Marc Ivaldi
email: marc.ivaldi@tse-fr.eu

Jiekai Zhang Mars

Jiekai Zhang
email: jiekai.zhang@hanken.1i.

Hugo Molina

Tanja Saxell

Markku Siikanen

Otto Toivanen

Ari Hyytinen

Markus Reisinger

Heiner Schumacher

Carlo Cambini

Gregory Crawford

Pierre Dubois

Pierre Fleckinger

Margaret Kyle

Tuomas Takalo

"Platform Mergers: Lessons from a Case in the Digital TV Market"

Keywords: JEL Classication: K21, L10, L40, L82, M37 merger, remedy, two-sided market, platform, TV, advertising

INTRODUCTION

Competition authorities have been particularly concerned in recent years by the behavior of dominant rms in two-sided markets, which provide services on one side but generate revenues on the other, in a way that could harm the interest of consumers. 1 In the internet industry, users search the web free of charge, but trigger advertisements which generate revenues for the rms that supply the search engines.

Similarly, in the digital TV market, when viewers watch a program for free, they receive a ow of advertisements that generate revenues for the TV channels. Dominant rms in these markets often provide better services than their rivals without charging an extra monetary price to their users. In this way, the dominant rms retain more users, which, in turn, increases their attractiveness in the advertising market. While consumers enjoy a free service from these rms, they may be overwhelmed by the amount of advertising. This can be even more problematic when the dominant media companies get bigger by acquiring smaller competitors. On the one hand, the dominant rms can oer better services by expanding their customer base, which allows them to show more advertisements (as a non-monetary price) to their users. On the other hand, the acquisition can increase the market power of the merging rms in the TV advertising market, which allows them to charge higher prices to the advertisers. This paper studies this situation by providing a structural analysis of the acquisition of two new entrants in the French digital TV market, namely, channels NT1 and TMC, by a big media holding company, the TF1 Group. We observe a unique situation in which the French competition authority approved the merger of the broadcasting services of the two purchased channels with those of the acquiring company but blocked the merger of their advertising sales houses (ASHs herein). 2 In practice, the competition authority imposed a behavioral remedy which requires that the ASH of NT1 and TMC remain separate from that of the main channel of 1 See for instance the European Commission decision of March 2019 to ne Google 1.49 billion euros for abusive practices in online advertising. 2 An ASH handles and sells the advertising time available on the TV stations that it works for.

the TF1 Group, namely, channel TF1. 3 In this decision, the authority wished to improve the broadcasting quality of the two purchased channels without generating detrimental eects for the advertising market. 4 Its expectation was to enhance competition for viewers among TV broadcasters without reducing competition in the TV advertising market.

While the competition authority's examinations of both the broadcasting and advertising sides of the market were straightforward, its decision nevertheless treated the channels' advertising services separately from their broadcasting services. We

show here that ignoring the interaction between the two sides of the market can result in unexpected outcomes.

Using a comprehensive dataset on the French digital TV market, covering two years of the pre-acquisition period and three years of the post-acquisition period, we rst provide reduced-form evidence that the French TV market is a two-sided market.

In other words, we show that cross-side network externalities between viewers and advertisers do exist. Based on the assumption that TV channels are two-sided platforms, we then build a structural model which describes the demand functions of TV viewers and advertisers as well as the objective of the ASHs.

TV viewers' preferences between dierent channels are approximated by a nested logit model. We take care to solve the endogeneity issues related to this type of model, thanks to data on the genres of broadcasting content. Our estimates of demand elasticities with respect to the amount of advertising indicate a 10 percent increase in advertising time causes a mean audience loss of about 10.55 percent for a private TV station, suggesting that the negative externalities that advertisers generate for TV viewers are not negligible.

Our novel approach to model the demand of advertisers considers their multihoming behavior and allows us to estimate, using aggregated advertising data, whether a channel is a substitute or complement to another channel for advertisers. 5In more detail, we model the advertisers' objective as one that minimizes the total advertising costs which generate the desired audience, and estimate the advertisers' cost function approximated by a translog model. We nd that the advertisers consider the two purchased channels (NT1 and TMC) as substitutes, but both as complements of channel TF1. This demand pattern of advertisers implies that merging the ASHs of the three channels would not result in a signicant increase in the price of advertising.

Based on the estimates of the model parameters, we evaluate the consequences of the acquisition. Using post-acquisition data, we rst show that merging the broadcasting services of TV channels has consequences for their equilibrium amounts and prices of advertising. While the joint management of broadcasting services improves the broadcasting quality of the merging channels, it also increases the value (and therefore the prices) of their advertising slots. At equilibrium, the negative externalities that advertisers generate for viewers incentivize the ASHs to increase the amount of advertising following an increase in TV channels' broadcasting quality.

Welfare analysis further suggests that the total surplus of TV viewers has decreased post-acquisition, meaning that the positive eects of the increase in broadcasting quality on the viewers' surplus are not suciently large to outweigh the associated detrimental eects of the increase in the amount of advertising. Overall, the approved merger harms consumers (both viewers and advertisers) but benets the TV stations.

To gain an insight into the potential consequences of merging the ASHs of the three channels, compared to the observed situation under the remedy of keeping them separated, we simulate the equilibrium amount of advertising and its price for the case in which the advertising time of NT1, TMC and TF1 is chosen and sold by one common ASH. The results show that merging the ASHs of the three channels would only slightly increase their total advertising, due to the small substitution eect of advertising on the viewers' side, and would have almost no impact on the price of advertising, since the slots of the two merged ASHs are complements for tarity among TV channels in the analysis of equilibrium outcomes of our model.

advertisers. Moreover, welfare analysis conrms that the implemented behavioral remedy is ineective once the merger on the broadcasting side has been approved.

This paper rst contributes to the relatively limited empirical literature on twosided markets. Beginning with the seminal articles of [START_REF] Rochet | Platform competition in two-sided markets[END_REF] and [START_REF] Armstrong | Competition in two-sided markets[END_REF], theoretical papers have addressed TV advertising competition by considering the amount of advertising as a nuisance to TV viewers (e.g., [START_REF] Anderson | Market provision of broadcasting: A welfare analysis[END_REF]; [START_REF] Cunningham | A theory of broadcast media concentration and commercial advertising[END_REF]; [START_REF] Nilssen | TV advertising, programming investments, and product-market oligopoly[END_REF]).

In practice, very few empirical papers have estimated audience elasticities of demand with respect to the amount of advertising. [START_REF] Wilbur | A two-sided, empirical model of television advertising and viewing markets[END_REF] nds TV viewers dislike advertising. A similar attitude of viewers towards advertising has also been found in the radio and newspaper industries. (See Jeziorski [2014a]; [START_REF] Ivaldi | The dierentiated eect of advertising on readership: Evidence from a two-sided market approach[END_REF].)

However, empirical studies have also found audiences appreciating advertising in yellow pages and magazines. (See [START_REF] Rysman | Competition between networks: A study of the market for yellow pages[END_REF]; [START_REF] Kaiser | Price structure in two-sided markets: Evidence from the magazine industry[END_REF]; [START_REF] Ivaldi | The dierentiated eect of advertising on readership: Evidence from a two-sided market approach[END_REF].) Here, identifying the sign of the network externalities that the advertisers generate for viewers is crucial, as it impacts the strategic behavior of the ASHs in the advertising market. If viewers dislike advertising and switch channels during the advertisements, the ASHs would restrict the amount of advertising on TV to avoid losing viewers, but would increase the amount of advertising on a TV channel following an increase in its broadcasting quality. In our estimates, we nd a statistically signicant disutility of advertising to TV viewers.

This paper also contributes to the extensive literature on mergers. Post-merger analysis has been adopted to evaluate the eectiveness of competition policy in numerous industries, such as airlines [START_REF] Borenstein | Airline mergers, airport dominance, and market power[END_REF]; [START_REF] Kim | Mergers and market power: Evidence from the airline industry[END_REF]), banking (Facarelli and Panetta [2003]), petroleum [START_REF] Hastings | Vertical relationships and competition in retail gasoline markets: Empirical evidence from contract changes in southern California[END_REF]; [START_REF] Gilbert | Market power, vertical integration and the wholesale price of gasoline[END_REF]; [START_REF] Hosken | Does concentration matter? Measurement of petroleum merger price eects[END_REF]), and appliances [START_REF] Ashenfelter | The price eects of a large merger of manufacturers: A case study of Maytag-Whirlpool[END_REF]). [START_REF] Ashenfelter | The eect of mergers on consumer prices: Evidence from ve mergers on the enforcement margin[END_REF] assess mergers in ve dierent branded-goods industries. Björnerstedt and Verboven [2015] evaluate the performance of merger simulations in the Swedish analgesics market. In line with the previous literature, we evaluate the ex-post consequences of an approved acquisition under behavioral remedy in the digital TV market. Our structural analysis is related to another branch of the merger literature that quanties the welfare eects of mergers. Examples of such articles include [START_REF] Baker | The gains from merger or collusion in product-dierentiated industries[END_REF], Hausman et al. [1994], [START_REF] Werden | The eects of mergers in dierentiated products industries: Logit demand and merger policy[END_REF], and [START_REF] Nevo | Mergers with dierentiated products: The case of the ready-to-eat cereal industry[END_REF], among others.

More recent literature has addressed the issue that the merger may change the rms' product positioning [START_REF] Gandhi | Post-merger product repositioning[END_REF] and/or impact the quality of product oered at equilibrium [START_REF] Chen | Mergers and product quality: Evidence from the airline industry[END_REF]). [START_REF] Gandhi | Post-merger product repositioning[END_REF] show that the product repositioning has consequences for the pricing decisions of rms. [START_REF] Mazzeo | The welfare consequences of mergers with endogenous product choice[END_REF] nd that the product-oering adjustment results in additional eects on protability and consumer welfare that are not realized by price responses alone. Li et al. [2020] highlight that ignoring the potential changes in product oering may bias the merger evaluation.

Sweeting [2010] has initially studied this topic in detail in the context of twosided media markets. [START_REF] Fan | Ownership consolidation and product characteristics: A study of the US daily newspaper market[END_REF] develops a model which endogenizes the choice of characteristics and shows that ignoring adjustments of product characteristics causes substantial dierences in the estimated eects of mergers. Jeziorski [2014a] decomposes the changes in consumer surplus into product repositioning eects and advertising quantity readjustment eects, and shows that the product repositioning eect of a merger improves consumer surplus but the resulting advertising readjustment reduces it. In the same vein, combining ex-ante and ex-post data, we show that the post-merger changes in product quality impact market pricing and welfare.

Our data are not suciently disaggregated to allow us to study the repositioning of products after the merger. However, we demonstrate the key role of the quality of broadcasting services, alongside that of two-sided network externalities, in determining the magnitude of the impact of the merger.

