diff --git a/res/finder/phd.ttx b/res/finder/phd.ttx new file mode 100644 index 0000000..82e2f4f --- /dev/null +++ b/res/finder/phd.ttx @@ -0,0 +1,2957 @@ +title : ESSAYS ON HORIZONTAL MERGERS AND ANTITRUST +blank : +blank : +blank : +blank : +title : A DISSERTATION + : SUBMITTED TO THE GRADUATE SCHOOL OF BUSINESS + : AND THE COMMITTEE ON GRADUATE STUDIES + : OF STANFORD UNIVERSITY + : IN PARTIAL FULFILLMENT OF THE REQUIREMENTS + : FOR THE DEGREE OF + : DOCTOR OF PHILOSOPHY +blank : +blank : +blank : +blank : +author : Przemyslaw Jeziorski +text : June 2010 + : © 2010 by Przemyslaw Jeziorski. All Rights Reserved. + : Re-distributed by Stanford University under license with the author. +blank : +blank : +blank : +text : This work is licensed under a Creative Commons Attribution- + : Noncommercial 3.0 United States License. + : http://creativecommons.org/licenses/by-nc/3.0/us/ +blank : +blank : +blank : +blank : +text : This dissertation is online at: http://purl.stanford.edu/bb599nz4341 +blank : +blank : +blank : +blank : +footer : ii +text : I certify that I have read this dissertation and that, in my opinion, it is fully adequate + : in scope and quality as a dissertation for the degree of Doctor of Philosophy. +blank : +text : Peter Reiss, Primary Adviser +blank : +blank : +blank : +text : I certify that I have read this dissertation and that, in my opinion, it is fully adequate + : in scope and quality as a dissertation for the degree of Doctor of Philosophy. +blank : +text : Ali Yurukoglu +blank : +blank : +blank : +text : I certify that I have read this dissertation and that, in my opinion, it is fully adequate + : in scope and quality as a dissertation for the degree of Doctor of Philosophy. +blank : +text : C. Lanier Benkard +blank : +blank : +blank : +blank : +text : Approved for the Stanford University Committee on Graduate Studies. + : Patricia J. Gumport, Vice Provost Graduate Education +blank : +blank : +blank : +blank : +text : This signature page was generated electronically upon submission of this dissertation in + : electronic format. An original signed hard copy of the signature page is on file in + : University Archives. +blank : +blank : +blank : +blank : +footer : iii +heading : Abstract +blank : +text : This thesis contributes to understanding the economics of mergers and acquisitions. + : It provides new empirical techniques to study these processes, based on structural, + : game theoretical models. In particular, it makes two main contributions. In Chapter + : 2, I study the issues arising when mergers take place in a two-sided market. In such + : markets, firms face two interrelated demand curves, which complicates the decision + : making process and makes standard merger models inapplicable. In Chapter 3, I + : provide a general framework to identify cost synergies from mergers without using + : cost data. The estimator is based on a dynamic model with endogenous mergers and + : product repositioning. Both chapters contain an abstract model that can be tailored + : to many markets, as well as a specific application to the merger wave in the U.S. + : radio industry. +blank : +blank : +blank : +blank : +footer : iv +heading : Acknowledgments +blank : +text : I would like to thank my advisers Lanier Benkard and Peter Reiss for their guidance + : over the years, their patience and their constant feedback that helped me to consider- + : ably improve my work. Moreover, I would like to express my gratitude to numerous + : people I encountered who believed in me and supported me along my path to this + : degree. In particular, this thesis would have been impossible without my adviser + : Tomasz Szapiro at the Warsaw School of Economics. He motivated me and directly + : helped me to make my adventure in the United States possible. My interest in game + : theory and dynamic models was triggered by great conversations with my adviser + : Rabah Amir at the University of Arizona. I would like to thank him for his support + : and help while applying to Stanford GSB. Last but not least, I am grateful to all the + : community at Stanford University – professors, fellow students and casual friends – + : for creating a unique environment for my intellectual and personal development. +blank : +blank : +blank : +blank : + : v +heading : Contents +blank : +toc : Abstract iv +blank : +toc : Acknowledgments v +blank : +toc : 1 Introduction 1 +blank : +toc : 2 Mergers in two-sided markets: Case of U.S. radio industry 5 + : 2.1 Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 + : 2.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 + : 2.3 Radio as a two-sided market . . . . . . . . . . . . . . . . . . . . . . . 9 + : 2.3.1 Industry setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 + : 2.3.2 Listeners . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 + : 2.3.3 Advertisers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 + : 2.3.4 Radio station owners . . . . . . . . . . . . . . . . . . . . . . . 16 + : 2.4 Data description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 + : 2.5 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 + : 2.5.1 First stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 + : 2.5.2 Second stage . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 + : 2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 + : 2.6.1 Listeners’ demand . . . . . . . . . . . . . . . . . . . . . . . . . 23 + : 2.6.2 Advertisers’ demand . . . . . . . . . . . . . . . . . . . . . . . 23 + : 2.6.3 Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 + : 2.7 Counterfactual experiments . . . . . . . . . . . . . . . . . . . . . . . 29 + : 2.7.1 Impact of mergers on consumer surplus . . . . . . . . . . . . . 29 +blank : +footer : vi +toc : 2.7.2 Effects of product variety and market power . . . . . . . . . . 31 + : 2.8 Robustness analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 + : 2.9 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 +blank : +toc : 3 Estimation of cost synergies from mergers without cost data: Ap- + : plication to U.S. radio 35 + : 3.1 Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 + : 3.2 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 + : 3.3 Merger and repositioning framework . . . . . . . . . . . . . . . . . . 38 + : 3.3.1 Industry basics . . . . . . . . . . . . . . . . . . . . . . . . . . 38 + : 3.3.2 Players’ actions . . . . . . . . . . . . . . . . . . . . . . . . . . 39 + : 3.3.3 Payoffs and equilibrium . . . . . . . . . . . . . . . . . . . . . 41 + : 3.4 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 + : 3.4.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 + : 3.4.2 Policy estimation . . . . . . . . . . . . . . . . . . . . . . . . . 43 + : 3.4.3 Minimum distance estimator . . . . . . . . . . . . . . . . . . . 46 + : 3.5 Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 + : 3.5.1 Industry and data description . . . . . . . . . . . . . . . . . . 48 + : 3.5.2 Static profits . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 + : 3.5.3 Estimation details . . . . . . . . . . . . . . . . . . . . . . . . . 51 + : 3.5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 + : 3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 +blank : +toc : A Additional material to Chapter 2 57 + : A.1 Advertising demand: Micro foundations . . . . . . . . . . . . . . . . . 57 + : A.2 Numerical considerations . . . . . . . . . . . . . . . . . . . . . . . . . 59 +blank : +toc : B Additional material to Chapter 3 61 + : B.1 Estimation without acquisition prices . . . . . . . . . . . . . . . . . . 61 + : B.2 Radio acquisition and format switching algorithms . . . . . . . . . . . 62 + : B.3 Policy function covariates . . . . . . . . . . . . . . . . . . . . . . . . 63 + : B.4 First stage estimates: Dynamic model . . . . . . . . . . . . . . . . . . 