with generalised OLS using instrumental variables (IV) to correct for possible endogeneity. We will estimate the multimarket contact index by a two stage OLS regression using as instruments the following variables: the number of months between the product’s launch date and t (launch_t); the number of trademarked products in the target market excluding the observed product (numtm); the logarithm of the total amount of sales observed in all the target markets at time t excluding the one under analysis (ls); a dummy variable that indicates whether the analysed product is sold as an OTC product or a prescription drug (EP); and HHI-1 at time t. All of these variables affect the entry decision of the observed company to a certain market, but do not affect directly the pricing decisions of the firm. For example, entry to a market that has a significant number of trademarks may demand a strong expenditure on advertising and marketing strategies; this means that these markets will have stronger entry barriers. The same analysis may hold in markets with a significant amount of sales or markets that are highly concentrated. The second estimation is carried out using panel data with fixed effects (FE). This will remove the endogeneity that may be caused by some unobserved variables that do not vary over time. However, given the complexity of the market, there might exist some unobserved variables affecting firm’s 15 Endogeneity over this variable comes from the entry decision that each firm has to make in each market. Since entry decisions can be a response to the prices in each market, MMC may be correlated to the error term in the proposed regression.
  17. 17. 17 entry decisions and the total amount of sales of a given firm might be driven by variables that actually vary across time, such as seasonal differences in demand or demand shocks. Therefore panel data with fixed effects will not be enough to remove all the endogeneity from the model. Thus, in order to complement our second model, we instrument the multimarket contact index using the same instrumental variables as before and then we estimate the regression using panel data with fixed effects. The two models mentioned above include fixed effects at the target market level16 and fixed effects for the different dosage forms observed in the analysed market. Introduction of these two elements allows to capture certain idiosyncratic characteristics of each market that could be affecting firm’s price setting decisions and to take into account the effects that different dosage forms could have on the price17 . We have chosen to use as instruments a mix of variables that will affect firm’s entry decisions in different markets and mark-up shifters, that take into account different characteristics of competing products in the target market at time t, as proposed by Berry, Levinsohn and Pakes (1995). As mentioned above, in both cases, we estimate the average multimarket contact index using as instruments launch_t, numtm, ls, EP and HHI-1 and also all the exogenous variables used to estimate log price. The fist stage regression of the panel data with fixed effects estimation also takes into account the fixed effects both at the market level and for the different dosage forms. !!"!" ! = !! + ln !"#$%$&!!"# !! + !"!!! + !"#$%!!! + !!!!! + !"#$!!! + !"#$%&! ! !! + !"#$%&'! ! !! + !"!!! + !"#$#!!! ! !! + ln  (!"#$!)!!! ! !!" + HHI − 1!!! ! !!! + !"!"#ℎ_!!" ! !!" + !!" ! The results given by both models are presented in the following section. 16 In the regression we include a dummy variable for each of the six target markets relevant for our analysis. 17 For instance, dosage forms might affect production costs, meaning that some dosage forms might be more expensive than others.
  18. 18. 18 5.4 Results The results given by IV and panel data with FE estimations as well as are the results from the first stage regression of both models are given in Figures 5 and 6. Figure 5 Regression of the log of product prices on the multimarket contact index (dependent variable – ln(p)) Variable IV Panel FE MMC 0,203*** 0,151*** (0,024) (0,018) ln(pricecap) 0,417*** 0.414*** (0,010) (0,009) TM 0,735*** 0,638*** (0,049) (0,039) units -0,014*** -.0011*** (0,001) (0,001) numpro -0,132*** -0,126 (0,012) (0,011) numpres 0,187*** 0,177*** (0,013) (0,011) Cons -2,854*** -2,424*** (0,205) (0,157) Ad. R squared 0,7087 0,3564 No. Obs 4.822 4.822 F 534,12 N.A. Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Figure 6 First stage regression: estimation of the average multimarket contact index (dependent variable – MMC) Variable IV Panel FE ln(pricecap) -0,059 -0,080* (0,403) (0,0411) TM -2,279*** -2,383*** (,095) (0,099) units 0,054*** 0,055*** (0,004) (0,004) numpro 0,092* 0,096* (0,0435) (0,043) numpres -0,207*** -0,212*** (0,045) (0,045) EP 0,229*** 0,230** (0,080) (0,080) numtm -0,243*** -0,373*** (0,025) (0,033) lauch_t -0,002* -0,004** (0,001) (0,001) ls 0,125*** 0,130*** (0,021) (0,022) HHI-1 0,780*** 0,856*** (0,219) (0.241) Cons 8,135*** 8,934*** (0,343) (0,376) Ad. R squared 0,7087 0,7083 No. Obs 4822 4822 F 534,12 539,09 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
  19. 19. 19 We can see that all defined variables have the correct sing and are significant at a 1%, but the IV model gives larger coefficients than those reported by the panel data with fixed effects model, especially in the case of the coefficient for the endogenous variable MMC. These differences could be the result of an overestimation of the coefficients if the IV model is not enough to remove all the endogeneity between the price and the average multimarket contact index. To check whether this is the case, we run Durbin-Wu-Hausman test for endogeneity test, with the null hypothesis being that the log price and the MMC index are exogenous. The results are shown below: H0: variables are exogenous Durbin (score) chi2(1) = 195.219 (p = 0.0000) Wu-Hausman F(1,4802) = 202.612 (p = 0.0000) As we can see, the Null Hypotheses is rejected and we can conclude that the IV model was not able to remove all the endogeneity from the model. The results of the fist stage regression show that the instruments are clearly efficient since all of them are significant and the value of R2 suggests that the proposed instruments explain at least 70% of the variation within the model. The results suggest that the estimation using panel data with fixed effects model together with the instrumented average multimarket contact index (MMC), removes all the possible sources of endogeneity in the model, thus delivering more consistent and unbiased coefficients. The panel data with fixed effects regression gives smaller coefficients and a smaller standard deviation of the coefficients. With regard to the main variable of interest, the multimarket contact index, both models suggest that an increase in the average number of markets in which the corporations are present will have a positive effect on price on a given target market m. This confirms our initial hypothesis presented and is in line with the theoretical models outlined in section 4. The IV regression infers that an increase by one unit in the average multimarket contact index (MMC) in market m, will increase the price of the observed product by 20,3%. The estimation using panel data with fixed effects shows that an increase in the average multimarket contact index in the target market m by one unit will increase the price of the observed product in 15,1% on average.