In an article related to this strand of literature, [START_REF] Gentzkow | Competition and ideological diversity: Historical evidence from us newspapers[END_REF] study the competitive forces which shaped ideological diversity in the US press. Their results highlight that optimal competition policy must account for the two-sidedness of the news market. Similarly, we take advantage of our relatively detailed advertising data to provide evidence that when TV channels increase their broadcasting quality, they also raise the amount of advertising for viewers and the prices of advertising for advertisers, in response to the cross-side network externalities between viewers and advertisers. In other words, the two-sidedness of the digital TV market cannot be ignored. 6 The remainder of this paper is structured as follows. In Section II, we present the French digital TV market. In Section III, we model the demand of TV viewers and of advertisers. The demand estimates are presented in Section IV. We carry out the merger evaluation in Section V and conclude in Section VI. The audience data come originally from Médiamétrie, which builds a measure of audience by recording the television usage every second by a panel of households equipped with one or more TV sets in their main residence. 7 From these raw data, 6 In the context of this paper, the amount of advertising can be viewed as a price that TV stations charge their viewers. 7 This panel has been built to account for both the socio-demographic characteristics of households in metropolitan France and the structure of the television supply. It is made up of nearly 4,300 households, which corresponds to approximately 10,500 individuals aged 4 and over. In each home, Médiamétrie installs one or more audimeters (depending on how many pieces of equipment there are) tted with a remote control with individual keys, which constantly record all uses of the television set(s) in the household and all the viewing habits of each member of the household and their guests. (See http://www.mediametrie.fr.) The advertising data more precisely, the gross advertising revenues and the number of advertising seconds come from Kantar Media. Using this information, we estimate the monthly average advertising price per second by dividing each channel's gross advertising revenues by the number of seconds of advertising in the month. This is the price that the ASHs charge advertisers in our model.

Table II presents summary statistics of the main variables in our analysis. The total number of TV viewers per channel per second is on average equal to 3.84 thousand. A TV channel broadcasts on average 56 hours (i.e., roughly 0.2 million seconds) of advertising per month. The average advertising price is 5.96 euros per second. These three main variables are measured at the monthly level for each channel. In addition to the above data provided by the CSA, Médiamétrie supplied us with complementary information on the broadcasting content of the 12 TV stations in our sample. In more detail, we observe the monthly broadcasting hours of six major genres of TV shows per channel per month during the period of study (i.e., 8 In practice, the number of viewers of channel j in month t, y jt used in the model later, is dened as follows. Médiamétrie measures the number of viewers of channel j at each second s. Assuming there are 30 days in month t (so in all, 2,592,000 seconds in the month), the monthly average number of viewers of channel j, y jt , is equal to 2592000 s yjs 2592000 , where y js denotes the total number of viewers of channel j at second s. In other words, y jt denotes the average number of viewers per second of channel j in month t.

Market Structure

TV stations could be considered as two-sided market platforms connecting viewers to advertisers. They provide two services: TV shows to viewers on one side, and advertising slots to advertisers on the other. While viewers enjoy the news and entertainment content on TV, they receive the ow of advertising. When TV viewers see the advertisements, this generates an audience for the advertisers.

For an industrial organization to be considered as a two-sided market, we must identify the externalities between the consumers on the two sides, which are here the viewers and the advertisers. On the viewing side, the amount of advertising should have a signicant eect on the viewership of TV channels, and the direction of such an eect depends on how do the TV viewers perceive the advertising. In Table XIII in web appendix, we establish the relationship between the amount of advertising and the number of viewers.9 Controlling for the broadcasting hours of dierent program genres, the channel-, month-, year-xed eects, and the endogeneity issue with the number of advertising seconds using BLP IVs, data suggest a negative eect of advertising on the viewership. 10 In other words, the advertisers generate negative cross-side externalities for viewers.

On the advertising side, advertisers buy time slots of TV channels from their respective advertising sales houses (ASHs). The ASHs charge advertisers a price per second of advertising that warrants a certain level of audience. Intuituvely, the willingness to pay of advertisers should be higher for the advertising seconds of a channel which attracts more viewers. Accordingly, we should expect higher advertising spending on the advert seconds with more viewers. In Figure 1 in Appendix below, we present a binned scatterplot of the relation between the number of viewers per second and its corresponding advertising spending, i.e., the per second advert price dened in Section II(i), controlling for channel-, month-and year-xed eects.11 Data show that a higher viewership is associated with higher advertising spending, meaning that the TV viewers generate positive network externalities for the advertisers; moreover, there are network-eect advantages to having a broader audience in the sense that an advertising second is more valuable (expensive) when it is seen by more viewers.

The advertisers combine the advertising slots on several TV channels to achieve a certain overall reach of audience at the lowest cost. In other words, the advertisers practice multi-homing strategies.

TV programming is decided by the TV channels several months in advance.

Based on the broadcasting content provided by the TV channel, each ASH determines its optimal supply of advertising slots. The amount of advertising of a broadcast TV station is, however, subject to two regulation caps enforced by the French law, at the hourly and daily average levels. 12 In Table XII in web appendix, we compare the eective amount of advertising to the maximum minutes of advertisements per month allowed for each TV station (calculated based on the daily average level of the regulation cap); we note that the observed advertising time of dierent TV channels is well below the regulatory ceilings. 13,14 The ASHs behave as Cournot-type rms, since they adjust the amount of advertising according to the quality of the TV program and the sensitivity of viewers to the amount of advertising. At equilibrium, each ASH's objective is to determine the optimal amount of advertising on each channel under its management to maximize the sum of its prots from all of these channels. 15 Unlike pay TV channels, which charge subscription fees to their viewers, freebroadcast TV stations only require their viewers to bear the advertising. While the pay TV stations play an important role in the U.S. TV market, they are much less common in France. Although there were between 184 and 207 pay TV channels available in the French TV market during the observation period, neither their total audience share nor their total advertising revenue share exceeds 10 percent. 16 In 12 The average time per hour per day devoted to advertising must not exceed six minutes for public TV channels, nine minutes for the incumbent private channels, and 12 minutes during the rst seven years of broadcasting for the new channels launched in 2005. Moreover, the advertising time cannot exceed 12 minutes within any given clock hour for private TV broadcasters and eight minutes for public TV broadcasters. 13 As we use monthly data in this paper, we computed the maximum minutes of advertisements per month allowed for each TV station from its daily average level of regulation cap imposed by the regulator: The maximum minutes of advertisements allowed for channel j in month t is equal to the maximum minutes of advertisements per day allowed for channel j in month t multiplied by the number of days in month t.

14 Regulatory constraints at the hourly level can be binding during prime time, though our monthly aggregate data does not allow us to explore this. Crawford et al. (2017) and [START_REF] Zhang | Regulating advertising quantity: Is it ecient?[END_REF] have studied this issue in detail. 15 As long as TV channels comply with legal regulations regarding the maximum amount of advertising per hour and per day, the ASHs have full exibility to adjust the length of advertising breaks and the number of advert slots within each hour and within each day. 16 See for instance:

https://www.csa.fr/Informer/Collections-du-CSA/ Panorama-Toutes-les-etudes-liees-a-l-ecosysteme-audiovisuel/Les-chiffres-cles/ Les-chiffres-cles-de-l-audiovisuel-francais-Edition-du-2nd-semestre-2013 addition, most of the pay TV channels specialize in one theme and target a specic audience (children, young women, etc.), while the 12 major free-broadcast TV stations included in our study are generalist TV channels which aim to serve a wide audience. We group all the pay TV channels into an outside option of our model because the statistics on the audience share of an individual pay TV channel are not available, due to their negligible share of the market.

II(iii).

Eects of the Merger

At this point, we can provide an initial insight into the impact of the merger decision by the French competition authority: Joint operation of the broadcasting services of three TV channels is approved, but some rewalls reducing the coordination between their respective advertising sales houses (ASHs) are in place.17 Note that this decision is opposite to those made by DOJ in the US newspaper market, which allow the newspaper companies to combine advertising sales services under Joint Sales Agreement (JSA) but maintain separate and competitive editorial operations. 18We estimate the following regression, in line with [START_REF] Ashenfelter | The eect of mergers on consumer prices: Evidence from ve mergers on the enforcement margin[END_REF] and Björnerstedt and Verboven [2015]:

ln A jt = µ 1 j + µ 2 t + ρ j P ostAcquisition t + η jt ln P jt = τ 1 j + τ 2 t + λ j P ostAcquisition t + ω jt
where A jt denotes the amount of advertising by channel j and P jt its price during period (month-year) t; µ 1 j and τ 1 j denote the channel xed eect; µ 2 t and τ 2 t denote the month-year time xed eect; the variable P ostAcquisition t is equal to 0 from March 2008 to January 2010, and is equal to 1 from January 2010 to December 2013, for all channels. 19The estimation results presented in Table IV suggest that the acquisition led to a signicant increase in both the amounts and prices of advertising by the three merging channels. 20 It seems that the behavioral remedy, which imposes separate advertising sales operations by the two ASHs of the three merging channels, is ineective in keeping the advertising side of the market unchanged post-acquisition.

Indeed, since the programming of content on the three channels is coordinated, one could conjecture that the two separate ASHs would anyway take into account the changes in programming strategy of the three TV channels when determining their respective prot-maximizing amounts of advertising, and therefore chose dierent advertising levels post-acquisition. This is similar to what Gowrisankaran, [START_REF] Gowrisankaran | Mergers when prices are negotiated: Evidence from the hospital industry[END_REF] show in the setting of health insurance merger: A behavioral remedy which imposes separate business operations for the merging parties is ineective in preventing post-merger price increase. Below we specify a structual econometric model that we use to assess the welfare eects the acquisition and the behavioral remedy.

III. DEMAND MODEL

We now present our structural model for the demand of TV viewers and advertisers.

We explain here the motivation behind the choice of our specication. Then, in the next section, we discuss the estimation results for these models.

dierence-in-dierences estimators, where the dierence between the merging rms' ρ j (or λ j ) and the competitors' ρ j (λ j ) measures the eect of the acquisition on the amounts (prices, respectively) of advertising under the assumption that the acquisition does not have an impact on the competitors' amounts (prices) of advertising. In practice, however, the acquisition could raise the competitors' prices as well; then, the dierence between the merging rms' ρ j (λ j ) and the competitors' ρ j (λ j ) could be viewed as a lower bound of the eect of the merger on the amount (price) of advertising of channel j. 20 We use our full sample (22 months' pre-acquisition data and 47 months' post-acquisition data) to estimate the channel-specic treatment eects ρ j and λ j that are presented in Table IV. Since the acquisition was announced in January 2010, while the merger of broadcasting services of the three channels could have taken time, we also re-estimated ρ j and λ j , excluding the post-acquisition data immediately following the announcement of the acquisition (from February 2010 to December 2010), i.e., using 22 months' pre-acquisition data and 36 months' post-acquisition data, as a robustness check. The estimates using this reduced post-acquisition sample are presented in Table XIX in web appendix. We obtain very similar results with the two samples. The percentage eects on the amount of advertising and price are obtained from a transformation of the parameters ρ j and λ j using exp(ρ j ) -1 and exp(λ j ) -1. Others × Acquisition presents the estimated average eect of the acquisition on the non-merging channels. Standard errors are computed using the delta method. ***p <0.01, **p <0.05, *p <0.1

III(i).

