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

INDEX

1 Introduction ......................................................................................................................... 3

2 Pharmaceutical Market in Ecuador: a quick overview ....................................................... 3

3 Market definition ................................................................................................................. 6

3.1 ATC code ................................................................................................................................................... 6

3.2 Case law ...................................................................................................................................................... 7

3.3 Relevant product and geographical markets ........................................................................................ 8

4 Multimarket contact ............................................................................................................ 9

5 Empirical analysis .............................................................................................................. 11

5.1 Data .......................................................................................................................................................... 11

5.2 Variables ................................................................................................................................................... 12

5.3 Reduced form analysis ........................................................................................................................... 15

5.4 Results ...................................................................................................................................................... 18

6 Conclusions ....................................................................................................................... 20

7 References ......................................................................................................................... 22

8 Annexes ............................................................................................................................. 24

8.1 Annex 1: Stata Commands .................................................................................................................... 24

8.2 Annex 2: Full IV and panel data with FE Regressions ................................................................... 25

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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