  20. 20. 20 Both models suggest that an increasing the price cap of the observed product would also imply an increase in the market price of that product. This was expected since a greater price cap will allow the firm to have more flexibility in setting prices and therefore will also allow to set higher prices. Similarly, both models predict that a product which has a registered trademark (TM) will have a higher price than those that have not. Since we consider that registering the name of a specific product as a trademark may be indicative of the firm’s effort to vertically differentiate its product and may involve additional costs, such as investment in advertising, it is reasonable to assume that a trademarked product will be more expensive as compared with one without a trademark. With regard to the number of units contained within a presentation (units), both models predict that an increase in the number of units within a certain presentation of a specific product will have a negative effect on the price of the observed product. This could be expected, since the costs of packaging in the pharmaceutical industry does not vary much with the increase in the number of units in each presentation, e.g. the costs of packaging incurred by the firm do not differ significantly depending on whether the presentation contains 15 or 30 units of a certain product (Coronado 2010). In the same way, both models predict that an increase in the number of products (numpro) in the target market would decrease the price of the observed product. This result is also expected, since an increase of the number of products in the market will increase competition between different firms present on the market and also exert additional competitive pressure on the incumbent firms, thus forcing the prices to go down. Finally both models predict that the number of presentations that a firm has in the target market (numpres) will positively affect the price of the observed product. If we consider that having different presentations of a specific product in the target market will reflect a certain degree of vertical differentiation, it is logical to expect that as the number of presentation increase, thus leading to an increase in vertical differentiation, firms will be able to charge higher prices for each specific product. 6 Conclusions We have tried to estimate the possible effect that multimarket contacts might have on prices and collusion in the Ecuadorian pharmaceutical industry. For the purposes of this paper we have chosen
  21. 21. 21 to limit our analysis and only focus on the market for multivitamins defined at the 4th ATC level. To test our predictions we tried to replicate simple techniques used by Ciliberto and Williams (2013), Evans and Kessides (1994) and Coronado (2010). We have constructed a multimarket contact index and estimated its effect on prices by using IV and then Panel Data with fixed effects estimations and also correcting for endogeneity. As seen in section 5, our model gives robust results and provides a reasonable confirmation of our expectations: the coefficients predicted by the two models (IV and panel data with fixed effects) have the correct sings and are highly significant. Our results show that the IV estimation alone is insufficient to successfully solve all endogeneity issues, however we find that using panel data with fixed effects and also instrumenting endogenous variables (MMC) we can successfully remove the endogeneity problem from the proposed regression and obtain unbiased estimates. Our analysis shows that average multimarket contact index has a significant positive effect on price, thus confirming our predictions that the contacts between firms in different product markets can lead to higher prices for pharmaceutical products. Although we believe that this result could be indicative of possible collusive practices in the sector, the actual existence of collusion could only be confirmed by direct evidence, such as direct contacts between firms with the aim of setting prices or sharing markets. Due to time constraints we were only able to conduct our analysis in one market and using only simple estimations and models of multimarket contact index. Therefore possible future extensions to this paper could include estimating the effect of the multimarket contact index in other markets, possibly taking into account both private and public markets; or to estimate the effect of multimarket contact by using more complex models, such as nested logit model used in Ciliberto and Williams (2013).