Viewers' Demand

We specify the viewers' demand using a nested-logit model, which classies the choices of the TV viewers into g groups (or nests) and an additional group for the outside goods. As it is well known, one of the main properties of this model is that choices within the same group are closer substitutes than choices from dierent groups (see [START_REF] Berry | Estimating discrete-choice models of product dierentiation[END_REF]).

Our sample includes 12 major broadcast TV stations: ve incumbent channels and seven new entrants. We categorize the incumbent channels and the new entrants into two dierent groups to take into account their dierent brand awareness, type of content, and quality. 21 The model allows for the probability that a representative viewer chooses an incumbent channel to be higher than the probability of choosing a new channel, which is consistent with their respective audience shares and with the higher reputation and quality of incumbent channels compared to the new channels.

The outside option is dened as not watching one of the channels included in the model.

Formally, in each period t, the indirect utility of consumer i from watching channel j, belonging to the group g (incumbent, entrant or outside good), is given by:

(1)

U i jgt = δ jt + ζ i jgt
where δ jt denotes the mean utility level of TV viewers from watching channel j or choosing the outside good at time t and ζ i jgt denotes the departure of consumer i's preference from the common utility level. 22 We dene:

(2)

δ jt = q jt + αA jt
where q jt denotes the perceived quality of channel j in period t and A jt denotes the amount of advertising. We model the quality by q jt = X jt β + ξ jt , where ξ jt is a random term capturing the unobserved quality of channel j in period t and X jt is a matrix of variables including observed content characteristics, channel-xed effects, as well as month-and year-xed eects. The observed content characteristics are broadcasting hours of TV series/movies, entertainment, news, culture/science, sports and cartoons, capturing the observable channel-time specic broadcasting quality. Channel dummies capture the brand awareness of each individual TV station; year dummies capture the potential changes in policy, uctuations of the ecoevening, from 18:00 to 23:00, while the new entrants have the whole day to carry out the same obligation. By consequence, the seven new entrants do not enjoy the same market position as the ve incumbent channels: The audience shares of the new channels are remarkably lower than those of the incumbents (see Table IX in Appendix below for detailed statistics). 22 Recall that we observe the monthly average number of viewers per channel per second, computed from the per second measurement by Médiamétrie. (See footnote 8.) We assume that a viewer chooses one channel to watch in a given second, but we only observe the average number of viewers per second for each channel in a given month. nomic climate and the generalization of the digital TV technology; month dummies capture the seasonality of TV viewing. The error term ζ i jgt in Equation (1) reects individual deviations from the mean valuation, as a weighted sum of two unobserved variables ε i gt and ε i jt given by:

ζ i jgt = ε i gt + (1 -σ)ε i jt . 23
The parameters of interest to be estimated are α and σ. The parameter α denotes the mean preference of TV viewers for advertising: A positive (negative) value of α suggests that viewers value (disvalue, respectively) advertisements. We let the data decide the sign of α at the estimation stage. Moreover, a statistically signicant α would conrm the two-sided nature of the TV market and is hence a crucial element of our structural estimation.

The parameter σ ∈ [0, 1) denes the substitutability of TV channels belonging to the same group. As σ approaches one, the TV viewers substitute signicantly between channels within the same group g; as σ decreases, the correlation of preferences for channels within the same group decreases. Typically, σ = 0 signies that the TV viewers are equally likely to switch between channels of dierent categories as between channels in the same group.

The mean utility level for the outside good is normalized to 0: δ 0 = 0, and the TV viewers' demand function to be estimated is given by:

(3) ln s jt -ln s 0t = αA jt + σ ln s jt/g + X jt β + ξ jt where s jt , s 0t , and s jt/g denote respectively the market share of channel j, the market shares of the outside goods, and the market share of channel j within its group g. s jt|g = s jt j∈Cg s jt , where C g is the set of TV channels in group g. s 0t = 1j s jt .

From Equation (3), we can derive the number of TV viewers watching channel j, y jt = s jt T t ≡ y jt (A t ), where T t is the market size at time t and A t = 23 The term ε i gt aects the individual i's preferences common to all channels belonging to group g, and the term (1 -σ)ε i jt aects the individual i's preferences specic to channel j. The two terms ε i gt and ε i jt are distributed in such a way that the individual preferences have an extreme value distribution and are allowed to be correlated across channels j. (See McFadden et al. [1978] and [START_REF] Williams | On the formation of travel demand models and economic evaluation measures of user benet[END_REF].) {A 1t , . . . , A jt , . . . , A Jt } is the vector of amounts of advertising for each channel.

Identication

Since the error term ξ jt might include the unobserved programming quality of channel j that might be correlated with the conditional market share s jt/g and the amount of advertising A jt , Equation (3) entails two identication problems.

We use the following BLP-style instrumental variables to address above endogeneity issues: monthly broadcasting hours of news and entertainment of all competing channels, as well as monthly broadcasting hours of news and entertainment of all competing channels in a group (incumbent or entrant). Note that the channels which share a common ownership with the instrumented channel are not considered as competing channels in our IV construction.

The validity of this set of instruments relies on an assumption commonly used in the literature of logit-type demand estimation: Channels do not observe the error term ξ jt until after they have selected the content X jt ; therefore, content selection does not depend on the unobserved characteristics included in ξ jt . 24 This timing is realistic and based on facts. In general, TV channels obtain the broadcasting rights of a show via a contract with its producer, which requires a commitment to broadcast even when the program is still at the production stage. Experts in the industry have conrmed to us that broadcasting content is decided at least three months prior to the broadcasting time.

III(ii).

Advertisers' Demand

The literature on two-sided media markets has modelled advertisers' demand by their inverse demand curve relating the price of advertising to the amount of advertising and size of the audience. (See, among others, [START_REF] Rysman | Competition between networks: A study of the market for yellow pages[END_REF]; [START_REF] Argentesi | Estimating market power in a two-sided market: The case of newspapers[END_REF]; [START_REF] Wilbur | A two-sided, empirical model of television advertising and viewing markets[END_REF]; [START_REF] Fan | Ownership consolidation and product characteristics: A study of the US daily newspaper market[END_REF]; [START_REF] Berry | Optimal product variety in radio markets[END_REF].) While this approach can provide the elasticity of advertisers' inverse demand, it does not explicitly model the substitutability or complementarity between the advertising slots of dierent TV channels. Since cross-substitution of channels by advertisers can incentivize the ASHs to increase advertising prices via a merger, ignoring the substitutability and/or complementarity between the advertising slots of the TV channels could bias the results of the merger analysis that we carry out below. We propose a demand model which takes into account the advertisers' multihoming behavior and allows us to estimate, from the aggregated advertising data, the cross-elasticities between the advertising slots of dierent TV channels.

To reach a wide range of TV viewers, advertisers place the same advertisements on dierent TV channels. The advertisers book an amount of advertising A jt from channel j and expect it could reach out to a minimum number of viewers y jt . This activity comes at a cost. Let p jt denote the price per second of advertising that the ASHs charge to advertisers. A representative advertiser's problem consists of choosing the vector of amounts of advertising A t = (A 1t , ..., A Jt ) that minimizes the total costs C t of achieving the desired overall reach of audience, Y t = J j y jt . At equilibrium, the desired number of viewers corresponds to the number of viewers that we observe. The underlying timing assumption is that the unobservable demand shocks are realized after the content decisions but before the decisions on the amount of advertising. 25,26 The associated cost function of the advertiser is dened as:

(4) C t = C(p t , Y t ) = min A 1t ,...,A Jt j p jt × A jt |F(A 1t , ..., A Jt ) ≥ Y t
where p t = (p 1t , ..., p Jt ) and F(A 1t , ..., A Jt ) is the production function of the repre- 25 It is also consistent with the fact that a contract between an advertiser and an ASH often species the cost of an advertising campaign to be a function of the eective amount of advertising seconds which, in turn, determines the overall reach in audience. Note that the property of linear homogeneity in input prices implies that changing the production scale does not impact the degree of substitution between the dierent input factors. So this assumption has no consequences for the estimates of demand elasticities. sentative advertiser. 27 In practice, TV channels generate the number of viewers for advertisers, and y jt = f (A t , q jt ) is specied by the viewers demand model.

We assume that this cost function can be approximated by a translog exible form as: 28

ln C t = γ 0 + γ y ln Y t + 1 2 γ yy (ln Y t ) 2 + J j γ j ln p jt + 1 2 J i J j γ ij (ln p it )(ln p jt ) + J j θ j (ln Y t )(ln p jt ) + J j ξ A jt ln p jt (5)
where ξ A jt denotes the error term. Taking the derivative of Equation ( 5) with respect to ln p jt and applying Shephard's lemma yields the cost share equations for each TV channel j:

29 (6) S A jt = γ j + J i γ ij (ln p it ) + θ j (ln Y t ) + ξ A jt
To satisfy the adding-up, homogeneity and symmetry conditions of the duality theory, the parameters of the Translog cost function must satisfy the following constraints: 30

(7)

J j γ j = 1 and J j γ ij = J i γ ij = J j θ j = 0 and γ ij = γ ji
Solving the system of Equations ( 6) yields the vector of advertising prices p jt ≡ 27 Researchers in this literature sometimes use price per viewer as a measure of advertising price. We have chosen to use the price per second here because it is what the professionals in the TV advertising market use to talk about the cost of an advertising campaign. Indeed, the contracts between advertisers and the ASHs specify the number of minutes booked for an advertisement on a channel j and its price, which depends a lot on the broadcasting time and network of the advertisement. In practice, there is no specic price per viewer, because audiences dier during a day and on dierent networks. 28 The translog cost function is exible in the sense that it does not impose restrictions on the substitutability or complementarity between the input factors (A it and A jt , ∀i = j). (See [START_REF] Berndt | The Practice of Econometrics: Classic and Contemporary[END_REF]; Christensen et al. [1973].) 29 That is to say: 30 Since each cost share is the proportion of the total advertising cost spent on that input, the cost shares of the dierent TV stations must sum to one. This condition implies that the intercepts of the cost share equations must sum to one, while both the row and column coecients must sum to zero. In addition, imposing the symmetry γ ij = γ ji guarantees the integrability of the demand function of the advertisers. (See [START_REF] Hurwicz | On the integrability of demand functions[END_REF].) p jt (A t , Y t ). Given this expression, we can view the Translog cost model as a way to retrieve the relation between the price per second, the amount of advertising, and the number of viewers, which is dened in the contracts between advertisers and ASHs.