65 +blank : +blank : +footer : vii +toc : Bibliography 68 +blank : +blank : +blank : +blank : +footer : viii +heading : List of Tables +blank : +toc : 2.1 Simple example of advertising weights . . . . . . . . . . . . . . . . . . 15 + : 2.2 Panel data descriptive statistics . . . . . . . . . . . . . . . . . . . . . 18 + : 2.3 Estimates of mean and random effects of demand for radio program- + : ming. Stars indicate parameter significance when testing with 0.1, 0.05 + : and 0.01 test sizes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 + : 2.4 Interaction terms between listeners’ demographics and taste for radio + : programming. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 + : 2.5 Product closeness matrices for chosen markets . . . . . . . . . . . . . 26 + : 2.6 Slope of the inverse demand for ads θ2A , by market size . . . . . . . . 27 + : 2.7 Estimated marginal cost (in dollars per minute of broadcasted advertis- + : ing) and profit margins (before subtracting the fixed cost) for a chosen + : set of markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 + : 2.8 Counterfactuals for all markets . . . . . . . . . . . . . . . . . . . . . 29 + : 2.9 Counterfactuals for small markets (less than 500k people) . . . . . . . 30 + : 2.10 Counterfactuals for large markets (more than 2,000k people) . . . . . 30 + : 2.11 Slope of the inverse demand for ads θ2A , by market size . . . . . . . . 33 + : 2.12 Robustness of counterfactuals . . . . . . . . . . . . . . . . . . . . . . 33 +blank : +toc : 3.1 Change in the local ownership caps introduced by the 1996 Telecom Act. 49 + : 3.2 Savings when two stations are owned by the same firm vs. operating + : separately . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 + : 3.3 Total cost savings created by mergers after 1996, compared to demand + : effects from Jeziorski (2010) . . . . . . . . . . . . . . . . . . . . . . . 55 + : 3.4 Format switching cost for chosen markets . . . . . . . . . . . . . . . . 55 +blank : +footer : ix +toc : B.1 Covariates for the format switching strategy multinomial logic regression. 63 + : B.2 Covariates for the purchase strategy logic regression. . . . . . . . . . 64 + : B.3 Station purchase policy estimates - buyer/seller dummies . . . . . . . 65 + : B.4 Station purchase policy estimates - other variables . . . . . . . . . . . 65 + : B.5 Format switching policy estimates - format dynamics . . . . . . . . . 66 + : B.6 Format switching policy estimates - current demographics . . . . . . . 66 + : B.7 Format switching policy estimates - demographic dynamics . . . . . . 67 +blank : +blank : +blank : +blank : +footer : x +heading : List of Figures +blank : +toc : 3.1 Dynamics of station acquisition and format switching . . . . . . . . . 50 +blank : +blank : +blank : +blank : +footer : xi +header : Chapter 1 +blank : +heading : Introduction +blank : +text : A horizontal merger occurs when two or more competing companies combine to jointly + : operate. Both the European Commission (2004) and the U.S. Department of Justice + : (1997) recognize that such mergers may lessen competition and thereby harm con- + : sumers. Therefore, in order to prevent anti-competitive conduct, both bodies employ + : a set of analytical tools that predict and analyze the consequences of mergers. The + : dominant paradigm from the 1950s and through the 1970s was the structure-conduct- + : performance approach (see Bain (1968)). It assumes that market power is directly + : related to market concentration, and proposes using concentration indexes (e.g. the + : Herfindahl-Hirschman Index) for merger enforcement. This approach however, does + : not explicitly explain the conduct of firms and ignores many important issues, for + : example product differentiation, and heterogeneity of consumers or cost synergies. + : In contrast, modern industrial organization has developed new techniques, based on + : game theory, that endogenize the behavior of companies and allow for more detailed + : and robust evaluation of mergers. + : Current analysis of horizontal mergers in markets with differentiated products is + : based on a static supply and demand approach (e.g. Nevo (2000)). It is usually + : done in two steps. In the first step, one estimates a flexible demand system (e.g. + : Deaton and Muellbauer (1980), Ackerberg and Rysman (2005), Berry (1994), Berry, + : Levinsohn, and Pakes (1995)) and supply system. The demand system is a function of + : product characteristics, prices and heterogeneous consumer preferences. The supply +blank : +blank : +footer : 1 +header : CHAPTER 1. INTRODUCTION 2 +blank : +blank : +blank : +text : system is determined by the equilibrium behavior of firms that maximize their profits. + : In the second step, one exogenously imposes a hypothetical merger and solves for the + : post-merger equilibrium using the estimates from the first step. The new equilibrium + : provides predictions about post-merger prices and quantities that can be used to + : identify the short-run impact of the merger on consumer and producer surplus. This + : thesis provides two extensions to this framework. First, it develops a new supply and + : demand system that encompasses the merger analysis of two-sided markets. Second, + : it proposes a dynamic framework in which mergers and product repositioning are + : endogenous. It allows for long-run predictions, including evaluation of possible fixed + : cost synergies of mergers. These methods are applied to analyze the 1996-2006 merger + : wave in the U.S. radio industry. + : In the chapter 2 of this thesis, I focus on how mergers affect two-sided markets. In a + : two-sided market, firms provide services to two types of consumers and facilitate their + : interaction via a platform. This creates cross-consumer externalities; thus, the profits + : of a firm operating a platform depend on sales to both types of consumers. Examples + : of such markets include the following: radio, in which stations sell ads and provide + : programming to listeners; credit cards, in which firms connect merchants and credit + : card holders; operating systems, in which revenue comes from hardware buyers and + : application developers. Antitrust analysis in these markets is complicated and it must + : take into account the market specific economic features (Armstrong (2006), Rochet + : and Tirole (2006), Evans (2002)). In particular, in the case of a merger, a firm has + : incentives to exercise market power on both sides of the market. These incentives are + : often conflicting. For example, in the radio market, stations sell advertising knowing + : it negatively impacts their listenership. On the one hand, a merged firm might sell + : more advertising in order to exercise market power on listeners. On the other hand, it + : might sell less advertising in order to exercise market power on advertisers. Chapter 2 + : investigates this conflict by estimating a model of supply and demand for advertising + : and radio programming. Using this model, it performs counterfactual experiments + : that predict the post-merger advertising quantity supplied and the new division of + : surplus between listeners and advertisers. I find that mergers decrease the amount of + : advertising supplied, thereby increasing listener welfare by 1%. However, at the same +header : CHAPTER 1. INTRODUCTION 3 +blank : +blank : +blank : +text : time the decrease in ad supply raises prices and lowers advertiser welfare by $300m + : per year. + : A static analysis does not recognize that firms may adjust their product portfolio + : after a merger. In theory, mergers could increase or decrease product variety. On + : the one hand, they can increase the variety because a merged firm wants to avoid + : cannibalization. On the other hand, the firm might crowd products together to + : prevent