MERGER SIMULATION AS A SCREENING DEVICE: SIMULATING THE EFFECTS OF THE KRAFT/CADBURY TRANSACTION

Transcription

1 MERGER SIMULATION AS A SCREENING DEVICE: SIMULATING THE EFFECTS OF THE KRAFT/CADBURY TRANSACTION Enique Andeu, Kisten Edwads, Aleando Requeo 1,2 Novembe 2010 Abstact In this aticle we pesent a method that can substantially educe the amount of time to conduct a mege simulation, unde altenative scenaios of cost efficiencies and substitution pattens between poducts, while still poviding obust esults. The poposed method poduces a ange of pedicted post-mege pice inceases though calibation in the context of a nested-logit demand stuctue. It educes the need to obtain econometically pecise estimates of the paametes of the demand function and can be successfully employed within the time constaints of Phase I, and theefoe may be used a sceening device in mege contol. We used this methodology in the Kaft/Cadbuy tansaction, which eceived cleaance fom the Euopean Commission DG Comp at Phase I. A. INTRODUCTION Mege simulation models ae often used duing competition investigations to simulate the pice effects of meges involving diffeentiated poducts. They use maet data and assumptions about the behaviou of consumes and fims in ode to pedict pices and output. Thee ae two main elements to a simulation model: a supply side which simulates how fims compete to maximise pofits; and a demand side which simulates how consumes choose what to puchase to maximise thei utility (satisfaction). In addition to assumptions about fim and consume behaviou, the models equie data on pe-mege pices and volumes fo all mao bands, estimates of supplies costs and estimates of the own-pice and coss-pice elasticities of demand fo all poducts in the maet. Pice, volume and cost data is often eadily available, wheeas elasticities usually need to be estimated. In this aticle we descibe a method that can substantially educe the amount of time to conduct a mege simulation unde altenative scenaios of cost efficiencies and substitution pattens between poducts, while still poviding obust esults. This appoach can be successfully employed within the 1 The authos ae economists at LECG Consulting in London and Madid. Enique Andeu (eandeu@lecg.com), Kisten Edwads (edwads@lecg.com), Aleando Requeo (aequeo@lecg.com). 2. We would lie to than Eduad Baniol and David Shahaudin fo exceptional eseach assistance. 1

2 time constaints of Phase I investigations. We used this appoach in the Kaft/Cadbuy tansaction, which could only poceed if it eceived cleaance fom the Euopean Commission DG Comp at Phase I. The poposed methodology educes the need fo pecise point estimates of demand elasticities unde altenative assumptions about demand stuctue, instead using cost and maet shae data to wo out the ange within which the actual demand elasticities ae liely to fall. The method wos as, fo any set of pices and costs, thee is only a limited ange of combinations of own-pice and cosspice elasticities which ae consistent with the undelying assumptions egading pofit maximisation behaviou and demand stuctue. This implies that it possible to calibate a simulation model to deduce the feasible ange of demand paametes combinations consistent with the pices, costs and volumes obseved in the pe-mege maet. The feasible ange of elasticities can be obtained unde altenative assumptions egading the maet segmentation and aggegated demand elasticity. Unde each of these assumptions, one can simulate post-mege pices and volumes fo each feasible combination of demand paametes. The esults povides with a ange of plausible pice inceases unde each altenative maet segmentation, thus allowing us to identify the liely uppe and lowe bound pice effect of the mege. If the uppe bound simulated pice incease is not mateial, then it is not necessay to futhe the analysis by estimating elasticities (and thus simulating pice changes) pecisely fo each of the demand stuctues (maet segmentations) unde consideation. We used this appoach in Kaft/Cadbuy as a complement to econometic demand estimation and mege simulation unde one specific demand stuctue (nest stuctue). The Euopean Commission consideed that the uppe bound pice incease fom the mege simulation was sufficiently low to conclude that the mege would not esult in significant anti-competitive effects in the UK, Fance o Ieland. 3 In Section B of this pape we descibe how a mege simulation model wos and eview cases in which mege simulations have been used. In Section C we povide bacgound to the Kaft/Cadbuy case and we explain how we efined the standad mege simulation methodology to sceen fo anticompetitive effects. In Section D we conclude. B. THE USE OF MERGER SIMULATIONS IN COMPETITION INVESTIGATIONS Unilateal effects concens in meges involving diffeentiated poducts The main concen in most mege investigations is that the mege will lead to unilateal effects, such that the meged fim will be able to unilateally incease its pices as a esult of the educed 3 See Euopean Commission Decision in Case No. COMP/M.5644 Kaft Foods/Cadbuy, available at: 2