S A jt = ∂ ln Ct ∂ ln pjt = pjt∂Ct Ct∂pjt = pjtAjt Ct .
We makes several important assumptions in this model. First, advertisers only care about the number of viewers that they can reach; they do not care about any intensive margin (e.g., hitting one viewer multiple times). Second, advertisers' valuations of viewers are homogeneous (as in [START_REF] Chandra | Targeted advertising: The role of subscriber characteristics in media markets[END_REF], [START_REF] Gentzkow | Competition and ideological diversity: Historical evidence from us newspapers[END_REF]). These are reasonable assumptions as so far the technology of TV advertising does not allow for the broadcasters and the advertisers to discriminate between the viewers. On the other hand, a broadcaster's ability to segment its audience, or be able to attract viewers with similar characteristics, should allow it to charge a higher advertising price. Finally, as noted by a referee, we assume that the parameters of the Translog cost function do not change after the merger of TV channels. 31The own-and cross-price elasticities of the advertiser's demand (E A jj,t , E A ji,t ) can be derived from the Allen partial elasticities of substitution (see [START_REF] Berndt | Technology, prices, and the derived demand for energy[END_REF]):

E A jj,t = ∂A j,t p j,t ∂p j,t A j,t = γ jj /S A j,t + S A j,t -1 (8) E A ji,t = ∂A j,t p i,t ∂p i,t A j,t = γ ij /S A j,t + S A i,t (9)

Identication

We estimate the system of advertising cost share equations, specied by Equations (6), under the constraints in Equations ( 7), using Zellner's iterated seemingly unrelated regression method. To avoid the singularity of the covariance matrix of the advertising cost share system, one share equation must be omitted in the estimation.

We select randomly one channel, channel j, to omit, but recover its parameters, γ j , γ ij and θ j , using the restrictions given by Equations ( 7).

The error term ξ A jt might include unobserved factors revealing the broadcasting quality of channel j in month t, which does not only impact the amount of advertising A jt but also the advertising prices p 1t , ..., p Jt . If this is the case, there would be a problem of endogeneity. We then need instruments for ln p 1t , ..., ln p Jt to estimate the system of advertising cost share equations using the three-stage least squares method. We have compared two sets of instruments for p 1t , ..., p Jt : the broadcasting hours of news and entertainment programs of the competing channels (also used as instruments for A jt in the viewers' demand model) and the twice-lagged logarithm of the price of advertising. 32,33 For each set of instruments, we tested the dierence between the estimates with and without instrumenting for ln p jt using the Hausman test. The testing results do not reject the null assumption that the dierence between the estimates under the two specications (with and without IVs) is not systematic.

In other words, we obtain very similar estimates with and without instrumenting for ln p jt ; thus ln p jt can be treated as exogenous in Equations ( 6).

IV. ESTIMATION RESULTS

IV(i).

Viewers' Demand

The estimation results for Equation (3) are presented in Table X in Appendix below.

Both the coecient associated with the amount of advertising, α, and the one associated with the within-nest shares, σ, are signicant at the one percent level. Since α < 0, an increase in the amount of advertising induces a decrease in the number 32 The assumption on the timing of the decision process supporting the exogeneity of the BLP IVs is similar to the one we use for the identication of viewers' demand in Section III(i): The broadcasting content is selected before the unobserved demand shock to advertisers ξ A jt is realized. We note that, in this model, the error term ξ A jt is realized simultaneously with the determination of the advertising prices p 1t , ..., p Jt . It means that the past advertising prices are chosen before ξ A jt . Any lagged prices should then be exogenous in principle. Here, we reported the results using the second lags of advertising prices, but in practice, we obtain similar results with either rst, second or third lags of advertising prices. 33 The rst stage estimations with each set of IVs are upon requests to authors.

of viewers of the TV channel. This result suggests that, on average, TV viewers are adversely sensitive to the amount of advertising. The estimated σ is signicantly smaller than 1, indicating that there is competition between the incumbents and the new channels, although its value suggests that there is signicant segmentation between the two groups of TV channels (incumbents and entrants). We nd a statistically signicant mean positive eect of news and cartoons on the size of the audience, but a statistically signicant mean negative eect of entertainment on this size. 34 We note here that monthly data are not the best way to study the eects of genre on audience size. TV channels have a strategy of scheduling dierent genres at dierent times of day, but the availability of many genres (in terms of broadcasting hours) does not vary signicantly from one month to another. However, the broadcasting hours of dierent program genres are here covariates that we control for in the viewers' demand model to better identify the disutility of advertising (measured by α) and the segmentation between the incumbent channels and the new entrants

(measured by σ). 35
To validate our choice of instruments, we conducted statistical tests for weak instruments and overidentication of the IV estimations of Equation (3). The results are presented in the bottom of Table X. The StockYogo weak instrument test suggests the instruments are strong, while the Hansen J statistic does not reject the null hypothesis that the instruments are valid at the 10 percent level.

The rst stage regressions are presented in Table XIV in web appendix. The total 34 The latter fact, which looks counterintuitive, can be explained as follows: The entertainment genre includes many unpopular programs that the TV channels use to ll the broadcasting slots during working hours and sleeping time; several high-quality shows belonging to this category are exclusively broadcast by the incumbent channels; their impact on audience size is captured by the nest parameter and the channel xed eect. We could not identify any statistically signicant eect of either TV series/movies or culture/science on the audience size, because the total broadcasting hours of both genres do not vary from one month to another, although their availability (in terms of broadcasting hours) is very channel-specic. In other words, the eects of TV series/movies and culture/science programs on the audience size of a TV channel are absorbed by the channel xed eect in the monthly data. We have identied a positive eect of sports on the audience size, although the parameter is not signicant at usual levels. This is because there is an important heterogeneity between the dierent sport events: This genre includes the broadcasting of the Champions League, the Roland Garros tennis tournament, and the Olympic Games, but also many small sports events that are scheduled daily between 00:00 and 06:00.

35 [START_REF] Zhang | Regulating advertising quantity: Is it ecient?[END_REF] provides more detailed comments and more precise estimation of the dierent genre eects on the audience size of a TV channel using hourly data. number of hours of news and entertainment broadcast by all the competing channels can explain the amount of advertising on the instrumented channel. News has a mean positive eect on the size of the audience, while entertainment programs have a mean negative eect on the size of the audience. Accordingly, the amount of advertising of competing channels is higher during news programs, but lower during entertainment programs. The amount of advertising of the instrumented channel is higher when it anticipates more advertising by its rivals, due to their scheduling of more news and/or less entertainment programs. The sum of the broadcasting hours of news and entertainment programs of the competing channels in a group (incumbent, entrant) signicantly explain the logarithm of the conditional market share, ln(s jt/g ). The conditional market share of a channel decreases with the amount of news broadcast by its close competitors (i.e., competing channels in the same group), but increases with the amount of entertainment broadcast by its close competitors. We have also tested whether the estimates in Table X are robust to the choice of instruments, by including additional instrumental variables in the estimation. Such an experiment does not change signicantly the values of the estimates but decreases the associated CraggDonald Wald F statistics. (See Table XVI in web appendix for details.)

To determine whether the instruments used in the estimation are helpful in xing the endogeneity bias, we compare the results from the IV estimation with those from OLS in Table XV in web appendix. We observe that the parameter estimates associated with the amount of advertising and the within-nest share in the viewers' demand function strongly dier under the two types of estimation.

Without controlling for the endogeneity bias, the quantity of advertising reects the quality of TV channel and is estimated to have a positive eect on the audience of the channel. The eect of the disutility of advertising can be isolated from the eect of the quality of the TV channel only if the endogeneity bias is properly controlled for. Moreover, with the nested-logit model specication, the value of σ should be between 0 and 1. This constraint is not satised with OLS.

The own-and cross-elasticities of the viewers' demand with respect to the amount of advertising (E They suggest that a 10 percent increase in advertising time causes a mean audience loss of about 10.55 percent (resp. 1.87 percent) for a private (resp. public) TV station. 36,37 All the estimates of own-demand elasticities are signicant at the 10 percent signicance level. The estimated cross-demand elasticities are very small, suggesting that viewers substitute between channels to a very limited degree. Nevertheless, it is important to note that viewers do switch to other channels following an increase in the amount of advertising, although the estimated substitution eects are very small. This is true notably for channels TF1, NT1 and TMC, which suggests that these three channels do have an incentive to merge their ASHs in order to internalize the competition between them for the audience. However, given the weak substitution eects of advertising, we should not expect an important change in the amount of advertising following the merger of their ASHs.

IV(ii).

Advertisers' Demand

The estimates of γ ij in the cost share Equations ( 6) are presented in Table XI in Appendix below. All the estimates of γ jj , ∀j are positive and statistically signicant. 38 Many of the γ ij , i = j are estimated to be close to zero.

Using these estimates, we compute the own-and cross-price elasticities of advertisers' demand for viewers (E A jj,t and E A ji,t ) according to Equations ( 8) and ( 9). The 36 The substantial dierence between the estimated elasticities for public and private TV stations must be related to the large dierence in their advertising levels. Because of the ban on commercial advertising on public TV stations during the prime times, the monthly total amounts of advertising for the public TV stations are much smaller than for the private ones, although none of them hit the maximum minutes of advertising per month allowed legally. 37 We nd smaller own-demand elasticities than previous research using US data: [START_REF] Wilbur | A two-sided, empirical model of television advertising and viewing markets[END_REF] nds that a 10 percent rise in advertising time causes a median 25 percent audience loss for highly rated TV networks, and larger percentage audience losses for low-rated networks; Using improved audience measurement, Wilbur, Goeree and Ridder [2009] nd a median audience loss of about 15 percent in response to a 10 percent increase in advertising time. The dierence between our estimates and the ndings in Wilbur et al. [2009] could be explained by the much more intensive TV advertising in the US. 38 Note that a positive sign of γ jj does not imply positive demand elasticity. (See Equations ( 8) and ( 9) in Section III(ii).) The estimates of elasticities are averaged over the entire period of observation. Own-price elasticities are in bold. Standard errors of these average estimates of elasticities computed by the delta method are in parentheses.

to Table XVII, the estimates of own-price elasticities are statistically signicant for all channels. They show that the advertisers' demand for viewers is relatively price inelastic since all cross-price elasticities are smaller than one in absolute value.