entry. In the former case, if consumers prefer more variety, it is possible that + : repositioning could alleviate the negative effects of the merger (Berry and Waldfogel + : (2001), Sweeting (2008)). Chapter 2 provides a method to disaggregate the total + : impact of the merger on consumer surplus into changes in product variety and in + : supplied quantity. The same method can be used to predict whether extra variety + : could alleviate negative market power effects for a hypothetical merger. In the case + : of radio, extra variety alone leads to a 1.3% increase in listener welfare and decreases + : advertiser welfare by $147m per year. I find that product ownership consolidation + : and repositioning are followed by advertising quantity readjustments. I estimate that + : this effect alone leads to a 0.3% decrease in listener welfare (with the variety effect it + : sums to a 1% increase) and an additional $153m decrease in advertiser welfare (with + : the variety effect it totals $300m). While extra variety mitigates the negative effects + : of mergers on listeners, it increases the negative impact on advertisers. + : Chapter 3 deals with a dynamic merger analysis. The current empirical litera- + : ture on mergers and repositioning assumes that the market structure is exogenous + : (Nevo (2000), Pinkse and Slade (2004), Ivaldi and Verboven (2005)). This approach + : does not take into account dynamic processes like post-merger repositioning, follow- + : up mergers, and fixed cost synergies, that could potentially lower prices and provide + : consumers with other non-price benefits. Moreover, the assumption that mergers are + : exogenous may create a selection bias that results in overestimation of cost synergies + : (for example the estimator might pick up other unobserved components correlated + : with the propensity to merge). This thesis provides a new, dynamic framework in + : which decisions to merge and to reposition products are endogenous. Such an ap- + : proach provides consistent estimates of the long-run effects of mergers. In addition, it + : allows for the estimation of cost synergies without any data on cost. The framework +header : CHAPTER 1. INTRODUCTION 4 +blank : +blank : +blank : +text : is straightforward, easy to implement, and computationally tractable. Application to + : radio reveals that the 1996-2006 merger wave provided $2.5b per year of cost syn- + : ergies, which constitutes about 10% of total industry revenue. The scale of those + : efficiencies is a an order of magnitude higher than loss in surplus for advertisers. +header : Chapter 2 +blank : +text : Mergers in two-sided markets: + : Case of U.S. radio industry +blank : +heading : 2.1 Preface +text : This chapter studies the consequences of mergers in two-sided markets by estimating a + : structural supply and demand model and performing counterfactual experiments. The + : analysis is performed on the example of a merger wave in U.S. radio; however, it is applicable + : to other two-sided markets like credit cards, trading platforms or computer games. There + : are two main contributions from this chapter. First, I identify the conflicting incentives of + : merged firms to exercise market power on both sides of the market (listeners and advertisers + : in the case of radio). Second, I disaggregate the effect of mergers on consumers into changes + : in product variety and changes in supplied ad quantity. + : The model is estimated using data on 13,000 radio stations from 1996 to 2006. I find that + : firms have moderate market power over listeners in all markets, extensive market power over + : advertisers in small markets and no market power over advertisers in large markets. Coun- + : terfactuals reveal that extra product variety created by post-merger repositioning increased + : listeners’ welfare by 1.3% and decreased advertisers’ welfare by about $160m per-year. How- + : ever, subsequent changes in supplied ad quantity decreased listener welfare by 0.4% (for a + : total impact of +0.9%) and advertiser welfare by an additional $140m (for a total impact + : of -$300m). +blank : +blank : +footer : 5 +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 6 +blank : +blank : +blank : +heading : 2.2 Introduction +text : Between 1996 and 2006, the U.S. radio industry experienced an unprecedented merger + : wave due to the 1996 Telecommucation Act, which raised ownership caps in local + : markets and abolished cross-market ownership restrictions. At the height of merger + : activity, about 30% of stations changed ownership each year and about 20% changed + : the format of broadcasted programming. In this paper, I use this merger wave to + : study the consequences of consolidation in two-sided markets. I make two main + : contributions. First, I identify conflicting incentives for stations to exercise market + : power on both sides of the market (in the case of radio, the two sides are advertisers + : and listeners). In particular, I separate the impact of consolidation on listener and + : advertiser surplus. Second, I decompose this impact into effects of changes on product + : variety and market power. As a result, I ask whether extra variety can mitigate the + : negative effects of a decrease in competition. Similar issues arise in other two-sided + : markets such as credit cards, newspapers or computer hardware. The framework + : proposed in this paper can be easily adjusted to analyze any of these industries. + : In two-sided markets, firms face two interrelated demand curves from two distinct + : types of consumers. These demands give merging firms conflicting incentives because + : exercising market power in one market lowers profits in the other market. In the case + : of radio, a company provides free programming to listeners but draws revenue from + : selling advertising that is priced on a per-listener basis. In the listener market, a + : merged firm would like to increase post-merger advertising because it captures some + : switching listeners. This advertising decreases the welfare of listeners and increases + : the welfare of advertisers. However, from the perspective of the advertising market, + : the merged firm would like to supply less advertising, which has the exact opposite + : impact on listener and advertiser welfare. The firm’s ultimate decision, which deter- + : mines the impact of consolidation on the welfare of both consumer groups, depends + : on the relative demand elasticities in both markets. + : In this paper, I separately estimate elasticities for both consumer groups using a + : structural model of the demand and supply of radio programming and advertising. + : Using those estimates, I perform counterfactual policy experiments that quantify the +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 7 +blank : +blank : +blank : +text : impact of consolidation on listener and advertiser surplus. I find that market power + : on the listener side is similar across geographical markets. In contrast, the amount + : of market power on the advertiser side depends on market population. In particular, + : firms have a considerable control over advertising price in smaller markets; however, + : they are price takers in larger markets. Consequently, mergers result in firms lowering + : advertising quantity in small markets (less than 500 thousand people) by about 13%, + : which leads to a 6% per-listener increase in ad prices. Mergers increase listener + : surplus by 2.5% but at the same time decrease advertiser surplus by $235m per + : year. Conversely, in large markets (more than 2 million people) mergers lead to + : a 5.5% increase in total advertising minutes while per-listener price stays constant. + : This results in a 0.3% decrease in listener welfare as well as a slight decrease in + : advertiser welfare ($0.1m per