  22. 22. 22 7 References Articles/Textbooks: Bernheim, D., and M. Whinston (1990), “Multimarket contact and collusive behavior”, Rand Journal of Economics, 21, 1-26. Berry, Levinsohn and Pakes (1995), “Automobile Prices in Market Equilibrium”, Econometrica, 63, 841-890. Ciliberto, F. and Williams, J.W. (2013), “Does Multimarket Contact Facilitate Tacit Collusion? Inference on Conduct Parameters in the Airline Industry.” Coronado, F. (2010), “Market Structure and Regulation in Pharmaceutical Markets” Edwards, C. (1955): “Conglomerate bigness as a source of power” in The National Bureau of Economic Research Conference Report, Business Concentration and Price Policy, pp. 331-359, Princeton University Press. Evans, W.N and Kessides, I.N. (1994), “Living by the ‘Golden Rule’: Multimarket Contact in the U.S. Airline Industry”, Quarterly Journal of Economics, 109, 341-366. Motta, M., “Competition Policy: Theory and Practice”(2004), New York: Cambridge University Press Parker, P.M. and Röller, L.-H. (1997), “Collusive Conduct in Duopolies: Multimarket Contact and Cross-Ownership in the Mobile Telephony Industry”, Rand Journal of Economics, 28, 304-322. Case-law: Case COMP/M.1846 – Glaxo Wellcome/SmithKline Beecham, Commission decision dated 8 May 2000. Case COMP/M.1878 – Pfizer/Warner-Lambert, Commission decision dated 22 May 2000. Case COMP/A.37.507/F3 AstraZeneca, Commission Decision dated 15 June 2005. Case COMP/M.3751 Novartis/Hexal, Commission Decision dated 27 May 2005. Case COMP/M.5295, Teva/Barr, Commission Decision dated 19 December 2008. Case COMP/M.5253, Sanofi-Aventis/Zentiva, Commission Decision dated 4 February 2009.
  23. 23. 23 Other: CRA Competition Memo “Market Definition in the Pharmaceutical Sector” <www.crai.com/ecp/assets/Market_Definition_Pharma.pdf>, accessed 30 June 2014. Ministerio de Industrias Y Productividad de República del Ecuador “Lineamientos de la Política para el desarrollo de la Industria Farmacéutica Nacional” (2012) Superintendencia de Control del Poder de Mercado de República del Ecuador “Análisis Sectoral #00X: Sector Farmacéutico” (2012) “Essential Medicines and Health Products Information Portal: A World Health Organization resource” <http://apps.who.int/medicinedocs/en/d/Js4876e/6.html>, accessed 30 June 2014.
  24. 24. 24 8 Annexes 8.1 Annex 1: Stata Commands egen numpro=sum(cuentaproducto), by (codcorp codmark cuentames) egen numpres=sum(cuentaproducto), by (codcorp codmark cuentames codff) gen n_prod_d=0 replace n_prod_d=1 if numpro>1 gen n_pres_d=0 replace n_pres_d=1 if numpro>1 egen fsale1=sum(Q_D), by (codcorp cuentames) gen fsales=fsale1- Q_D egen Msale=sum(Q_D), by (codmark cuentames) egen fMsale=sum(Q_D), by (codmark cuentames codcorp) gen MS=(fMsale/Msale)^2 egen HHI =sum(MS), by (codmark cuentames) gen HHI_1=HHI-MS tabulate TM, gen(trademark) egen ntm=sum(trademark2), by (codmark cuentames) gen numtm=ntm-trademark2 sum P_D MMC pricecap launch numpres numpro fsales tset cuentames codpro gen lp = ln(P_D) gen ls=ln(fsale) gen lq = ln(Q_D) gen lpc = ln(PC_D) xi:ivreg 2sls lp lpc (MMC = i.EP numtm lauch_t ls HHI_1) i.codff i.codmark i.TM units numpro numpres, first estat endog xi: xtivreg lp lpc (MMC = i.EP numtm lauch_t ls HHI_1) i.codff i.codmark i.TM units numpro numpres, fe first
  25. 25. 25 8.2 Annex 2: Full IV and panel data with FE Regressions (dependent variable – ln(p)) Variable IV Panel FE MMC 0.203*** 0.151*** (0.0241) (0.0184) lpc 0.417*** 0.414*** (0.0110) (0.00952) _Icodff_2 0.182*** 0.0994** (0.0574) (0.0479) _Icodff_3 0.0580 0.0182 (0.0367) (0.0311) _Icodff_4 0.320** 0.265** (0.148) (0.129) _Icodff_5 0.332*** 0.300*** (0.0669) (0.0578) _Icodff_6 -0.726*** -0.667*** (0.0784) (0.0665) _Icodff_7 -0.564*** -0.472*** (0.107) (0.0895) _Icodff_8 -0.169*** -0.144*** (0.0506) (0.0434) _Icodmark_46 1.358*** 1.094*** (0.134) (0.104) _Icodmark_47 -2.841*** -2.111*** (0.332) (0.254) _Icodmark_48 0.509*** 0.405*** (0.0620) (0.0494) _Icodmark_49 0.118 -0.0302 (0.0881) (0.0703) _Icodmark_50 -0.325*** -0.179*** (0.0795) (0.0634) TM 0.735*** 0.638*** (0.0500) (0.0391) units -0.014*** -0.011*** (0.00178) (0.00144) numpro -0.132*** -0.126*** (0.0122) (0.0105) numpres 0.187*** 0.177*** (0.0134) (0.0115) Constant -2.854*** -2.424*** (0.206) (0.158) No. Obs 4,822 4,822 Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

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