Concerning the cross-price elasticities, we observe that inter-channel substitutability coexists with complementarity. Out of the 132 average cross-price elasticities, 72 (i.e., 55 percent) indicate that channels are substitutes. The precision of the estimates tends to be slightly greater for substitutes than for complements: 53 percent of the positive average cross-price elasticities are statistically signicant, while this percentage is 51 percent for the negative average cross-price elasticities. The magnitude of cross-price eects, whether they identify substitutes or complements, tends to be limited. The median value of the 67 positive average cross-price elasticities is 0.11 (with a range between 8 × 10 -4 and 0.83). The median value of the 54 negative cross-price elasticities is -0.10 (with a range between -0.54 and -3 × 10 -3 ).

The cross-price elasticities presented in Table VI suggest that the advertisers consider channels NT1 and TMC as substitutes, but both as complements of channel TF1. The result of complementarity can be explained as follows. First, NT1 and TMC had a programming (and therefore an audience) structure close to that of channel TF1 even before the acquisition. For instance, the programs attracting the best audience on these two small channels were ctional series and movies, as on channel TF1. Second, when TF1 Group took over the broadcasting services of the two channels in 2010, it introduced more news programs on NT1 and TMC. We can observe that after the acquisition NT1 broadcast six times more news programs and TMC broadcast three times more news programs than before. This has de facto reinforced the similarity of these channels' programs and audiences. It means that the advertisers on channel TF1 can consider the advertising slots of NT1 or TMC as additions to those of channel TF1, especially when these advertisers target an audience coverage that exceeds the capacity of channel TF1. In these conditions, a change in the price per second of advertising on channel TF1 should aect the demand for advertising slots of NT1/TMC in the same way as it aects the demand for the advertising slots of channel TF1. 39 Given this demand pattern of advertisers, we conjecture that a merger between the ASH of channel TF1 with the ASH of channels NT1 and TMC would not lead to a signicant increase in their advertising prices. Indeed, if the merged ASH increased 39 More generally, the complementarity between the dierent advertising platforms could be explained by a phenomenon that is common knowledge in marketing literature: Repeated exposure to the same advertising message increases the eciency of advertising (see [START_REF] Schmidt | Advertising repetition: A meta-analysis on eective frequency in advertising[END_REF]). Advertisers might value the fact that a viewer sees the same advertising message when he/she switches from one channel to the other. This implies that TV networks with a similar audience structure are likely to be complementary for advertisers.

the price of advertising on channel TF1, the marginal advertisers would not switch to NT1 or TMC but to the other channels that are substitutes for channel TF1.

V. MERGER EVALUATION

In January 2010, the Autorité de la concurrence (the French competition authority) cleared the acquisition of two free broadcast TV channels, NT1 and TMC, by the TF1 Group, subject to a behavioral remedy requiring that channels NT1 and TMC sell their advertising time separately from the main channel of TF1 Group, namely, channel TF1. In practice, the decision prohibits the merger between the ASH of channel TF1 and that of channels NT1 and TMC; only the broadcasting content of the three channels is allowed to be managed jointly following the acquisition.

The competition authority had concluded that the acquisition would have a positive impact on the broadcasting side, since channels NT1 and TMC could benet from the large catalog of programs of TF1 Group, which is due to its partnership with numerous content providers: Having more channels oering high-quality content could enhance the competition between the dierent TV broadcasters for audience. 40 The authority was, however, concerned about the potential anti-competitive effects of merging the ASHs of the three channels, due to the dominant position of TF1

Group in the TV advertising market. Before the acquisition, the ASH of channel TF1 held a 40 percent share in the TV advertising market, while the ASH of NT1

and TMC held a ve percent share. The merger could simply reinforce the position of TF1 Group in the TV advertising market, which would translate into an increase in either the amount of advertising or its price. 41 To avoid any detrimental eect of the acquisition on the TV advertising market, the authority decided to impose a 40 While NT1 and TMC are growing very fast as new entrants to the market, their catalogs of broadcasting programs are not as rich as the catalogs of the incumbent channels like TF1. 41 In its decision, the French competition authority states that the TV advertising market is a separate market from the other media advertising channels (radio, print media, and internet), because of their specic pricing, population coverage rate, and costs for the advertisers. Note that the ASH of channel TF1 is no longer dominant if we consider the whole advertising market including all types of media. TF1 Group made use of this argument when proposing the merger between the ASH of channel TF1 with that of NT1 and TMC. However, it was rejected by the French competition authority. behavioral remedy, which prohabits the joint sales of advertising time of the three channels, for a period of at least ve years. 42Below, we rst evaluate the aftermath of the approved acquisition; we then comment on the eectiveness of the implemented behavioral remedy. We start by explaining why it is crucial to account for the interaction between the two sides of the market by exploiting the strategic decisions of the ASHs at equilibrium. In more detail, we estimate the post-acquisition changes in broadcasting quality of the three merging channels using our viewers' demand model; to highlight how the changes in quality of broadcasting impacts the equilibrium amounts and prices of advertising, we then counterfactually simulate the eects of the acquisition in the hypothetical case in which there are no post-acquisition changes in the broadcasting quality of the three merging channels. To break down the direct impact of the changes in broadcasting quality and the eect of two-sided network externalities, we also counterfactually simulate the eects of the acquisition in the absence of the two-sided network externalities between viewers and advertisers. To comment on the eectiveness of the implemented remedy, we simulate the advertising market equilibrium for the case in which the three merging channels sell their advertising time via a common ASH. Finally, we conclude with a welfare analysis to assess the overall impacts of the decision of the French competition authority.

V(i).

Market Equilibrium

We now write down the prot maximization problem of ASHs, which we use to perform dierent counterfactual simulations. The prot of an ASH depends on the demands of viewers and advertisers and on the feedback loop between these two groups of consumers. Each ASH maximizes the joint prot from the advertising slots of all the channels under its management. Formally, the prot function of an ASH H , in month t is given by: (10)

Π kt = j∈H Π jt = j∈H (p jt -c jt ) A jt
where c jt is the marginal cost of commercializing one second of advertising on channel j in month t for the ASH H . 43 At equilibrium, the amount of advertising is the variable which links both sides of the market: It has an impact on both the number of viewers and on the advertising prices of the TV channels. An ASH internalizes the network externalities between viewers and advertisers by choosing the amount of advertising which maximizes its prots. Using the notation introduced in Section III(i) where we write down the number of viewers y jt of channel j as a function of the amounts of advertising of dierent TV channels A t : y jt ≡ y jt (A t ), and the notation introduced in Section III(ii) where we write down the price of advertising p jt of channel j as a function of the amounts of advertising of dierent channels A t and of the total number of TV viewers Y t = J j y jt : p jt ≡ p jt A t , Y t y 1t (A t ), ..., y Jt (A t ) , the objective of an ASH H at equilibrium can be written as:

max {A jt } j∈H j∈H p jt A t , Y t y 1t (A t ), ..., y Jt (A t ) -c jt A jt
Assuming that a pure strategy Nash equilibrium in the amount of advertising exists, and omitting the time index t for the sake of clarity, the rst-order conditions (FOCs) associated with the above prot-maximization problem are:

(11) (p j -c j ) + k∈H A k ( ∂p k ∂A j + ∂p k ∂Y i,∀i ∂Y ∂y i ∂y i ∂A j ) = 0, ∀j
where ∂p k ∂A j measures the impact of the amount of advertising of channel j on the 43 A referee has pointed out that we study here a static short-run equilibrium in which content decisions are xed; in other words, we do not consider the dynamic equilibrium in which TV stations strategically select the content. Endogenizing the content choice of TV stations is a stimulating avenue that we have left for future research. Nonetheless, we note that the following merger evaluation uses ex-post data which takes into account the post-merger changes in content quality.

price of advertising seconds of channel k, ∂p k ∂Y ∂Y ∂y i measures the impact of the number of viewers of channel i on the price of the advertising seconds of channel k, and ∂y i ∂A j is the impact of the amount of advertising of channel j on the number of viewers of channel i.

The above FOCs suggest that each ASH H trades o between three eects when determining the amount of advertising A j of channel j at equilibrium: rst, the impact of A j on the price of advertising p j of channel j and on the price of advertising of the other channels p k managed by the same ASH H , through the term ∂p k ∂A j , ∀j ∈ H ; second, the impact of A j on the number of viewers of dierent TV channels y i through the term ∂y i ∂A j , ∀i; lastly, the impact of the number of viewers of dierent TV channels y i , ∀i on the price of advertising of each channel p k of the ASH H , through the term ∂p k ∂Y ∂Y ∂y i , ∀k ∈ H . Using the estimated preference parameters on the viewing and advertising sides (α, σ, γ j , γ ij , θ j ), and the observed amounts and prices of advertising (A jt and p jt ), we can solve Equations ( 11) for the marginal costs c jt .

In what follows, we make use of Equations ( 11), the estimated preference parameters, and the estimated marginal costs: rst, to show the impact of changes in broadcasting quality on the equilibrium amounts and prices of advertising; then, to comment on the role of two-sided network externalities; nally, to evaluate the eectiveness of the behavioral remedy imposed by the French competition authority.

V(ii).

Impact of Changes in Broadcasting Quality

In this section, we rst estimate the post-acquisition changes in the broadcasting quality of channels TF1, NT1 and TMC, using observed data. We next simulate the equilibrium amounts and prices of advertising in the absence of these changes, which allows us to assess if changes in broadcasting quality can either reinforce or oset the merger eects on the advertising side. Panels 1 and 2 of Table VII summarize the dierent results discussed in this section.

We estimate the TV channels' broadcasting quality from our nested-logit model of viewers' demand. Formally, according to the TV viewer's utility function (Equation (1)), the mean quality of channel j at time t can be measured by q jt (Equation (2)) and is denoted as qjt . The estimated percentage changes in qjt post- acquisition are presented in the rst row of Table VII for each of the three merging channels. 44 As expected, we nd a clear increase in the broadcasting quality of the two purchased channels (NT1 and TMC) after the acquisition. The estimated broadcasting quality of channel TF1 has increased as well but much less signicantly than for the two purchased channels. Taken all together, the average quality of the three merging channels has increased by 14.57% post-acquisition. 45 Accompanied with the increase in broadcasting quality of the merging channels, we also observe a signicant increase in their amounts and prices of advertising. (See Panel 1 of Table VII.) We could explain these rather non-standard eects of acquisition by the joint impact of the changes in broadcasting quality of the merging channels and the two-sided network externalities between viewers and advertisers.