3 competitive constaint it faces. The emoval of a competitive constaint in the maet place may also deteioate ivals offeings. The magnitude of the effect depends on the natue of competition in the maet. If competition is lagely diven by capacity o poduction (Counot competition), then the pice effect will often be diectly popotional to the maet shaes of the meging paties (befoe taing into account countevailing factos such as enty). 4 In such cases, concentation measues ae typically a good indication of the potential fo unilateal effects. Howeve, if competition is pimaily on pices o quality then the pice effect of a mege depends mainly on two factos: (i) the closeness of competition between the meging paties elative to emaining fims in the maet, and (ii) the magins eaned by the paties. In geneal, the close the meging paties ae as competitos and the highe thei magins, the geate the potential fo unilateal effects. The closeness of competition between two fims poducts can be quantified: divesion atios and coss-pice elasticities ae both measues of poduct substitutability. Pe-mege, if a fim inceases its pices, it will lose some sales to its competitos. A divesion atio measues the popotion of sales lost to each competito (e.g. if Fim A inceases its pice and loses 100 sales, of which 20 ae ecaptued by Fim B, then the divesion atio fom A to B is 20%). The coss-pice elasticity measues the same movement of volumes, but elative to the volumes of the fim who gains the sales athe than the fim who loses them. 5 Divesion atios and coss-pice elasticities ae thus closely elated measues of the closeness of competition between two fims. The highe thei values, the moe liely it is that thee will be unilateal effects fom a mege, all else equal. The othe ey facto which affects a post-mege fim s incentive to incease pices is the magin eaned on the acquied poducts. The highe the magin of the taget company, the geate the pofit which is intenalised fom the sales which peviously would have been lost following a pice incease. Because the ey dives of unilateal pice inceases in diffeentiated poducts meges ae the closeness of competition and pofit magins, maet shaes ae often poo indicatos of the effect of this type of mege. This was illustated by the Whole Foods/Wild Oats mege in the US whee the paties wee the only pemium natual, oganic supemaet chains in many local geogaphies, but faced competition fom pemium oganic anges in standad supemaet chains. In that case it was agued that defining the maet to include only pemium oganic supemaet chains led to excessively high maet shaes, wheeas defining the maet to include all supemaet chains esulted in vey small shaes of supply fo the paties. 6 4 See fo example, Motta, M, Competition Policy: Theoy and Pactice, Cambidge, 2004, p Following on fom the above example, this means that if Fim B had initial volumes of 500, and a 10% pice incease by Fim A led to it gaining 20 volumes (a volume incease of 4%), then the coss-pice elasticity of B with espect to A is (4%/10%) = Whole Foods and Wild Oats wee national supemaet chains specialising in natual and oganic goods. The Fedeal Tade Commission defined the maet naowly to include only pemium natual and oganic supemaets. On this basis the paties wee each othe s closest ivals, with 100% maet shae in many of the geogaphic maets. 3

4 As such, economists, and competition authoities in the ecent yeas, tend to place less emphasis on maet shaes in diffeentiated poducts meges and instead analyse the liely pice effects. The use of mege simulation to measue the pice effect of meges A mege combines the poductive esouces and decision-maing of two fims. As a esult, the meged fim may have the incentive to implement diffeent competitive stategies than those of the two fims pio to the mege. Theefoe any mege is liely to alte maet outcomes. Changes in maet outcomes induced by a mege ae efeed to as the competitive effects of the mege. Competitive effects esult lagely fom the elimination of competition between the poducts of the meging fims, the ealisation of post-mege cost efficiencies fo the meging paties and changes in the incentives to coodinate between ival fims in the post-mege wold. Mege simulation in antitust is typically used to pedict the pice effects of meges that esult fom the elimination of competition between the poducts of the meging fims. 7 The basic idea behind simulating a mege is to build a stuctual model of the industy that fits the pe-mege wold, and use it to pedict the maet outcomes post-mege. Any stuctual model of an industy has two main elements: (i) a supply side which simulates how fims behave e.g. how fims compete with each othe to maximise pofits given a cost stuctue, and (ii) a demand side which simulates how consumes behave, e.g. how customes choose what to buy in ode to maximise thei own utility given a set of pices. Typically mege simulation models ae used in antitust to diectly estimate the pice (and sometimes volume) effects of a mege. Thee ae many diffeent appoaches. The simplest appoaches have few data equiements but ely heavily on assumptions. An example is the model poposed by Shapio (1996) 8, which has been used by the UK Office of Fai Tading (OFT) in many fist phase mege assessments. 9 It equies only two inputs; a divesion atio and a magin, fom which it estimates how pices change as esult of a mege. The fomula is deived by Shapio by compaing the pofit maximisation function of a fim befoe and afte a mege, taing into account the emoved constaint fom the competito. The fomula is The FTC sought to bloc the mege on this basis. The Distict Cout howeve uled that the evidence instead indicated a elevant maet that included the poduct ange of conventional supemaets as well. On this maet, the paties would have a vey low maet shae suggesting that the mege would not ham competition. The Distict Cout s decision on maet definition was late ovetuned on appeal. See Fedeal Tade Commission, Plaintiff, v. Whole Foods Maet, Inc., and Wild Oats Maets, Inc. (United States Distict Cout fo the Distict of Columbia) at 7 Recently mege simulation techniques have been applied to analyse the lielihood of pice-coodination among fims may aise (coodinated effects) following a mege. See Davis, P. and Huse, C. Estimating the coodinated effects of meges, Competition Commission Woing Pape, C. Shapio, Meges with Diffeentiated Poducts, Antitust, Sping See fo example, CGL/Somefield (see OFT, Anticipated Acquisition by Co-opeative Goup Limited of Somefield Limited, 2008), Love Film/Amazon (see OFT, Anticipated Acquisition of the Online DVD Rental Subsciption Business of Amazon Inc. by LOVEFILM Intenational Limited, 2008). 4