year). The aggregate national impact of the merger + : wave amounted to a listener welfare gain of 1% and a $300m per year advertiser + : welfare loss. I conclude that listeners benefited and advertisers were disadvantaged + : by the 1996 Telecom Act. + : My work is related to several theoretical papers studying complexity of pricing + : strategies in two-sided markets. The closest studies related to this paper are: Arm- + : strong (2006), Rochet and Tirole (2006), Evans (2002) and Dukes (2004). The general + : conclusion in this literature is that using a standard supply and demand framework + : of single-sided markets might be not sufficient to capture the economics of two-sided + : markets. Additionally, there have been several empirical studies on this topic. For + : example Kaiser and Wright (2006), Argentesi and Filistrucchi (2007) and Chandra + : and Collard-Wexler (2009) develop empirical models that recognize the possibility of + : market power in both sides of the market. They use a form of the Hotelling model pro- + : posed by Armstrong (2006) to deal with product heterogeneity. I build on their work, + : incorporating recent advances in the literature on demand with differentiated prod- + : ucts. This allows me to incorporate richer consumer heterogeneity and substitution + : patterns (e.g. Berry, Levinsohn, and Pakes (1995), and Nevo (2000)) that are neces- + : sary to capture complicated consumer preferences for radio programing. Moreover, I + : supplement reduced form results on market power with out-of-sample counterfactuals + : that explicitly predict changes in supplied ad quantity and consumer welfare. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 8 +blank : +blank : +blank : +text : The second contribution of this paper is the decomposition of the total impact of + : mergers on consumer surplus into changes in product variety and effects of exercising + : extra market power from joint ownership. This exercise is motivated by the fact + : that in most cases consumers have preference for variety, so it is possible that extra + : variety created by mergers might mitigate the negative effects of extra market power. + : In order to verify the above claim, I quantify consumers’ value for extra variety and + : compare it to the loss in surplus coming from the extra market power. This approach + : relates to Kim, Allenby, and Rossi (2002), who compute the compensating variation + : for the changes of variety in tastes of yogurt and Brynjolfsson, Hu, and Smith (2003) + : who do the same for the variety of books offered in on-line bookstores. These papers + : assume away the fact that changes in variety will be followed by readjustments in + : equilibrium prices. In this paper, taking their analysis one step forward, I incorporate + : such strategic responses by performing counterfactual experiments. + : Berry and Waldfogel (2001) and Sweeting (2008) document that the post-1996 + : merger wave resulted in an increase in product variety. I investigate their claim using + : a structural utility model and conclude that extra variety alone leads to a $1.3% + : increase in listener welfare. However, because product repositioning softened com- + : petition in the advertising market and caused some stations to switch to a “Dark“ + : format 1 , advertiser welfare decreased by $147m per year. Additionally, I find that + : product ownership consolidation and repositioning are followed by advertising quan- + : tity readjustments. I estimate, that effect alone leads to a 0.3% decrease in listener + : welfare (with the variety effect it totals to the 1% increase) and an additional $153m + : decrease in advertiser welfare (with the variety effect it totals $300m). While ex- + : tra variety mitigates the negative effects of mergers on listeners, it strengthens the + : negative impact on advertisers. + : This paper is organized as follows. Section 2 outlines the questions investigated + : in the paper in a formal way and describes the structural model of the industry. + : Section 3 contains the description of the data. Estimation techniques used to identify + : the parameters of the model are described in Section 4. Results of the structural +footnote : 1 +text : When in “dark” format, the station holds the frequency so that other stations cannot use it. + : “Dark” stations typically do not broadcast or broadcast very little non-commercial programming. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 9 +blank : +blank : +blank : +text : estimation are presented in Section 5. Section 6 describes the results of counterfactual + : experiments. Robustness checks of different modeling assumptions are contained in + : Section 7. Section 8 provides the conclusion. +blank : +blank : +heading : 2.3 Radio as a two-sided market +text : The radio industry is an example of a two-sided market (other examples include + : advertising platforms, credit cards or video games). Such markets are usually char- + : acterized by the existence of three types of agents: two types of consumers and + : a platform provider. What distinguishes this setup from a standard differentiated + : product oligopoly is that the platform provider is unable to set prices for each type of + : consumer separately. Instead, the demand curves are interrelated through a feedback + : loop in such a way that quantity sold to one consumer determines the market clearing + : price for the other consumer. In this subsection I argue that this feedback makes it + : complicated to determine whether the supplied quantities are strategic substitutes + : or complements (as defined in Bulow, Geanakoplos, and Klemperer (1985)). This + : creates important trade-offs in the case of a merger and affects the division of surplus + : between both types of consumers. The remainder of this subsection discusses this + : mechanism in detail using the example of radio; however, the discussion applies to + : the majority of other two-sided markets. + : In the case of radio there are three types of agents: radio stations, listeners, + : and advertisers. Radio stations provide free programming for listeners and draw + : revenue from selling advertising slots. First, consider the demand curve for radio + : programming. The listener market share of the radio station j is given by +blank : +math : rj = rj (q|s, d, θL ) (2.1) +blank : +text : where q is the vector of advertising quantities, s are observable and unobservable + : characteristics of all active stations, d are market covariates and θL are parameters + : of the listener demand. Since radio programming is free, there is no explicit price in + : this equation. However, because listeners have disutility for advertising, its effect is +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 10 +blank : +blank : +math : ∂rj + : similar to price, i.e. ∂qj + : < 0. +text : The market clearing price of an advertising slot in station j depends on the amount + : of advertising supplied and the number of listeners to station j. Therefore, the inverse + : demand curve for advertising slots is +blank : +math : pj = pj (q, rj (q)|s, d, θA ) (2.2) +blank : +text : where θA are parameters. Note that the advertising quantity affects the advertising + : price in two ways: directly through the first argument and indirectly through the + : listener demand feedback loop (the second argument). + : Suppose for now that each owner owns a single station and there is no marginal + : cost (I relax these assumptions later). In equilibrium, each radio station chooses their + : optimal ad quantity, keeping the quantities of the other stations fixed, i.e. +blank : +math : max pj (q, rj (q)|q−j )qj (2.3) + : qj +blank : +blank : +text : In contrast to a differentiated products oligopoly, the firm has just one control (ad + : quantity) that determines the equilibrium point