To highlight the quality eects, we here simulate the equilibrium amounts and prices of advertising post-acquisition (i.e., their average levels between 2011 and 2013) for the case in which the broadcasting qualities of the merging channels remain at their levels before the acquisition (i.e., in 2009). 46 We then compute the post- 44 The post-acquisition changes presented in Table VII compare the average levels of qjt , A jt , p jt before the acquisition in 2009 to their respective average levels after the acquisition between 2011 and 2013. We exclude the year 2008 in the estimation of pre-acquisition levels of the three variables, because data on the rst two months of 2008 are missing from the sample. However, we have checked that the average levels of qjt , A jt , p jt beween March 2008 and December 2008 are close to their average levels between March 2009 and December 2009, which means that the data in 2009 approximates well the pre-acquisition levels of these three variables. We estimate the average levels of qjt , A jt , p jt post-acquisition from three years of post-acquisition data, i.e., 2011-2013. We excluded the year 2010 because it was a transition period. 45 In practice, thanks to the merger, both NT1 and TMC obtained the rights to broadcast some attractive programs that might otherwise have been only scheduled on channel TF1. For instance, in 2011, NT1 started to broadcast some popular foreign series, such as `True Blood' and `Falling Skies', and started to oer a new culture program, `Tous Diérents', which is fully produced by TF1 Group and has a signicant audience. TMC obtained the broadcasting rights to the marriage of Prince Albert II of Monaco in July 2011, and for the movie `Bodyguard' in February 2012, in tribute to the deceased singer Whitney Houston. In these two examples, TMC beneted from the broadcasting rights acquired by TF1 Group. These two broadcasts generated signicant peaks in audience for TMC. Moreover, prior to the acquisition, channels NT1 and TMC had hardly any news programs. Since TF1 Group took over their broadcasting services, it has introduced six times more news programs on NT1 and three times more news programs on TMC, so that they now oer the most news programs among the seven new entrants to the market. acquisition changes in the merging channels' amounts and prices of advertising based on the simulated equilibrium, as presented in Panel 2 of Table VII.

The counterfactual simulation is performed according to the algorithm detailed in web appendix B.1. The equilibrium choices of the amounts of advertising by the ASHs of the 12 TV stations are simulated simultaneously. Our simulation procedure considers the strategic reactions between the ASHs of dierent TV channels. For instance, the ASH of channel TF1 acknowledges that its amount of advertising impacts not only the number of viewers of channel TF1 but also the number of viewers of the other channels, all of which have an impact on the price of advertising of channel TF1 at equilibrium.

Comparing the results presented in Panels 1 and 2 of Table VII allows us to draw conclusions about the impact of merging the broadcasting side of the market on the advertising side. We note that the post-acquisition changes in the merging channels' amounts and prices of advertising are mainly due to the changes in their broadcasting quality, as their levels would remain almost unchanged post-acquisition in the absence of the changes in their broadcasting quality. We hence establish clear evidence that merging one side of the market has signicant impacts on the other side in a two-sided market.

V(iii).

The Role of Two-sided Network Externalities

We now discuss the role of two-sided network externalities. To do so, we simulate the post-acquisition changes in the amounts and prices of advertising if viewers were not negatively impacted by the amount of advertising. Panel 3 of Table VII presents the results.

The counterfactual simulation is performed according to the algorithm detailed in web appendix B.2. In the simulation, we keep the broadcasting quality of the dierent TV channels at the same level as in the observed equilibrium (both before and after the acquisition), so that the simulated results are directly comparable to the observed eects of the acquisition in Panel 1.

are set at their levels before the acquisition (i.e., in 2009).

Comparing the simulated acquisition eects in Panel 3 to the observed acquisition eects in Panel 1, we can draw three conclusions. First, in the absence of the negative externalities that advertisers generate for viewers, the ASHs respond to the increase in advertisers' willingness to pay for the advertising slots of the merging channels (as a result of the increase in their broadcasting quality and therefore their number of viewers) by restricting the total amount of advertising slots on the merging channels and thereby increasing their prices. Second, the negative externalities that advertisers generate for viewers incentivize the ASHs to increase the amount of advertising following an increase in TV channels' broadcasting quality, as indicated by the dierence between the amount of advertising (A jt ) in Panel 1 and Panel 3.

Lastly, the joint eect of the two-sided network externalities and the changes in the broadcasting quality of the three merging channels is that both the amounts and prices of advertising of the three merging channels are increased, as presented in Panel 1. 47 We could explain the above ndings using the FOCs derived in Section V(i), namely, Equations ( 11). The broadcasting quality q j of channel j, ∀j, aects the amount of advertising A j in two ways: rst, via its impact on viewers' demand elasticity with respect to A j , and second, through its impact on the exibility of advertising prices with respect to A j . Specically, the derivatives ∂y i ∂A j in Equations (11) depend on q j according to the nested logit model for viewers' demand in Section III(i); the derivatives ∂p k ∂A j in Equations ( 11) depend on q j , because the prices of advertising p k , ∀k, are functions of the number of viewers of dierent TV channels according to the model for advertisers' demand in Section III(ii).

Intuitively, improving the broadcasting quality of a TV channel incentivizes its ASH to choose a higher amount of advertising, as it reduces the TV viewers' demand elasticity with respect to the amount of advertising of this channel: The value of 47 Note that the advertising level in a given period should be higher when viewers do not care about the amount of advertising than when they do. The simulated total amount of advertising is 7.65 percent higher under the assumption that viewers do not care about the amount of advertising than in the observed equilibrium where viewers do care. The negative values in the rst row of Panel 3 in Table VII are changes in the amounts of advertising post-acquisition, capturing the eects of the change in broadcasting quality of the merging channels in the absence of the negative externalities that advertisers generate for viewers. d ∂y j ∂A j /dq j is always negative. However, there may be an osetting incentive for the ASH to reduce the amount of advertising but increase its price instead, due to the increase in the exibility of the price of advertising with respect to its amount: The value of d ∂p j ∂A j /dq j can be either positive or negative in practice. The rst incentive comes from the negative externalities that the advertisers generate for the viewers.

The second incentive is the direct eect of changes in the broadcasting quality of a TV channel on its amount and price of advertising (in the absence of two-sided network externalities between viewers and advertisers). Our post-acquisition data suggest that the joint eect of the two-sided network externalities and the changes in broadcasting quality of the three merging channels results in an increase in both the amounts and prices of advertising of the three merging channels at equilibrium. V(iv).

Eectiveness of the Behavioral Remedy

In this section, we comment on the eects of the behavioral remedy imposed as a counterpart to the approval of the acquisition of channels NT1 and TMC by the TF1 Group. Since our sample covers the post-acquisition period, we observe a partial merger outcome in which the broadcasting side of the market is consolidated, while the advertising side is not due to the behavioral remedy. One practical way to assess the eectiveness of this remedy is to simulate the post-acquisition market equilibrium for the case in which the ASHs of the three channels are merged postacquisition. As we observe the ex-post quality adjustment of dierent TV channels, our counterfactual simulation (using post-acquisition data between 2011 and 2013) takes into account the merger eect on product quality.

The simulation algorithm is the one detailed in web appendix B.1. Panel 4 of Table VII presents the simulated impacts of the acquisition in the absence of the behavioral remedy. Here, we take the simulated equilibrium post-acquisition in which a common ASH manages the advertising slots of the three channels and precisely compare it to the observed equilibrium pre-acquisition in which the ASH of NT1 and TMC is separated from that of channel TF1. 48Comparing the results presented in Panel 4 to those in Panel 1 of Table VII, we can conclude that the implemented behavioral remedy, blocking the merger of the ASHs of the three channels, has very limited impacts at equilibrium. It has only slightly reduced the increase in the total amounts of advertising post-acquisition, and has almost no eect on the market equilibrium levels of advertising prices. In Panel 1 of Table XX in web appendix, we report the exact changes in the amounts and prices of advertising caused by the merger of ASHs. Here, we compare the simulated post-acquisition equilibrium in the absence of the remedy to the observed post-acquisition equilibrium under the remedy. We nd that merging the ASHs of the three channels would increase their total amount of advertising by about 3 percent and decrease their average advertising price by less than 1 percent. This result is not surprising, provided that the substitution eects of the amount of advertising on the viewers' side are small, and that the advertisers consider the advertising slots of NT1 and of TMC to be complementary to those of channel TF1. (See the demand elasticities presented in Section IV(i) and Section IV(ii).) Nonetheless, it is important to understand the role of complementarity in the simulated impacts of a merger of ASHs. We therefore conduct an additional simulation to separate the substitution eects of the amounts of advertising on the viewers' side from the complementary and substitution eects of prices on the advertising side. This simulation is performed using the procedure detailed in web appendix B.2, under the assumption that a common ASH determines the prot-maximizing amounts of advertising for the three channels. Panel 2 of Table XX in web appendix presents the results of this counterfactual exercise, which exhibits the eects of a hypothetical merger of complementary rms. We show that the prices for the complementary products (advertising slots) are lower under joint ownership rather than independent ownership, which is consistent with a well-known nding of [START_REF] Economides | Competition and integration among complements, and network market structure[END_REF]. When viewers are negatively impacted by the amount of advertising, the merged ASH internalizes the substitution eects on the viewers' side, in addition to the complementary and substitution eects on the advertising side. This explains levels imposed by the regulator. competition authority expected the merger decision could improve the broadcasting quality of the two acquired channels (NT1 and TMC), an objective which has been reached post-acquisition (see the results presented in Section V(ii)). We must then establish whether the decision ultimately increased the surplus of TV viewers: Is the benet of the increase in broadcasting quality suciently large to outweigh the associated detrimental eects due to the increase in the total amount of advertising?

The complete estimates of the welfare eects of the acquisition under the behavioral remedy are presented in Panel 1 of Table VIII. We note that the total surplus of TV viewers has decreased post-acquisition. It means that the positive eects of the increase in broadcasting quality on the viewers' surplus were not suciently large to outweigh the associated detrimental eects of the increase in the amount of advertising. This result does not support the competition authority's decision, as the approved merger harms the surplus of TV viewers. Besides, the total costs of advertisements have increased, since both the market average amount and price of advertising have increased. However, the advertising prot of TF1 Group and the other TV groups have all increased following the concentration of the TV market.

We can therefore conclude that the approved merger harms consumers (both viewers and advertisers) but benets the TV stations. In compare the observed welfare changes post-acquisition (i.e., the results presented in Panel 1 of Table VIII) to the simulated welfare changes for the case in which the broadcasting qualities of the merging channels remain at their pre-acquisition levels. We note that the approved merger would not signicantly damage the surplus of consumers (viewers and advertisers) if there are no ex-post changes in the broadcasting quality. This is because the increase in the total amount of advertising, which negatively impacted the consumer surplus post-acquisition, is mainly caused by the increase in the broadcasting quality of the three merging channels. (See the discussion in Section V(ii).) This result is useful from a competition policy perspective. We show that the post-merger changes in product quality impact the market competition. In the context of a two-sided market in particular, the negative network externalities that one side of consumers generate for the other side incentivize the platforms to increase the prices for both sides of the market, in response to the increase in product quality. 50Welfare Eect of the Behavioral Remedy

The behavioral remedy was implemented to prevent the anti-competitive eects that a common ASH for the three merging channels could potentially cause in the TV advertising market. However, our analysis in Section V(iv) shows that the remedy, which blocked a merger between two complementary rms, had very limited impact on the market equilibrium amounts and prices of advertising. In Panel 2 of Table VIII, we present the estimated welfare eects of the acquisition in the absence of this remedy. Comparing the numbers in Panel 2 to those in Panel 1, we can see that the welfare outcomes of the remedy are relatively small.