5 simplified geatly though a numbe of assumptions: that the fims only sell one poduct; thei pices and costs ae identical; the divesion atio is symmetic 10 ; and the demand function facing the fims is isoelastic. This is also a patial equilibium model; it does not tae into account the esponse of emaining competitos. 11 Note also that while this model only equies limited data, obtaining obust divesion atios can be ticy (the OFT usually does this though consume suveys). Moe sophisticated appoaches equie moe data but impose less estictive assumptions. A fullfledged mege simulation models the inteaction between all fims, poducts and consumes in a maet. A full-fledged simulation model is liely to be the most compehensive and geneal appoach to estimating the liely pice effect of a mege accounting fo the ey featues of an industy such as the maet stuctue, the stategic inteaction between the fims, cost efficiencies aising fom the mege, and the behaviou of consumes. This appoach involves specifying an oligopolistic model that easonably eflects the natue of competition in the maet and a demand function eflecting the behaviou of consumes and the natue of poduct diffeentiation. The paametes of the simulation model (e.g. manufactuing costs fo all fims) ae then calibated so that they accuately pedict obseved actual levels of pices and output in the pe-mege state. Data used fo the calibation of the paametes of the model (pe-mege pices and volumes fo all poducts in the maet) must be available. Typically, the paametes of the demand function, which detemine the own-pice and coss-pice elasticities of demand fo all poducts in the maet, ae estimated econometically on the basis of the specified demand function. As we will emphasize in the next section, unde cetain demand specifications, the paametes of the demand function can easily be calibated togethe with the paametes of the supply side of the model on the basis of eadily available infomation on pices and volumes in the pe-mege state. That is, any input paametes which ae not nown with a high degee of cetainty can be infeed fom the obseved pice and volume data. This calibated model is then used to simulate the behaviou of the fims following the mege and measue the pice impact of the mege. In pactice, a mege simulation is usually caied out in a numbe of steps: 1. Obtaining data on pices, volumes and poduct chaacteistics fo all poducts, by fim. This data is most eadily available fo etail poducts whee scanne data o household suvey data has been collected. 10 Meaning that the divesion atio fom Fim A to Fim B is the same as the divesion atio fom Fim B to Fim A. 11 Thee ae a numbe of vesions of this model whee one of the above assumptions is eithe changed o elaxed. In the same pape Shapio pesents a slightly diffeent fomula which assumes a linea demand function. Simila models have been deived which allow fo cost asymmeties and divesion atio asymmeties. See, fo example, Bishop, S. and Wale, M. The economics of EC competition law, Sweet and Maxwell, In geneal, the moe assumptions which ae elaxed, the moe unwieldy the fomula. 5

6 2. Obtaining data on manufactuing costs, by poduct o by band. This data may only be available at this level of detail fom one of the fims. 3. Specifying a demand function. In meges involving diffeentiated poducts, discete choice models (logit and nested-logit models) ae commonly used to assimilate the demand stuctue of diffeentiated poducts maets when maet-level data on quantities, pices and othe poduct chaacteistics is available, as they allow fo heteogeneity in consume choice. 12 These models povide a tactable and pasimonious way to model demand decisions based on paametic estictions of the demand stuctue. 13 The ey elements of the demand-side model ae the own-pice and coss-pice elasticities of demand fo all poducts in the maet. 4. Specifying a model supply side of the maet. Typically Betand competition is assumed to chaacteize the behaviou of multi-poduct fims in mege simulation models in the context of diffeentiated poducts. In this model, each fim sets the pices of its poducts to maximize pofits, taing into account the expected non-coopeative esponses of its competitos. An equilibium esults when no fim can incease its pofit by changing its pices. The level of supply will depend on the fims costs, and thus magins, fo any given set of pices. 5. Calibating the model. The paametes of the model ae adusted so that they accuately pedict actual levels of pices and output. When demand paametes ae estimated econometically, calibation entails ecoveing the maginal costs figues fo all poducts in the maet that ae consistent with cuent maet conditions (i.e. obseved pices and volumes), estimated elasticities,and pofit maximising behaviou of the fims. 6. Simulating the mege. Once the model has been calibated, the supply side of the model can be adusted to eflect changes in the owneship of poducts, and possibly cost efficiencies and potential divestitues, following the mege. The model is then used to compute a new pofit-maximizing set of equilibium pices and volumes. Full-fledged mege simulation models lie the one descibed above show the expected changes in pices and volumes fo all poducts in a maet following a mege. These models ae usually vey flexible and can be used to assess the liely effect of diffeent divestment pacages, o the effect of changing assumptions about the stuctue of demand o maet segmentation. Simulations can also be conducted unde altenative scenaios of cost eductions (efficiencies) following the mege. This type of models has infomed decision maing on a numbe of cases both in Euope and globally. We conside some of the ey cases in Euope below. 12 Thee ae othe models of demand fo diffeentiated poducts, such as linea and log-linea demand systems and Almost Ideal Demand System (AIDS) that can be used. 13 Bey, S. T. (1994), Estimating Discete-choice Models of Poduct Diffeentiation, RAND Jounal of Economics, Vol. 25 (2), pp

7 Case pecedent Mege simulation has been commonly used to assess meges since the 1990s. 14 In many of these cases, econometic analysis has played an impotant ole in the Euopean Commission s final decisions. In 1996 in the Kimbely-Cla/Scott case, the Euopean Commission consideed econometic studies submitted by the paties to the mege and a competito that assessed the impact of the mege on toilet pape. 15 The focus of the studies was to assess whethe pices fo banded toilet pape wee constained by the pices fo pivate labels. In 1999 in the Volvo/Scania case, the Euopean Commission commissioned an econometic study by Pofessos Ivaldi and Veboven to diectly assess the impact that mege would have on tuc pices. 16 Ivaldi and Veboven used a nested-logit model using panel data on list pices and hosepowe fo two types of tucs fo each of the seven mao tuc manufactues in diffeent Euopean counties in 1997 and In 2003 in the Philip Mois /Papastatos case the Commission too into account esults of a mege simulation submitted by the paties. 17 The esults of the study wee that the post-mege pice incease would be low. In TomTom/Teleatlas (2004) the Commission caied out an econometic estimation of the downsteam pice elasticities to measue the sales that the meged entity would captue if it inceased map database pices fo TomTom s competitos downsteam. 18 The Commission found that the meged entity would have no incentive to incease pices in a way that would lead to anticompetitive foeclosue downsteam. Also in 2004, the Commission constucted a model in the Oacle/Peoplesoft case to simulate the impact of the tansaction on pices and the economic benefit to customes fom paticipating in the maet. 19 It is notable that these cases wee mostly Phase II cases. Economists ae getting faste at designing and conducting mege simulations and competition authoities moe comfotable at it; but they still have a lot of discussion especially aound estimation of elasticities. The eason why the mege simulation in Kaft/Cadbuy could be caied out in a shote time fame was because we modified the basic methodology to bypass the elasticity estimation stage. We explain how in the next section. 14 Fo othe examples see Budzinsi, Olive & Ruhme, Isabel (2009). Mege Simulation in Competition Policy: A Suvey, Jounal of Competition Law and Economics, Vol. 6, Issue 2, pp See Euopean Commission Decision in Case Case No IV/M.623 Kimbely-Cla/Scott, paa , available at: 16 See Euopean Commission Decision in Case No COMP/M.1672 Volvo/Scania, paa , available at: 17 See Euopean Commission Decision in Case No COMP/M.3191 Philip Mois/Papastatos, paa. 32, available at: 18 See Euopean Commission Decision in Case No COMP/M.4854 TomTom/Tele Atlas, paa , available at: 19 See Euopean Commission Decision in Case No COMP/M.3216 Oacle/Peoplesoft, paa , available at: 7