on both demand curves simultane- + : ously. The first order conditions for profit maximization are given by +blank : +math : ∂pj ∂pj ∂rj + : qj + qj + pj = 0 + : ∂qj ∂rj ∂qj +blank : +text : The important fact is that this condition shares features with both the Cournot and + : Bertrand models. On the one hand, the first term represents the direct effect of + : quantity on price, and it is reminiscent of the standard quantity setting equilibrium + : (Cournot). On the other hard, the second component represents the listener feedback + : loop and is reminiscent of the price setting model (Bertrand), because ad quantities + : function like prices in the demand for programming. + : In order to determine the impact of a merger on the equilibrium ad quantities + : supplied we need to know if they are strategic complements or substitutes. The + : duality described in the previous paragraph make it ambiguous. This is because + : in the Cournot model quantities are strategic substitutes and in the differentiated +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 11 +blank : +blank : +blank : +text : product Bertrand model prices are strategic complements. Without knowing the + : relative strengths of the direct effects and the feedback loop, we cannot conclude + : whether a merger leads to an increase or decrease in ad quantity on the margin. + : Moreover, in the borderline case in which the effects cancel each other, a merger does + : not effect quantity at all; in this case, even though companies have market power + : over both consumers, they are unable to exercise it. Measuring these effects is critical + : for predicting the split of surplus between advertisers and listeners. When the direct + : effect is stronger, mergers lead to contraction in the ad quantity supplied and higher + : prices. This will benefit listeners but hurt advertisers. However, if the feedback loop + : is stronger than the direct effect then merger leads to more advertising and lower + : prices, benefiting advertisers and hurting listeners. + : Because the theory does not give a clear prediction about the split of surplus, I + : investigate this question empirically using a structural model. In the remainder of + : this section I put more structure on equations (2.1), (2.2) and (2.3), enabling separate + : identification of both sets of demand elasticities. I discover the relative strength of + : the direct and feedback effects and perform counterfactuals that quantify the extent + : of surplus reallocation. +blank : +blank : +heading : 2.3.1 Industry setup +text : During each period t, the industry consists of M geographical markets that are char- + : acterized by a set of demographic covariates d ∈ Dm . Each market m can have up to + : Jm active radio stations and Km active owners. Each radio station is characterized by + : one of F possible programming formats. Station formats include the so-called “dark” + : format when a station is not operational The set of all station/format configurations + : m + : is given by FJ . Ownership structure is defined as a Km -element partition of sta- + : m + : tion/format configuration smt ∈ FJ . In an abuse of notation, I will consider smt + : to be a station/format configuration for market m at time t, as well as an owner- + : ship partition. Each member of the ownership partition (denoted as sk ) specifies the + : portfolio of stations owned by firm k. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 12 +blank : +blank : +blank : +text : The quality of the programming of radio station j is fully characterized by a one- + : dimensional quality measure ξj ∈ Ξ ⊂ R. The state of the industry at time time t + : in market m is therefore fully characterized by: a station/format configuration and + : ownership structure stm , vector of station quality measures ξ tm and market covariates + : dtm . In the next subsections I present a detailed model of listener demand, advertiser + : demand, and supply side. Throughout the description I take the triple (stm , ξ tm , dtm ) + : as given and frequently omit market or time subscripts to simplify the notation. +blank : +blank : +heading : 2.3.2 Listeners +text : This subsection describes the details of the demand for listenership introduced in + : equation (2.1). The model will be a variation on the random coefficient discrete + : choice setup proposed by Berry, Levinsohn, and Pakes (1995). + : I assume that each listener chooses only one radio station to listen to at a particular + : moment. Suppose that s is a set of active stations in the current market at a particular + : time. For any radio station j ∈ s, I define a vector ιj = (0, . . . , 1, . . . , 0) where 1 is + : placed in a position that indicates the format of station j. + : The utility of listener i listening to station j ∈ s is given by +blank : +math : L L + : uij = θ1i ιj − θ2i qj + θ3L FMj + ξj + ji (2.4) +blank : +text : L +text : where θ2i is the individual listener’s demand sensitivity to adverting, qj the amount + : of advertising, ξj the unobserved station quality, ji an unobserved preference shock + : L + : (distributed type-1 extreme value), and finally θ1i is a vector of the individual listener’s + : random effects representing preferences for formats. + : I assume that the random coefficients can be decomposed as +blank : +math : L + : θ1i = θ1L + ΠDi + ν1i , Di ∼ Fm (Di |d), ν1i ∼ N (0, Σ1 ) +blank : +math : and + : L + : θ2i = θ2L + ν2i , ν2i ∼ N (0, Σ2 ) +blank : +text : where Σ1 is a diagonal matrix, Fm (Di |d) is an empirical distribution of demographic +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 13 +blank : +blank : +blank : +text : characteristics, νi is unobserved taste shock, and Π is the matrix representing the + : correlation between demographic characteristics and format preferences. I assume + : that draws for νi are uncorrelated across time and markets. + : The random effects model allows for fairly flexible substitution patterns. For + : example, if a particular rock station increases its level of advertising, the model + : allows for consumers to switch proportionally to other rock stations depending on + : demographics. + : Following Berry, Levinsohn, and Pakes (1995), I can decompose the utility into a + : part that does not vary with consumer characteristics +blank : +math : δj = δ(qj |ιj , ξj , θL ) = θ1L ιi − θ2L qj + θ3L FMj + ξj +blank : +text : an interaction part +blank : +math : µji = µ(ιj , qj , ΠDi , νi ) = (ΠDi + ν1i )ιj + ν2i qj +blank : +text : and error term ji . + : Given this specification, and the fact that ji is distributed as an extreme value, + : one can derive the expected station rating conditional on a vector of advertising levels + : q, market structure s, a vector of unobserved station characteristics ξ, and market + : demographic characteristics d, +math : Z Z + : L exp[δj + µji ] + : rj (q|s, ξ, d, θ ) = P dF (νi )dFm (Di |d) + : j 0 ∈s exp[δj 0 + µj 0 i ] +blank : +blank : +heading : 2.3.3 Advertisers +math : In this subsection I present the details of the demand for advertising introduced in + : equation (2.2). The model captures several important features specific to the radio + : industry. In particular, the pricing is done on a per-listener basis, so that the price + : for a 60sec slot of advertising is a product of cost-per-point (CPP) and station rating + : (market share in percents). Moreover, radio stations have a direct market power over + : advertisers, so that CPP is a decreasing function of the ad quantities offered by a +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 14 +blank : +blank : +blank : +text : station and its competitors. The simplest model that captures these features and is + : a good approximation of the industry is a linear inverse demand for advertising, such +math : as ! +math : X + : pj = θ1A rj 1 − θ2A