In Table XXII in web appendix, we report the ex-post welfare changes induced by the merger of the ASHs of the three channels. There, we compare the simulated postacquisition equilibrium in the absence of the remedy to the observed post-acquisition equilibrium under the remedy. We nd that merging the ASHs of the three channels reduces the surplus of TV viewers by less than 1 percent and increases the total cost of advertisers by 4.08 percent. As expected, a concentration of the advertising market increases the prots of both the merging rms (i.e., the ASH of TF1 Group)

and the non-merging rms (i.e., the ASHs of the other TV groups). More precisely, the simulated merger increases the advertising prot of TF1 Group by 3.86 percent and the total advertising prots of other TV groups by 6.84 percent. Given the small impact of this merger on the surplus of TV viewers and considering its benets to the broadcast TV stations, we conclude that the implemented behavioral remedy is not eective when the broadcasting side of the three channels is consolidated.

VI. CONCLUSION

This paper contributes to the analysis of mergers in two-sided markets by assessing a decision of the French competition authority, which approved the merger of the broadcasting services of three TV channels but has de facto prohibited the merger of their ASHs via a behavioral remedy.

To do so, we rst build a structural model which accounts for the multi-homing behavior of advertisers. We then estimate the demands of viewers and advertisers using a comprehensive data set. This step allows us to conrm that one should consider TV channels as two-sided market platforms since we show that advertising has signicant negative externalities on TV viewers. Using ex-post data, we evaluate the consequences of the acquisition. We show that the acquisition had a positive eect on the broadcasting qualities of merging channels; however, merging the broadcasting services of TV channels impacts their equilibrium amounts and prices of advertising.

Based on a counterfactual analysis, we show that the observed changes in the amounts and prices of advertising post-acquisition are mainly due to the changes in the merging channels' broadcasting quality. An additional counterfactual simulation of the market equilibrium in the absence of the negative externalities that advertisers generate for viewers shows how the ASHs respond to the increase in advertisers' willingness to pay (as a result of the increase in the merging channels' broadcasting quality and therefore their viewership) by restricting the total amount of advertising slots on the merging channels and thereby increasing their prices.

We further show that the negative externalities that advertisers generate for viewers incentivize the ASHs to increase the amounts of advertising of the merging channels as well (as a result of the increase in their broadcasting quality) since viewers are less sensitive to the amounts of advertising during programs of better quality. Regardless of the behavioral remedy aiming at limiting any detrimental eect of the acquisition on the advertising side of the market, the joint eect of the increase in the broadcasting quality of the merging channels and the two-sided network externalities between viewers and advertisers results in an increase in both the amounts and prices of advertising of the merging channels.

Welfare analysis suggests that the positive eects of the increase in broadcasting quality on the viewers' surplus are not suciently large to outweigh the associated detrimental eects of the increase in the amount of advertising. Overall, the approved merger harms consumers (both viewers and advertisers) but benets the TV stations.

To comment on the eectiveness of the behavioral remedy, we counterfactually simulate the acquisition under the assumption that the merger on the advertising side was also approved. Our results suggest that the remedy enforced by the French competition authority did not have any signicant impact at equilibrium. Welfare analysis conrms that the implemented behavioral remedy is not eective, provided that the broadcasting side of the market is already consolidated.

The main lesson of our analysis is that, in the process of designing competition or regulatory policy for two-sided markets, ignoring the interaction between the two sides of platforms can result in unexpected outcomes.

This conclusion is drawn from the study of the digital TV industry. Provided more disaggregated data on audience and advertising were available, further investigation to rene this analysis could be undertaken. We expect our methodology could also be helpful for examining similar markets, especially those in which the usage of services on one side is free and all the revenues come from the other side. ln p T F 1 0.107 -0.023 -0.009 0.025 -0.002 -0.016 -0.014 -0.003 -0.024 -0.014 0.001 -0.029 (0.023) (0.008) (0.007) (0.017) (0.001) (0.005) (0.004) (0.001) (0.006) (0.0007) (0.005) (0.007) ln p F R2 -0.023 0.036 0.002 0.012 0.000 -0.008 -0.010 -0.004 -0.004 0.001 0.001 -0.003 (0.008) (0.005) (0.004) (0.008) (0.001) (0.003) (0.002) (0.001) (0.003) (0.003) (0.002) (0.003) ln p F R3 -0.009 0.002 0.025 -0.012 0.001 -0.002 0.001 0.004 -0.014 0.006 0.003 -0.005 (0.007) (0.004) (0.004) (0.006) (0.001) (0.002) (0.002) (0.001) (0.003) (0.002) (0.002) (0.003) ln p M 6 0.025 0.012 -0.012 0.064 0.000 0.001 -0.001 0.003 0.008 -0.024 -0.009 -0.010 (0.017) (0.008) (0.006) (0.022) (0.001) (0.005) (0.004) (0.001) (0.007) (0.007) (0.005) (0.007) ln p F R5 -0.002 0.000 0.001 0.000 0.004 -0.001 0.006 -0.001 0.000 0.001 0.001 -0.002 (0.001) (0.001) (0.001) (0.001) (0.001) (0.000) (0.001) (0.000) (0.001) (0.000) (0.000) (0.001) ln p D8 -0.016 -0.008 -0.002 0.001 -0.001 0.018 0.000 0.001 -0.003 -0.005 0.007 0.008 (0.005) (0.003) (0.002) (0.005) (0.000) (0.002) (0.001) (0.000) (0.002) (0.002) (0.002) (0.002) ln p N T 1 -0.014 -0.010 0.001 -0.001 0.006 0.000 0.037 -0.000 0.003 -0.004 -0.007 0.000 (0.004) (0.002) (0.002) (0.004) (0.001) (0.001) (0.002) (0.000) (0.002) (0.001) (0.001) (0.002) ln p F R4 -0.003 -0.004 0.004 0.003 -0.001 0.001 -0.000 0.003 0.000 -0.003 -0.001 -0.009 (0.001) (0.001) (0.001) (0.001) (0.000) (0.000) (0.000) (0.000) (0.001) (0.001) (0.000) (0.001) ln p T M C -0.024 -0.004 -0.014 0.008 0.000 -0.003 0.003 0.000 0.042 -0.005 -0.006 0.008 (0.006) (0.003) (0.003) (0.007) (0.001) (0.002) (0.002) (0.001) (0.004) (0.003) (0.002) (0.003) ln p Gulli -0.014 0.001 0.006 -0.024 0.001 -0.005 -0.004 -0.003 -0.005 0.057 -0.003 -0.007 (0.007) (0.003) (0.002) (0.007) (0.000) (0.002) (0.001) (0.001) (0.003) (0.005) (0.002) (0.003) ln p D17 0.001 0.001 0.003 -0.009 0.001 0.007 -0.007 -0.001 -0.006 -0.003 0.019 -0.007 (0.005) (0.002) (0.002) (0.005) (0.000) (0.002) (0.001) (0.000) (0.002) (0.002) (0.002) (0.002) ln p W 9 -0.029 -0.003 -0.005 -0.010 -0.002 0.008 0.000 -0.009 0.008 -0.007 -0.007 0.047 (0.007) (0.003) (0.003) (0.007) (0.001) (0.002) (0.002) (0.001) (0. Computing the degree of freedom equation by equation is incorrect as it would not account for the constraints that we specied in Equation ( 7). Standard errors of estimates are in parentheses.

  de l'audiovisuel (the French audiovisual regulator, CSA herein), has made available to us the monthly audience and advertising data of 12 major broadcast TV stations in France from March 2008 to December 2013. The sample is representative of the French TV market: The total audience share of the 12 stations exceeds 95 percent of the free-broadcast TV market and 79 percent of the whole TV market including pay TV; the total advertising revenue share of the 12 stations exceeds 90 percent of the free-broadcast TV market and 81 percent of the whole TV market. The list of the 12 TV stations is provided in Table I. All 12 TV stations are generalist, broadcasting a wide range of programs. The incumbent channels have been broadcasting since 1950, while the new channels entered the market in 2005.

  have 78 independent parameters, and 69 data points for each of the 11 cost share equations in the estimation, which provides 681 degrees of freedom (69 × 11 -78 = 681).

Table I :

 I List of TV Channels and their Ownership Since 2010

		Channels Nature Media Group membership
	Incumbents	TF1	private TF1 Group
		M6	private M6 Group
		FR2	public FTV Group
		FR3	public FTV Group
		FR5	public FTV Group
	New entrants NT1	private TF1 Group
		TMC	private TF1 Group
		W9	private M6 Group
		FR4	public FTV Group
		D17	private Canal plus Group
		D8	private Canal plus Group
		Gulli	private Lagardère Group
	we derive our monthly measure of audience as a weighted average of viewers per
	second in a month. 8

Table II :

 II Summary Statistics

	Variable		Mean Std. Dev. Min. Max
	Number of TV viewers (in thousands)	3.84	4.14	2.86	16.03
	Number of seconds of advertising (in millions) 0.20	0.09	0.03	0.44
	Advertising price per second (in thousands)	5.96	8.01	0.42	35.95
	Notes: The total number of observations is 840, which amounts to 70 monthly observations per
	TV channel. Source: Médiamétrie & Kantar Media.
	20082013). Summary statistics on the broadcasting of the six program genres are
	provided in Table III. TV series/movies, culture/science, and entertainment are the
	most broadcast genres, which occupy more than 70 percent of the broadcasting time
	of each TV station. The broadcasting of news varies signicantly from channel to
	channel: The new entrants such as Gulli and D17 do not show any news. Sports
	and cartoons occupy relatively small shares of the total broadcasting time of the
	12 generalist TV stations in our sample; in particular, FR5 and TMC do not show
	sports, while D8 and W9 do not show cartoons.
	Table III: Monthly Broadcasting Hours of Dierent Genres
	Genre	Unit Level	Man	Stv.	Min.	Max
	TV Series/Movies hours channel/month 283.85 157.30 16.78	696.00
	Culture/Science	hours channel/month 153.23 156.77	2.70	711.45
	Entertainment	hours channel/month 123.41 137.39	0	692.67
	News	hours channel/month	52.31	53.66	0	214.47
	Sports	hours channel/month	14.31	19.45	0	166.80
	Cartoons	hours channel/month	1.30	2.35	0	21.13
	Source: Médiamétrie
	II(ii).