8 C. THE KRAFT/CADBURY CASE Bacgound to case Kaft Foods, a global food and dins company, announced a hostile bid fo Cadbuy plc in Novembe The companies ovelapped pimaily in the supply of chocolate confectionay. 20 Chocolate confectionay poducts ae diffeentiated poducts. They come in diffeent sizes, diffeent fomats, have diffeent levels of cocoa and suga content; they may contain vaious additional ingedients such as nuts, caamel, honey o fuit; they may seve diffeent needs (e.g. pesonal consumption, shaing, gifting) and consumes have paticula pefeences fo diffeent types of chocolate. Chocolate confectionay can boadly be gouped into thee categoies: countlines, tablets and palines. Countlines ae bas which ae typically unde 60g, usually consumed as a pesonal snac. Tablets ae lage bas, usually anging fom 100g to 400g in size, which ae ectangula and can usually be boen into bite size segments. Palines ae sepaate bite size pieces of chocolate that ae usually sold in boxes o bags as gifts. Kaft and Cadbuy poduced chocolates in all thee segments, but thei main ovelap was in the tablets segment. This was paticulaly the case in Fance, the UK and Ieland. 21 Kaft s main bands wee Tobleone, Mila and Côte d O, while Cadbuy was pesent with Cadbuy Daiy Mil, Geen & Blacs and Bounville in the UK and Ieland, and Poulain and 1848 in Fance. In the UK and Ieland, the main bands of chocolate tablets ae Cadbuy s Daiy Mil, Mas Galaxy and Tobleone (Kaft). Nestlé and Lindt also have a numbe of bands, and pivate labels ae also pesent. The bands with most simila physical chaacteistics ae Daiy Mil and Galaxy, which ae made up of Bitish heitage mil chocolate bite-sized squaes. 22 Tobleone is a quite unique and singula poduct. It is pism shaped, contains honey and nuts, and has a distinct continental flavou. It is also highly seasonal, with most sales in UK and Ieland made aound Chistmas, St. Valentine s Day and Fathe s Day. In Fance, the main tablet bands ae Mila, Côte d O, Poulain, 1848 and Nestlé. Pivate labels have a high shae of the maet. The Kaft bands ae pimaily mil chocolate, wheeas Cadbuy has a geate pesence in da chocolate confectionay. The tansaction was subect to the UK Taeove Code. This meant that Kaft had 60 days fom the date on which the fomal offe was made in which to obtain acceptance by a maoity of the shaeholdes. 23 If it failed to achieve this taget, the offe would have lapsed. This meant that a Phase I cleaance was ey as, if the investigation had caied on into Phase II, Kaft s offe would 20 Thee wee also mino ovelaps elating to suga confectionay. 21 In Romania and Potugal the paties also ovelapped to a significant extent, although the Cadbuy owned bands wee local bands which could be easily divested. 22 Bitish heitage chocolate lie Cadbuy Daiy Mil has a lowe content of cocoa butte and a highe content of mil than typical continental Euopean chocolate lie Linds and Mila tablets, and all othe continental Euopean chocolates, including those offeed by Cadbuy in Poland and Fance. Typically, Bitish heitage chocolates such as Mas, Galaxy, and Cadbuy Daiy Mil contain vegetable fats instead of cocoa and use a diffeent poduction pocess fom that used to poduce continental type of chocolate. The esulting diffeence in flavou, textue and taste limits significantly the appeal of continental chocolate in the Bitish Isles. 23 See UK Taeove Code, available at 8

9 have exceeded the 60 day limit. Futhemoe, thee was no obvious up-font emedy pacage which could be offeed to ensue a Phase I cleaance. As Kaft and Cadbuy s bands ae multinational bands, divestments to meet emedy equiements in any one county would have been vey commecially unattactive. Desciption of the mege simulation model We developed a mege simulation model in ode to estimate the liely pice impact of the mege between Kaft and Cadbuy in UK, Ieland and Fance. The model elied on the following assumptions: Consumes peceive diffeences between poducts that belong to diffeent categoies (e.g. countlines, tablets, palines); Consumes decide what to buy accoding to what will maximise thei utility (the value that they get fom the goods that they puchase); Each manufactue sets the wholesale pices of its bands to maximise pofits, taing into account the expected competitive esponses of its ivals; and Retailes pice so that they eceive a constant cash magin on thei poducts (e.g. if they mae a magin of 0.20 on a ba of chocolate and the manufactue inceases pices, we assume that the etaile passes some of the pice incease on so that it is still eaning a magin of 0.20). 24 Data desciption The constuction of the mege simulation model elied on two diffeent types of data: Maet data on pices and sales volumes. We used scanne data fom AC Nielsen which ecods, fo each chocolate confectionay SKU: 25 pices and volumes fo each fou wee peiod; poduct chaacteistics (e.g. weight, mil vs. da chocolate, whethe the chocolate contains nuts, caamel etc); band; owne (e.g. Kaft, Mas, Nestlé); and poduct categoisation into countlines, tablets o palines The esults of the simulation ae not significantly diffeent if instead we assume that the etailes eact to wholesale pice inceases by maintaining a constant pecentage cash magin. The simulation model pedicts the same etail pice inceases unde the constant pecentage cash magin pass though ule as unde a constant cash magin ule. Howeve, the mege of two manufactues leads to smalle inceases in wholesale pices unde a constant pecentage cash magin, since the pass though ate of a wholesale pice incease is geate unde a constant pecentage cash magin ule than unde a constant cash magin ule. 25 Stoc eeping unit. This is the most disaggegated level of poduct data, whee even a pomotion on a paticula type of chocolate is ecoded as a sepaate SKU. 26 Calibation and mege simulation esults wee obtained using annual data at the band level. To limit the size of the model, we excluded bands with a shae of supply below 0.5% in themaet. The bands included in ou simulation models epesent above 90% of the volume sales of chocolate confectioney poducts in the UK, Ieland and Fance in Note that the inclusion of additional tivially small bands inceases exponentially the time equied fo the computation of new equilibium pices and quantities in the post-mege scenaio. The impact of the 9