ωfmf 0 qf 0 (2.5) + : f 0 ∈F +blank : +text : where f is a format of station j, θ1A is a scaling factor for value of advertising, θ2A is + : a market power indicator and ωf f 0 ∈ Ω are weights indicating competition closeness, + : between formats f and f 0 . + : The weights ω are a key factor determining competition between formats and thus + : market power. They reflect the fact that some formats are further and others are closer + : substitutes for advertisers because of differences in the demographic composition of + : their listeners. In principle, one could proceed by estimating these weights from + : the data. However, here it is not feasible to do that because the available data + : do not contain radio station level advertising prices. Instead, I make additional + : assumptions that will enable me to compute the weights using publicly available data. + : The reminder of this subsection discusses the formula for the weights and provides + : an example supporting this intuition. The formal micro-model is given in Appendix + : A.1. + : Let there be A types of advertisers. Each type a ∈ A targets a certain demographic + : group(s) a. I.e. advertiser of type a gets positive utility only if a listener of type a + : hears an ad. Denote rf |a to be the probability that a listener of type a chooses format + : f and ra|f to be the probability that a random listener of format f is of type a. + : Advertisers take these numbers, along with station ratings rj , as given and decide on + : which station to advertise. This assumption is is motivated by the fact that about + : 75% is purchased by small local firms. Such firms’ advertising decisions are unlikely + : to influence prices and station ratings in the short run. + : This decision problem results in an inverse demand for advertising with weights + : ωjj 0 , that are given by +blank : +math : 1 X  + : ωf f 0 = P 2 + : ra|f ra|f rf 0 |a (2.6) + : a∈A ra|f a∈A +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 15 +blank : +blank : +blank : +text : The formal justification and derivation of this equation is given in Appendix A.1. The + : intuition behind it is that the total impact on the per-listener price of an ad in format + : f is a weighted average of impacts on the per-listener value of an ad for different types + : of advertisers. The weighting is done by the advertisers’ arrival rates, which are equal + : to the listeners’ arrival rates ra|f . For each advertiser of type a the change of value + : of an ad in format f , in response to a change of total quantity supplied in format f 0 , + : is affected by two things: it is proportional to the probability of correct targeting in + : format f , given by ra|f , because advertisers are expected utility maximizers; and it + : is proportional to the share of advertising purchased by this advertiser in format f 0 , + : given by rf 0 |a . Assembling these pieces together and normalizing the weights to sum + : to 1 gives equation (2.6). + : To illustrate how these weights work in practice, consider the following example. + : Suppose that there are only two possible formats of programming: Talk and Hits, and + : two types of consumers: Teens and Adults. Teens like mostly Hits format and Adults + : like Talk format. However, Adults like Hits more than Teens like Talk. Hypothetical + : numerical values of rf |a and ra|f are given in Table 2.1. +blank : +table : rf |a ra|f Ω + : Talk Hits Teens Adults Talk Hits + : Teens 1/5 4/5 Talk 1/4 3/4 Talk 0.56 0.44 + : Adults 3/5 2/5 Hits 2/3 1/3 Hits 0.28 0.72 +text : Table 2.1: Simple example of advertising weights +blank : +text : In Table 2.1, the impact of Hits on the price of Talk is greater than the impact of + : Talk on the price of Hits. This is due to the fact that the quantity supplied in the Hits + : format affects Adult-targeting advertisers (who drive the price of the Talk format) + : to a much greater extent than ad quantity in Talk affects Teen-targeting advertisers + : (who drive the price of the Hits format). Moreover, because the weights sum up to + : 1, it must be that the own effect of Talk is weaker than that of Hits. This is exactly + : the essence of the mechanism behind Equation (2.6). More examples from the data + : with an extensive discussion are given in Section 2.6. + : In the next section I will combine demand for programming and advertising to +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 16 +blank : +blank : +blank : +text : compose the profits of the radio station owners. +blank : +blank : +heading : 2.3.4 Radio station owners +text : In this subsection I will describe a profit maximizing problem for the radio station + : owners. It will be a version of equation (2.3) that allows for non-zero cost in selling + : advertising and common radio station ownership. Given the advertising quantity + : choices of competing owners q−k , the profit of radio station owner k is given by +math : X + : π̄k (qk |q−k , ξ, θ) = max rj (q|ξ, θL )pj qj − MCj (qj ) = + : {qj ;j∈sk } + : j∈sk +blank : +math : X X + : ! (2.7) + : = θ1A max L + : qj rj (q|ξ, θ ) 1 − θ2A ωfmf 0 qf 0 A C + : + Cj (qj |θ , θ ) + : {qj ;j∈sk } + : j∈sk f 0 ∈F +blank : +blank : +text : where Cj (qj ) is the total cost of selling advertising. I assume constant marginal cost + : and allow for a firm level of unobserved cost heterogeneity ηj , i.e. Cj (qj |θA , θC ) = + : θ1A [θC + ηj ]qj . + : I assume that the markets are in a Cournot Nash Equilibrium. The first order + : conditions for profit optimization become +blank :   + : X ∂rj 0 + : rj pj + qj 0 A m + : pj 0 − rj 0 θ2 ωjj 0 − θC − ηj = 0 ∀k and j ∈ sk (2.8) + : j 0 ∈s + : ∂qj + : k +blank : +blank : +blank : +text : Additionally, I assume that station unobserved quality is exogenous but serially cor- + : related. It evolves according an AR(1) process such that +blank : +math : ξjt = ρξjt−1 + ζjt (2.9) +blank : +text : where ζjt is an exogenous innovation to station quality. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 17 +blank : +blank : +blank : +heading : 2.4 Data description +text : I have constructed a panel of data on radio stations and radio station ownership + : merging data from two sources: BIA Financial Network Inc. and the SQAD Media + : Market Guide. + : BIAfn provided me data on: radio station ownership, revenues, market shares and + : formats. The data are a 1996-2006 panel covering each radio station in the market + : in 2006. The data are incomplete in the sense that I do not observe all the stations + : that exited the market between 1996 and 2006. According to Sweeting (2007) there + : were only 50 stations that exited during this period, mostly due to violations of FCC + : regulations. Because this number is small relative to the 11,000 stations in the sample, + : this omission is unlikely to significantly influence the results. + : The BIAfn data are supplemented with data on aggregate advertising prices. Un- + : fortunately, price data at the station level are not available. SQAD instead provides + : estimates of market prices that are obtained using proprietary formulas. According + : to anecdotal evidence, those estimates are widely recognized as the industry standard + : and are the best available data on market prices. Radio market prices are reported + : as a Cost per Rating Point (CPP). CPP is the cost of advertising per 1 percent of + : listenership. SQAD provides CPP broken down into daytime and demographic cat- + : egories. We will estimate station level prices from SQAD CPPs using radio station + : ratings that are broken down by time of day and demographics. + : An observation in my data is a radio station operating in a specific half-year and + : in a specific market. BIAfn and SQAD use Arbitron market definitions. An Arbitron + : market is in most cases a county or a metropolitan area. According to the surveys + : conducted by CRA International (2007) for the Canadian market (which is similar to + : the US market): “The majority of radio advertisers are local. They are only interested + : in advertising in their local area since most of their customers and potential buyers + : live in or very near their city.” In our analysis, I assume no interdependence between + : markets. To further assure that there is no overlap between markets, I use only the + : 88 market sub-selection that was developed in Sweeting (2007). Table 2.7 presents a + : list of the 88 markets, along with their populations. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 18 +blank : +blank : +table : 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 + : Number of + : 26.75 26.92 27.25 27.53 27.66 27.89 28.48 28.61 28.72 28.78 28.86 + : stations + : Number of + : 16.58 15.55 14.94 14.21 13.29 13.03 13.16 12.96 12.73 12.52 12.48 + : owners + : C3 0.77 0.83 0.88 0.91 0.97 0.95 0.93 0.93 0.93 0.93 0.90 + : Number of + : 4.43 5.10 5.66 5.94 6.58 6.32 6.31 6.34 6.42 6.38 6.28 + : stations owned + : Fraction of + : stations that 0.12 0.12 0.10 0.11 0.12 0.03 0.04 0.03 0.03 0.03 NaN + : changed ownership + : Fraction of + : stations that 0.11 0.11 0.13 0.12 0.12 0.13 0.10 0.11 0.11 0.11 NaN + : changed format + : Ad quantity 23.19 25.85 26.12 28.45 30.31 24.71 28.37 24.54 28.16 28.30 26.95 + : Price divided by + : 1.00 0.96 1.08 1.10 1.26 1.51 1.42 1.51 1.39 1.37 1.43 + : price in 1996 +blank : +text : Table 2.2: Panel data descriptive statistics +blank : +text : To achieve a sharper identification of the random effects covariance matrix, I use + : listenership shares of different demographic groups in each of the formats that has + : been aggregated from the 100 biggest markets 2 . I observe listenership shares of + : different age/gender groups within each station format between 1998 and 2006, and + : shares for income, race and education groups between 2003 and 2006. Unfortunately, + : I do not observe a full matrix of market shares for all the combinations of demographic + : variables. For example, I do not see what the share of rock stations is among black, + : educated males. Instead I have shares for blacks, educated people, and males. + : Table 2.2 contains some basic aggregate statistics about the industry. The top + : part of the table documents changes in concentration of radio station ownership. + : The average number of stations owned in our dataset grew from 4.43 in 1996 to + : 6.28 in 2006. This ownership consolidation resulted in growth of the market share + : of the 3 biggest owners (C3) from 77% in 1996 to 90% in 2006, peaking at 97% in + : 2000. The middle part of the table contains the average percentages of stations that + : switched owners and that switched formats. Between 1996 and 2000 more than 10% + : of stations switched owners yearly. After 2000 the number dropped to below 4%. + : Greater concentration activity in the 1996-2000 period was also associated with more + : format switching. The percentage of stations that switched format peaked in 1998 + : and 2001 at 13%. +footnote : 2 +text : Source: Arbitron Format Trends Report +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 19 +blank : +blank : +blank : +heading : 2.5 Estimation +text : The estimation of the model is done in two steps. In the first step, I estimate the + : demand model that includes parameters of the consumer utility θL (see equation + : (2.4)) and the unobserved station quality lag parameter ρ (see equation (3.1)). In + : the second step, we recover parameters of the inverse demand for advertising θA , wjj 0 + : (see equation (2.5)) and cost parameters θC (see equation (2.7)). +blank : +blank : +heading : 2.5.1 First stage +text : This stage provides the estimates of demand for radio programming θL . Estimation is + : done using the generalized method of simulated moments. I use two sets of moment + : conditions. The first set is based on the fact that innovation to station unobserved + : quality ξj has a mean of zero conditional on the instruments: +blank : +math : E[ξjt − ρξjt−1 |Z1 , θL ] = 0 (2.10) +blank : +text : This moment condition follows Berry, Levinsohn, and Pakes (1995) and extends it by + : explicitly introducing auto-correlation of ξ. I use instruments for advertising quantity + : since it is likely to be correlated with unobserved station quality. My instruments + : include: lagged mean and second central moment of competitors’ advertising quantity, + : lagged market HHIs and lagged number and cumulative market share of other stations + : in the same format. These are valid instruments under the assumption that ξt follows + : an AR(1) process and the fact that decisions about portfolio selection are made before + : decisions about advertising. + : A second set of moment conditions is based on demographic listenership data. + : Let Rf c be the national market share of format f among listeners possessing certain + : demographic characteristics c. The population moment conditions are +blank : +math : exp[δjmt + µmt ji ] + : Z Z Z + : P mt mt + : t + : dF (νi )dFct (Dic , m)dt = Rf c (2.11) + : t t ,m) + : (Dic νi 0 + : j ∈s mt exp[δ j 0 + µ ij 0 ] +blank : +text : where Fct (Di , m) is a national distribution of people who possess characteristic c at +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 20 +blank : +blank : +blank : +text : time t. Each person is characterized by the demographic characteristics Di and the + : market m they belong to. + : For each time t and demographic characteristic c, I draw I observation pairs + : t + : (Dic , m) from the nationally aggregated CPS. Let g = (g1 , g2 ) represent the empirical + : moments and W be a weighting matrix. I estimate the model by using the constrained + : optimization procedure: +blank : +math : min g 0 W g + : θL ,ξ,g +blank : +math : Subject to: + : r̂jmt (qmt |smt , ξmt , dmt , θL ) = rjmt ∀t, m + : (2.12) + : exp[δjmt + µmt ji ] + : Z + : 1 X X + : P mt mt + : dF (νi ) − Rf c = g1 ∀c + : TI t t ν i j 0 ∈smt exp[δ j 0 + µ ij 0 ] + : (Dic ,m) + : 1 + : Z1 (ξ − ρLξ) = g2 + : size of ξ +blank : +text : where L is a lag operator that converts the vector ξ into one-period lagged values. If + : the radio station did not exist in the previous period, the lag operator has a value of + : zero. Integration with respect to demographics when calculating the first constraint is + : obtained by drawing from the CPS in the particular market and period. This way of + : integrating allows us to maintain proper correlations between possessed demographic + : characteristics. The same is true when obtaining the data set Dict . When computing + : the interaction terms µ in the second constraint, I draw one vector νi from the normal + : distribution for each Dict . +blank : +blank : +heading : 2.5.2 Second stage +text : The second stage of the estimation obtains the competition matrix Ω and the pa- + : rameters of demand for advertising θA . The estimation is done separately for every + : market, thereby allowing for different Ω and θA . + : To compute the matrices Ωm for each market I use the specification layed out in +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 21 +blank : +blank : +blank : +text : section 2.3.3. The elements of the matrix Ω are specified as +blank : +math : 1 X  + : ωf f 0 = P 2 + : ra|f ra|f rf 0 |a + : a∈A ra|f a∈A +blank : +blank : +text : following equation (2.6). The rf |a are advertisers’ beliefs about listeners’ preferences + : for formats. These are constant across markets. To recognize that advertisers know + : the demographic composition of each market I allow for market specific listener arrival + : rates for each format rfm|a . However, I assume that the advertisers compute those + : values by using Radio Today reports and the Current Population