Table IV :

 IV Eects of the Merger

		Amount of advertising	Advertising price
		coe.	(s.e.)	coe.	(s.e.)
	T F 1 × Acquisition	+29.06%*** (0.099)	+26.77%*** (0.100)
	N T 1 × Acquisition	+49.71%*** (0.115)	+119.44%*** (0.174)
	T M C × Acquisition	+43.761%*** (0.110)	+110.60%*** (0.167)
	Others × Acquisition	+41.039%*** (0.093)	+56.18%*** (0.107)
	Channel FE	Yes		Yes
	Month-Year FE	Yes		Yes
	Notes:

  Their estimates, averaged by channel over the sample periods, are presented in Table XVII in web appendix. The elasticities of channels TF1, NT1 and TMC are extracted from this table and presented in Table V below.

V jj,t , E V ji,t ) follow the classical formula in the nested-logit model. (See, for instance,

[START_REF] Verboven | International price discrimination in the European car market[END_REF]

.)

Table V :

 V Viewers' Elasticities of Demand for Channels TF1, NT1 and TMC E V ji _DG denotes the cross-elasticities between channels of two dierent groups (incumbents and entrants). Standard errors computed by delta method are in parentheses. estimated elasticities, averaged over the whole period of observation, for all channels are presented in Table XVII in web appendix. The elasticities of channels TF1, NT1 and TMC are extracted from this table and presented in Table VI below. Each cell of these two tables display the percentage change in demand that the row channel benets from the column channel's change in its price by one percent. According

		E V jj	E V ji _SG	E V ji _DG
	TF1	-0.120	0.0033	0.00085
		(0.074)	(0.0023)	(0.00024)
	NT1	-0.117	0.0008	0.00006
		(0.072)	(0.0007)	(0.00002)
	TMC	-0.118	0.0015	0.00011
		(0.072)	(0.0013)	(0.00003)
	Notes: E V ji _SG denotes the cross-elasticities between channels within the same group (incumbents
	and entrants);

Table VI :

 VI Advertisers' Elasticities of Demand for Channels TF1, NT1 and TMC

		TF1	NT1	TMC
	TF1	-0.296	-0.035	-0.055
		(0.047)	(0.014)	(0.013)
	NT1	-0.346	-0.290	0.312
		(0.124)	(0.072)	(0.070)
	TMC	-0.485	0.174	-0.094
		(0.133)	(0.053)	(0.032)

Notes:

Table VII :

 VII Observed and Simulated Impacts of the Acquisition

		TF1	NT1	TMC	TF1 Group average
	Panel 1: Observed equilibrium
	Estimated broadcasting quality (q jt )	+4.82% +19.76% +20.40%	+14.57%
	Amount of advertising (A jt )	+4.86% +19.63% +20.28%	+14.52%
	Price of advertising (p jt )	+2.09% +74.15% +40.16%	+8.12%
	Panel 2: Simulated equilibrium in the absence of changes in broadcasting quality
	Counterfactual broadcasting quality (q jt )	+0%	+0%	+0%	+0%
	Amount of advertising (A jt )	+0.09%	+0.19%	+0.22%	+0.16%
	Price of advertising (p jt )	+0.03%	+0.02%	+0.04%	+0.03%
	Panel 3: Simulated equilibrium in the absence of two-sided externalities
	Counterfactual broadcasting quality (q jt ) +4.82% +19.76% +20.40%	+14.57%
	Amount of advertising (A jt )	-16.73% -14.49% -15.76%	-15.66%
	Price of advertising (p jt )	+3.77% +114.05% +81.01%	+14.44%
	Panel 4: Simulated equilibrium in the absence of the behavioral remedy
	Counterfactual broadcasting quality (q jt ) +4.82% +19.76% +20.40%	+14.57%
	Amount of advertising (A jt )	+11.33% +20.07% +22.95%	+17.83%
	Price of advertising (p jt )	+1.41% +70.03% +39.31%	+7.29%
	Notes: The percentage changes compare the observed equilibrium pre-acquisition (i.e, in 2009) to the simulated
	equilibrium post-acquisition (i.e, averaged between 2011 and 2013). The channel-and time-xed eects are controlled
	for in the simulations.

Table VIII :

 VIII Table XXI in web appendix, we Welfare Eects of the Acquisition

	Viewers' surplus Total cost of advertisements Total prot (TF1 Group) Total prot (others)
		Panel 1: Observed equilibrium under the behavioral remedy
	-4.52%	19.52%	23.58%	41.26%
		Panel 2: Simulated equilibrium in the absence of the behavioral remedy
	-5.18%	24.40%	28.35%	50.92%
	Notes: The percentage changes compare the observed equilibirum pre-acquisition (i.e, in 2009) to the simulated
	equilibirum post-acquisition (i.e, averaged between 2011 and 2013)

Table X :

 X Estimates of Viewers' Demand ln s jt -ln s 0t : Standard errors cluster-robust at channel level are in parentheses. ***p <0.01, **p <0.05, *p <0.1

		coe.	(s.e.)
	Amount of advertising (α)	-0.111***	(0.031)
	Within-nest share (σ)	0.636***	(0.192)
	TV Series/Movies	0.003	(0.003)
	Culture/Science	0.001	(0.004)
	News	0.068***	(0.009)
	Entertainment	-0.255***	(0.078)
	Sports	0.011	(0.009)
	Cartoons	0.461**	(0.111)
	Channel FE	Yes
	Month FE	Yes
	Year FE	Yes
	No. observations	840
	CraggDonald Wald F statistic	14.665
	Hansen J statistic (p-value)	0.440

Notes

Table XI :

 XI Cost Share Input Price Coecient Estimates

Note that NT1 and TMC were co-owned by AB Group before being acquired by TF1 Group. The sales of their advertising slots were already managed by one common ASH before TF1 Group acquired it.

More details about the merger decision are provided in Section V of the paper.

We thank the Editor for pointing out the critical role of the substitutability and complemen-

See the Journal 's editorial website for further details.

The reduced form regression suggests an extra ten seconds of advertising leads to thirty less viewers. Note, however, that one should not misinterpret this result as the elasticity of viewing with respect to advertising, which we estimate in Section III(ii) below.

We thank an anonymous referee for suggesting this representation of the data.

In more details, the implemented behavioral remedy prohibits the two ASHs of TF1 Group from cooperating and requires that they develop separate B2B relationships with their advertisers. Joint sales of advertising time on the three channels is forbidden. The remedy also requires the two ASHs to have separate HRs and employees.

See Fan [2013] and[START_REF] Gentzkow | Competition and ideological diversity: Historical evidence from us newspapers[END_REF] for such case documentation.

As noted byBjörnerstedt and Verboven [2015], these regressions can be interpreted as

This categorization of groups is motivated by the following considerations. First, the incumbent channels and new entrants do not have the same brand awareness, simply because they entered the market at dierent times. The incumbent channels have been broadcasting since 1950, while the seven new channels entered the market in 2005. The new entrants also required a new reception technology, which was only adopted gradually by the French households between 2005 and 2013. Second, the broadcasting content on the incumbent channels has a dierent focus and quality from that on the new channels. Although all of the 12 TV stations in our sample show a wide range of genres of programs, the incumbent channels devote relatively more time to news and culture/science, while the new channels show more TV series/movies (see Figure2in web appendix for the distribution of dierent genres of program on incumbent and new entrant channels). The incumbent channels oer better quality of sports events and entertainment programs than the new entrants: Only the incumbent channels can aord the cost of broadcasting popular sports events such as the Champions League, the Olympic Games, and expensive live shows such as The Voice. Third, a French law requires the free-broadcast TV stations to show at least 40 percent of French audiovisual programs per day. The incumbent channels must full this obligation in the

Similar timing assumption is made in, for example,Jeziorski [2014a] and[START_REF] Jeziorski | Estimation of cost eciencies from mergers: application to us radio[END_REF].

Note that we do not consider any outside alternative, i.e., any other media advertising channels, to TV advertising. This assumption is based on the fact that the advertisers often proceed in a multistage budgeting. They rst dene an overall budget for an advertising campaign to promote a specic product, then share this budget among the dierent types of media (TV, print media, social networks, etc.). It does not mean that there is no reallocation of budgets among the dierent media. However, if there is a reallocation, it is unlikely due to the behavior of a dominant media platform in a specic segment of the advertising market, but is more likely linked to the changes in the advertisers' strategy to promote the product. This assumption is also coherent with the market denition that we consider in the analysis of the merger in Section V. See footnote 41 for more details.

We would ideally test if this assumption holds but in reality we do not have enough number of observations to run separately the estimation for the pre-and post-merger periods.

The ASHs of the three channels have remained separate after the eective period of the behavioral remedy, possibly because TF1 Group did not want to encourage increased scrutiny by the competition authority.

Note that we keep the post-acquisition levels of the broadcasting qualities of the non-merging channels as they are; in the simulation, only the broadcasting qualities of the three merging channels

We have carefully checked that the simulated amounts of advertising are below the maximum

Note that Panel 4 of TableVIIshows the overall impacts of the acquisition without the behavioral remedy (i.e., the impact of merging both the broadcasting side and the advertising side of the three channels). Table XX presents the impacts of merging the ASHs of the three channels, holding the broadcasting quality qjt at the post-acquisition level.

Recall that, here, the amount of advertising A jt can be viewed as a price that TV channels charge their viewers.

why we nd a greater increase in the total amount of advertising (thereby a smaller decrease in the advertising prices) when the two complementary ASHs merged at equilibrium in the presence of the negative externalities that advertisers generate for viewers. (See Panels 1 and 2 of Table XX in web appendix.) 49 V(v).

Welfare Eects of the Merger Decision

We now assess the welfare consequences of the merger decision. We rst look at how the approved merger under the behavioral remedy impacts the surplus of TV viewers, the total advertising costs and the advertising prots of dierent TV Groups. Then we evaluate the welfare implications of the behavioral remedy.

The viewers' surplus can be evaluated from our nested-logit model, as in Small and Rosen [1981]:

The advertisers' surplus can be measured by their total cost: C t = j p jt × A jt . The prot of an ASH H is given by Equation ( 10):

Welfare Eects of the Approved Merger

In Section V(ii) and V(iii), our analysis reaches the following conclusions. Merging the broadcasting services of TV channels impacts their equilibrium amounts and prices of advertising. If the joint management of broadcasting services improves the broadcasting quality of the merging channels; by the same token, it increases the value (therefore the prices) of their advertising slots. At equilibrium, the negative externalities that advertisers generate for viewers incentivize the ASHs to increase the amount of advertising following an increase in TV channels' broadcasting quality.

Therefore, prohibiting the merger of ASHs is not enough to keep the equilibrium amounts and prices of advertising at their levels before the acquisition. If the goal was to leave the TV advertising market unchanged, the acquisition -merging the broadcasting services of TV channels -should not have been approved. However, the APPENDIX