10 Data on costs. We use data fom Kaft Food s management accounts on the vaiable unit costs and wholesale pice of chocolate by band and by segment. 27 Demand side of the simulation model: the nested-logit model To eflect the diffeences that consumes peceive between goups of poducts, we use a paticula functional fom of demand called a nested-logit. The nested-logit model belongs to the family of discete choice models. This type of models epesents consume choices as diven both by pices and poduct chaacteistics. 28 Thei advantage in the context of heteogeneous poducts is that they captue the fact that demand does not depend exclusively on pices but also on vaious quality attibutes. Fo example, we would expect demand fo each chocolate band to depend on its pice and on chaacteistics such as the size of the pacage, flavou and the intended use of the poduct (pesonal consumption, shaing o gifting, etc). The nested-logit model taes into account the fact that cetain poducts ae moe simila than othes, and thus moe liely to be substitutes than othes. 29 The model allows the degee of substitutability between chocolate confectionay poducts within the same categoy o nest to be highe than the degee of substitutability between poducts in diffeent nests. This means that a consume willing to puchase a paticula chocolate poduct (e.g. a chocolate tablet) is moe liely to select amongst othe chocolate poducts in the same nest (othe tablets) when faced with a pice incease athe than to select poducts belonging to a diffeent nest (palines o countlines). This model places each poduct into a paticula categoy o nest. In ou baseline model, each nest coesponds to one of the maet segments as defined by the Nielsen data: countlines, tablets o palines (see Figue 1). Consumes may choose not to puchase chocolate at all, and this is epesented by an outside good. 30 exclusion of these bands in the estimated weighted aveage pice incease should be egaded as negligible due to the extemely low level of volume sales of these bands. 27 Data on aveage vaiable unit cost was povided at the family band level fo See Geene, W. (2002), Econometic Analysis, 5th Edition, Pentice Hall, Chapte We use the tansfomed nested logit appoach that has the additional advantage that it pemits estimation using instumental vaiables. See Bey, S. (1994), Estimating discete choice models of poduct diffeentiation, RAND Jounal of Economics, Vol. 25, Summe, pp See Annex fo a moe detailed discussion. 30 Note that the size of the outside good is lined to the aggegate o maet elasticity of demand. When the maet elasticity is lage, then the outside good is impotant. The elationship between the aggegate demand elasticity and the outside good in a logit model is h whee ε is the maet elasticity, α is a paamete estimated by the ε = α p 1 h model, h is the size of the outside good elative to the size of the inside good and p is the weighted aveage etail pice. A maet elasticity of implies that the size of the outside good is ust thee times the size of the goup of chocolate poducts in ou dataset, so we conside this unealistically low (note that chocolate confectionay sales in the UK ae aound 3 billion pe annum compaed with ove 100 billion gocey sales). 10

11 Figue 1: Nested-logit tee Chocolate confectionay Outside good Countlines Tablets Palines The demand side of the model estimates the pobability that a consume will select poduct, as a function of its poduct chaacteistics, pice, and its sales shae of the nest that it inhabits. Two ey paametes which ae estimated in the demand function ae α, which measues consume esponsiveness to changes in pice and σ, which measues how liely consumes ae to choose an altenative poduct in the same nest vis-a-vis a poduct in a diffeent nest. Own-pice 31 and coss-pice elasticities (inta-nest 32 and inte-nest 33 ) in a nested-logit model can be expessed as a function of these two unique paametes (α and σ) and obseved volume sales: 34 Own-pice elasticity: ε q = p p q = α p 1 σ q 1 σ 1 σ Q G q, (2) N Inta-nest coss-pice elasticity: ε G q = p p q = α p σ q 1 σ Q G q +, (3) N q p q Inte-nest coss-pice elasticity: ε = = α p, (4) G p q N 31 The own-pice elasticity indicates the esponsiveness of the quantity demanded of poduct to a change in its own pice, p. 32 The inta-nest coss-pice elasticity measues the esponsiveness of the quantity demanded of any poduct in the same nest as poduct to a change in the pice of poduct. Indeed, unde a nested logit demand specification, the inta-goup coss pice elasticity fo each poduct is constant fo all possible paiwise combinations within the nest. If the poducts within the same nest ae substitutes, the elasticity will be positive. 33 The inte-nest coss-pice elasticity measues the esponsiveness of the quantity of any poduct in any nest othe than that of poduct to a change in the pice of poduct. Unde a nested logit demand specification, the inte-goup coss-pice elasticity fo each poduct is constant fo all paiwise combinations in all othe nests. If the poducts acoss nests ae substitutes, the inte-nest coss pice elasticity will also be positive. 34 See Annex fo a moe detailed discussion. 11

12 whee q is the quantity sold of poduct, p if the pice of poduct, Q G ae the total sales of nest G to which the poduct belongs, and N is the total maet size, including the outside good. The intepetation of the two ey deteminants of the elasticities, α and σ, is the following. We expect the value of α to be positive as this means that consumes espond to a pice incease by educing demand. Eveything else held constant, a high value of α implies that all elasticities ae lage in absolute tems. The value of σ should be between zeo and one. A value of σ close to one means that both the own pice elasticity and the inta-nest coss-pice elasticity ae lage. On the othe hand, if σ is close to zeo, the own-pice elasticity is small and the inta-nest and inte-nest coss pice elasticities ae identical and also small. This implies that consumes ae equally liely to choose a poduct in the same nest o in a diffeent nest when consideing how to espond to a pice incease. The supply side of the model To model the behaviou of the fims we ely on the Betand oligopoly model with diffeentiated poducts, accoding to which each chocolate manufactue sets the wholesale pices of its bands to maximise pofits, taing into account the expected non-collusive esponses of its ivals. An equilibium esults when none of the fims in the maet can incease its pofit by alteing its pices. Each multipoduct manufactue sets wholesale pices of each of its bands so to maximize pofits taing into account (i) the competitive esponses of its ivals, (ii) the elationship between wholesale and etail pices, and (iii) the esponsiveness of demand to etail pice changes. See Annex fo a moe detailed desciption of the model equations. Calibation of the model Typically the calibation of the model in a mege simulation context equies estimating econometically the paametes of the demand function that detemine the own-pice and coss-pice elasticities of demand fo all poducts in the maet. Unde the nested-logit demand function this entails obtaining point estimates fo α and σ though estimation. As an altenative to calibating the model using a point estimate of the nested-logit demand paametes, it is possible to use the simulation model to identify the ange of the demand paametes (and hence the ange of the ownpice and coss-pice elasticities) which ae feasible fo the fist-ode conditions of the supplies to hold fo each poduct in the maet. The paametes of the demand function can be obtained though calibation togethe with the paametes of the supply side of the model on the basis of eadily available infomation on pices and volumes in the pe-mege state. The simulation model can be used to obtain, togethe with costs, the diffeent combinations of the demand paametes (α and σ) that ae consistent with pofit maximising behaviou by manufactues. Given that the values of α and σ togethe with obseved volume sales detemine own-pice and coss-pice elasticities, this is equivalent to calibating the ange of own-pice and 12

13 coss-pice elasticities that ae consistent with (i) utility maximising behaviou by consumes, and (ii) pofit maximising behaviou by manufactues, given the actual (obseved) level of pices, sales and costs. Ou appoach follows standad techniques fo the calibation of simulation models. Specifically, we tae the paametes α and σ as inputs into the model, and then calibate a set of costs such that the coesponding etail pices pedicted by the model match those in ou data set and ae consistent with optimal behaviou by the manufactues. The calibation pocedue wos in two stages. Fist, we calibate maginal costs fo each possible combination of the paametes α and σ in the nested-logit model of demand, given a value of the aggegate demand elasticity. Second, we select as plausible (feasible) those combinations of α and σ such that (i) the coesponding calibated maginal costs ae positive fo all bands included in the model, and (ii) aveage calibated costs ae boadly consistent with the financial infomation povided by Kaft. In tems of methodology, we poceed by assuming a value fo the maet elasticity of demand (and then checing obustness via sensitivity analysis), and then by obtaining combinations of α and σ and values of maginal costs via a calibation execise. Fo all plausible combinations of α and σ (elasticities) and aggegate demand elasticities we simulate the effect of the mege (i.e. a change in the owneship stuctue and potential efficiencies). This povides us with a ange of pedicted pice inceases, within which the tue pice incease is liely to fall. Figue 1 below summaizes the vaiables and the data souces used in the calibation of the model. 13

14 Figue 2: Vaiables and paametes used in the mege simulation Vaiables Sceening device Pices Maet data Quantities Maet data α Range σ Range (0 1) Nest stuctue Assumption / maet data Owneship stuctue Maet data Maginal costs Calibated and maet data Maet size Assumption (aggegate elasticity) Fo each possible combination of α and σ, calibate the model and obtain a vecto of calibated maginal costs. Select those combinations of α and σ that ae consistent with actual cost infomation Fo each selected combination of α and σ, obtain the pedicted pice incease though simulation. The intuition behind the pocedue is as follows. Fo any set of pices and costs, thee is only a limited numbe of combinations of the nested-logit demand paametes which ae consistent with the undelying assumptions egading pofit maximising behaviou and maet segmentation. Without having to econometically estimate the demand paametes, ou pocedue allows fo simulations to be caied out fo all feasible combinations of the demand paametes, each of which will esult in a diffeent set of pedicted pices and volumes. The value of this methodology is that it allows simulating the ange within which the actual postmege pice incease will fall, unde altenative nest stuctues (i.e. naowing the set of substitutes), classification of poducts into nests and assumptions about aggegate demand elasticity without the need fo obtaining econometically pecise estimation of elasticities. The poposed methodology is of paticula value as a sceening device in mege contol, as it allows assessing the ange within which the pice effect of a mege will fall unde many altenative scenaios within the time constaints of Phase I investigations. If the uppe bound simulated pice incease is not significant, then it is not necessay to futhe the investigations. On the contay, if the ange is sufficiently high, it may be clea that emedies ae equied (without the pecise pice effect being nown), o that futhe investigation needs to be caied out to assess moe pecisely the competitive impact of the mege. We applied this methodology in the Kaft/Cadbuy in the UK, Ieland, and Fance. We found that the uppe bound pice inceases wee not mateial. In the thee counties the mege simulation 14

15 esults using indicated a vey modest (weighted) aveage pice incease fo the entie maet as well as fo the set of poducts included in each of the nests o maet segments. This esult held even when the model had been subected to a numbe of obustness tests both by the authos and by the Euopean Commission. The obustness tests included: changes in assumptions about cost efficiencies; naowing of consume choice of substitutes though changes in the nest stuctue; changes in the assumed aggegate maet elasticity, and changes in the classification of poducts into nests. 35 The esults fom this execise wee egaded by the Commission as significant evidence that the poposed opeation was unliely to lead to significant pice inceases in the UK, Ieland and Fance. 36 At the equest of the Commission, we also estimated econometically the demand paametes in the UK, and Ieland, unde one specific nest stuctue, and poduct classification. 37 The obtained point estimates of the demand paametes ( α and σ) in both counties fell within the ange obtained peviously though calibation. Estimated elasticities wee theefoe consistent with those used to conduct mege simulation using calibated elasticities, and wee also consistent with estimated elasticities fo chocolate poducts in the UK found in ecent academic liteatue, and simila to estimated elasticities fo othe food poducts and consume goods. 38 On the basis of these point estimates, and following the Commission s suggestion, we launched simulations, and found that the pedicted pice incease also fell within the ange of pedicted pice inceases peviously obtained fo UK and Ieland using calibated elasticities. The Commission in its decision disegaded the use of the elasticities point estimates (and point estimate of the pice incease) aguing the estimated demand paametes wee sensitive to the inclusion of some contols (in paticula nest dummy vaiables) and issues elated with the set of selected instuments. 39 Howeve, the Commission egaded the mege simulation esults obtained though calibation of the demand paametes as obust and sufficient evidence that the mege was unliely to esult in 35 The calibated models wee used to simulate equilibium pices and volumes of all poducts in the maet afte the mege unde altenative scenaios about cost efficiencies of 0%, 5% and 10%. Calibation and the subsequent simulation of post-mege pices was also conducted fo altenative values of the aggegated demand elasticity. In paticula, and as a obustness test, we set aggegate demand elasticity of chocolate to tae the values of -0.75, -,1 and -1.25, and found no mateial impact on ou esults in tems of pedicted pice inceases. Othe obustness test included changes in the teatment of pivate labels, the nest stuctue consideed and the citeia used fo the selection of plausible values fo α and σ See Euopean Commission Decision in Case No. COMP/M.5644 Kaft Foods/Cadbuy, available at: 36 In its decision, the Commission egaded the esults of the mege simulation execise as consevative in the sense that the independence of ielevant altenatives assumption supposes that switching between poducts happens popotionally to maet shaes within a nest, while the qualitative and quantitative evidence gatheed duing the maet investigation indicated that the paties poducts wee not seen as paticulaly close competitos. 37 Demand estimation fo the UK and Ieland was conducted using the oiginal AC Nielsen maet segmentation and poduct classification.. 38 See Buno, H. A., and N. Vilcassim (2008): Stuctual Demand Estimation with Vaying Poduct Availability, Maeting Science, 27(6), , which estimates the own-pice elasticity of demand fo chocolate bands in the UK; Hausman and Leonad (1997): Economic Analysis of Diffeentiated Poducts Meges Using Real Wold Data, Geoge Mason Law Review; Abay and Jones (2006), Demand Elasticities and Pice-Cost Magin Ratios fo Gocey Poducts in Diffeent Socio-Economic Goups, Agic.Econ (5): See Euopean Commission Decision in Case No. COMP/M.5644 Kaft Foods/Cadbuy. 15

16 anti-competitive effects in the UK, Fance o Ieland. Thus, futhe analysis o discussion aound the detemination of point estimates fo elasticities was not egaded as necessay. The Commission concluded that the mege would not lead to a significant pice incease and the tansaction was cleaed in Phase I with no divestments in the UK, Ieland, and Fance. 40 D. CONCLUSIONS The use of simulation techniques in mege contol has been typically esticted to in-depth Phase II investigations. One eason behind this is that full-fledged mege simulation models equie estimating econometically own-pice and coss-pice elasticities of demand fo all poducts in the maet. The time needed to obtain eliable estimates of the assumed demand function paametes, togethe with the time constaints that apply in Phase I investigations, have significantly limited the use of mege simulation techniques in Phase I investigations. In this pape we show a method that avoids the need to obtain econometically pecise point estimates of own-pice and coss-pice elasticities in the context of a nested-logit demand function, instead using cost and maet shae data to deduce the ange within which the actual demand elasticities ae liely to fall. The poposed method can be used to assess the ange within which the pice effect of a mege is liely to fall unde a wide ange of altenative scenaios about nest stuctues (i.e. naowing the set of substitutes), classification of poducts into nests, assumptions about aggegate demand elasticity, efficiency gains, and potential divestitues. This methodology can be successfully employed within the time constaints of Phase I and, theefoe, is paticulaly suitable as a sceening device fo anticompetitive effects in mege contol. If the pedicted pice incease ange is sufficiently low (i.e. the uppe bound pedicted pice incease not significant), then the competition authoity may each comfot that the mege will have a limited effect on competition eliminating the need to estimate the pecise pice effect of the mege. Similaly, if that ange is sufficiently high, it may be clea that emedies ae equied, without the pecise pice effect being nown. Thee will be some situations whee the pecise point estimate may matte, indicating that futhe investigation is equied. 40 Divestments wee though equied in Romania and Poland, whee Cadbuy s poducts wee local bands with a vey significant pesence in tems of maet shae. 16

17 E. ANNEX The simulation model has a demand side and a supply side. The demand side is modelled using a nested-logit model. It taes into account peceived diffeences between poducts by gouping them into nests. The supply side of the model assumes that manufactues set pices to maximise pofits, taing into account the competitive esponses of thei ivals, and that etailes geneate a constant cash magin on thei poducts. 41 The model taes into account actual constaints between manufactues poducts. Howeve, it does not tae into account othe picing constaints, such as buye powe and enty. The model can tae into account cost efficiencies. Demand side of the simulation model: the nested-logit model This section eviews the theoetical undepinnings of the nested-logit model. The model is based on the assumption that poducts can be gouped into G+1 sets o nests, g = 0,1,...,G, whee goup 0 epesents the altenative of not puchasing any chocolate confectioney poduct (the outside good ). The utility to consume i fom puchasing poduct is given by: 42 ui = δ + ζ + ( 1 σ ) ε [1] ig i The fist tem, δ, is the mean valuation fo poduct, common to all consumes. It depends on the etail pice of poduct, p, a vecto of obseved chaacteistics of poduct, x, and an eo tem eflecting unobseved chaacteistics: δ = x β αp + ζ [2] The second and the thid tem in [1], ζ ig deviation fom the mean valuation. The tem poducts belonging to goup, and ε, ae andom vaiables eflecting individual i s i ζ ig stands fo consume i's utility, common to all G, wheeas the tem ε i epesents consume i's utility, specific to poduct. The paamete σ lies between 0 and 1 and measues the coelation of the consumes' utility acoss poducts belonging to the same poduct goup o nest. If σ = 1, thee is a pefect 41 The esults of the simulation ae not significantly diffeent if instead we assume that the etailes eact to wholesale pice inceases by maintaining a constant pecentage cash magin. In this latte case, the mege of two manufactues leads to a smalle incease in wholesale pices, since the pass though ate of a wholesale pice incease is geate unde a constant pecentage cash magin ule than unde a constant cash magin ule. The pedicted pice inceases at the etail level emain unalteed. 42 See Ivaldi and Veboven (2000), The Euopean Heavy Tucs Maet: an Economic Analysis, epot fo the Competition Diectoate Geneal of the Euopean Commission fo a detailed deivation of the demand equation. 17

18 coelation of pefeences fo poducts within the same goup o nest; so these poducts ae peceived as pefect substitutes. As σ deceases, the coelation of pefeences fo poducts within same goup deceases. If σ = 0 thee is no coelation of pefeences and consumes ae equally liely to switch to poducts in a diffeent goup o nest as to poducts in the same goup in esponse to a pice incease. This is the case of a logit model accoding to which all poducts in the maet compete symmetically with each othe. Nomalizing the mean utility level fo the outside good to 0, i.e., δ 0 =0, the pobability potential consume chooses poduct is given by: s that a s δ 1 σ e = D G (1 + ( D G G ) ( D (1 σ ) G ) (1 σ ) ) [3] whee D G = G e δ 1 σ and G denotes the goup of poducts that belong to the same nest than poduct. Taing logaithms and eaanging [3], we get: ln s ln s0 = x β αp + σ ln s + ζ [4] G whee q s =, N q s G = = G q q Q G, N q s = and N stands fo the total size of the 0 N maet. Using the demand expession [4] descibed above we can deive the own and coss pice elasticities at the etail level as a function of paametes α and σ and obseved volume sales: 43 Own-pice elasticity: ε q = p p q = α p 1 σ q 1 σ 1 σ Q G q, [5] N Inta-nest coss-pice elasticity: ε G q = p p q = α p σ q 1 σ Q G q +, [6] N q p q Inte-nest coss-pice elasticity: ε = = α p, [7] G p q N 43 To obtain these elasticities, we use the logaithm appoach fo the calculation of elasticities, i.e. q p ln q ε = p q ln p 18

19 whee q is the quantity sold of poduct, p, is the etail pice of poduct, Q G ae the total sales of nest G to which the poduct belongs, and N is the total maet size, including the outside good. Supply side of the simulation model To model the behaviou of the fims we ely on the Betand oligopoly model with diffeentiated poducts, accoding to which each chocolate manufactue sets the wholesale pices of its bands to maximise pofits, taing into account the expected non-collusive esponses of its ivals. An equilibium esults when none of the fims in the maet can incease its pofit by alteing its pices. Each multipoduct manufactue sets wholesale pices of each of its bands so to maximize pofits taing into account (i) the competitive esponses of its ivals, (ii) the elationship between wholesale and etail pices, and (iii) the esponsiveness of demand to etail pice changes. To maximize pofits manufactue f chooses fo each poduct the wholesale pice following fist-ode condition: 44 q ( ~ ) ( ~ + q q q + ) + ( ~ ) p c p c p c = 0 [8] p p p whee: G S f G S f p stands fo the pice set by the etaile fo poduct of wholesale f; w p to solve the c ~ stands fo the sum of the maginal cost incued by the wholesale to poduce poduct plus the etaile s magin [ ~ w w c = ( c + m ), and m = ( p p ) constant fo all values of the poduct ; w p ], and epesents the full cost to the manufactue of poducing and maeting S f is the set of poducts shipped by fim f; and G denotes the goup of poducts that belong to the same nest as poduct. Using the demand elasticity expessions [5], [6] and [7] and eaanging, the fist ode condition of the pofit-maximization poblem of the wholesale f can be estated as: ( p ~ 1 1 c ) = α 1 σ G Q f G Λ 0 q G Λ S f 1 [9] 44 Note that a multipoduct fim taes into account all its poducts in the pofit maximization decision. 19

20 whee G σ 1 = 1 σ Q G 1 + N 1, = N 0, Λ f = ( 0 ) Q G G σ, and f 1 Λ = ( + 0 ) G Q G. 1 σ 20