Survey. After + : computing weights, I treat Ωm as exogenous and fixed in all of the following steps 3 . + : After computing matrices Ω, I estimate θA . Using estimates of demand for radio + : programming θL from the first stage, I compute ratings for each station conditioned + : on the counterfactual advertising quantities. I use the set of 3M moment conditions +blank : +math : Em [η m |Z2 , θA , θC ] = 0 ∀m ∈ M (2.13) +blank : +text : where the integral is taken with respect to time and stations in each market. ηjtm is + : an unobserved shock to marginal cost defined in equation (2.5). The Z2 are three + : instruments: a column of ones, the AM/FM dummy and number of competitors in + : the same format. They are uncorrelated with η m under the IID assumption, but + : are correlated with the current choice of quantity because they describe the market + : structure. + : We back out ηjtm using FOCs for owner’s profit maximization (see equation (2.7)) +blank : +math : ∂rjt 0 t + : X   + : ηjt = rjt ptj + qjt 0 A t m C + : p 0 − θ2m rj 0 ωf f 0 − θm ∀t ∈ T, k ∈ Ktm , j ∈ stm (2.14) + : ∂qjt j k + : j 0 ∈stm + : k +blank : +blank : +math : A A C +text : Since the equation does not depend on θ1m , I can use it to estimate θ2m and θm . During + : the estimation, I allow for a different value of marginal cost for each market. I allow +footnote : 3 +text : Such an approach potentially ignores possible variance of the Ωm estimator. The source of + : this variance might come from the finiteness of the CPS dataset and the distribution of Arbitron + : estimates. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 22 +blank : +blank : +blank : +text : for 3 different values for the slope of inverse demand depending on the population of + : the market (up to 500 people, between 500 and 1500, and 1500 or more). Ratings + : and derivatives of ratings in the equation (2.14) are calculated using the estimates of + : θL and ξ from the first stage. Demographic draws are taken from the CPS and are + : A + : independent of those used in the first stage. Given the estimates of θ2m and θC , I + : A + : can back out θ1m by equating the observed average revenue in each market with its + : predicted counterpart. + : Next I discuss a variation in the data that identifies parameters θA and θC . The + : intuition for such identification is that estimating Equation 2.14 can be regarded as a + : C + : linear regression in which θm is an intercept and θ2A is a coefficient of a variable that + : is a function of supplied quantity. In this case, the mean deviation of FOCs from zero + : C + : in each market identifies the intercept θm . The slope parameter θ2A is identified by the + : size of the response of the firm to changes in quantity supplied by its competitors due + : to change in the market structure or demographics. Such a response, as mentioned + : in Section 2.3, is composed of listeners’ demand feedback and the direct effect of + : quantity on CPP. Elasticity of listeners’ demand, that determines the strength of the + : feedback, is consistently estimated in the first step. Therefore, one can subtract the + : difference out the feedback effect from the total response observed in the data. This + : allows to obtain the strength of the direct effect that directly identifies the slope of + : the CPP, θ2A . For example, if we look at the response of ad quantity reacting to the + : merger, the slope of listeners’ demand alone predicts large increases in ad quantity. + : However in the data, we observe smaller increases or even decrease in the quantity + : supplied, depending on the market. Those differences are rationalized by a negative + : value of CPP slope, θ2A . +blank : +blank : +heading : 2.6 Results +text : This section presents estimates of the structural parameters. The next subsection + : discusses listeners’ demand parameters. This is followed by results concerning adver- + : tisers’ demand and marker power. The last subsection contains estimates of marginal + : cost and profit margin (before subtracting fixed cost). +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 23 +blank : +blank : +blank : +heading : 2.6.1 Listeners’ demand +text : Table 2.3 contains estimates of demand parameters for radio programming. The esti- + : mate of the mean effect of advertising on listeners’ utility is negative and statistically + : significant. This is consistent with the belief that radio listeners have a disutility for + : advertising. When it comes to the mean effects of programming formats, Contempo- + : rary Hit Radio format gives the most utility, while the News/Talk format gives the + : least. + : The second column of Table 2.3 contains variances of random effects for station + : formats. The higher a format’s variance, the more persistent are the tastes of listeners + : for that format. For example, in response to an increased amount of advertising, if + : the variance of the random effect for that format is high, listeners tend to switch + : to a station of the same format. The estimates also suggest that tastes for the + : Alternative/Urban format are the most persistent. + : Table 2.4 contains estimates of interactions between listener characteristics and + : format dummies. The majority of the parameters are consistent with intuition. For + : example, younger people are more willing to choose a CHR format while older people + : go for News/Talk. The negative coefficients on the interaction of Hispanic format + : with education and income suggests that less educated Hispanic people with lower + : income are more willing to listen to Hispanic stations. For blacks, I find a disutility + : for Country, Rock and Hispanic, and a high utility for Urban. This is consistent + : with the the fact that Urban radio stations play mostly rap, hip-hop and soul music + : performed by black artists. +blank : +blank : +heading : 2.6.2 Advertisers’ demand +text : Tables 2.5 presents the weights for selected markets representing large, medium and + : small listener populations. They were computed using the 1999 edition of Radio + : Today publication and Common Population Survey aggregated from 1996 to 2006. + : It is interesting to compute a total impact coefficient that is the sum of all the + : columns of the table for each format. Not surprisingly, general interest formats like + : AC and News/Talk have the biggest impact on the price of advertising, while Spanish +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 24 +blank : +blank : +blank : +blank : +table : Mean Effects (θ1L ) Random Effects (Σ1 ) + : −1.106∗∗∗ 0.030∗∗∗ + : Advertising (0.002) (0.009) + : 0.861∗∗∗ + : AM/FM (0.000) + : - + : AC, + : SmoothJazz, −2.431∗∗∗ 0.043∗∗∗ + : (0.008) (0.004) + : and New AC + : ∗∗∗ + : Rock −1.559 0.004 + : (0.140) (0.020) +blank : +table : −0.179∗∗∗ 0.009∗ + : CHR (0.025) (0.006) + : ∗∗∗ + : Alternative −2.339 0.348∗∗∗ + : Urban (0.026) (0.008) + : ∗∗∗ + : −4.678 0.024∗∗∗ + : News/Talk (0.010) (0.002) +blank : +table : Country −2.301∗∗∗ 0.011∗∗∗ + : (0.006) (0.003) +blank : +table : Spanish −1.619∗∗∗ 0.011∗∗∗ + : (0.004) (0.001) +blank : +table : −4.657∗∗∗ 0.005∗∗∗ + : Other (0.004) (0.002) +blank : +table : 0.568∗∗∗ + : ρ (0.091) + : - +blank : +blank : +blank : +text : Table 2.3: Estimates of mean and random effects of demand for radio programming. + : Stars indicate parameter significance when testing with 0.1, 0.05 and 0.01 test sizes. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 25 +blank : +blank : +table : Demographics characteristics (Π) + : Age Sex Education Income Black Spanish + : AC, + : SmoothJazz, −0.171∗∗∗ −0.341∗∗∗ 0.602∗∗∗ −0.024∗∗∗ 0.121∗∗∗ −1.014∗∗∗ + : (0.001) (0.064) (0.013) (0.003) (0.012) (0.008) + : and New AC + : Rock −0.645∗∗∗ 0.399∗∗∗ 0.861∗∗∗ −0.147∗∗∗ −1.359∗∗∗ −1.643∗∗∗ + : (0.072) (0.031) (0.006) (0.045) (0.007) (0.003) +blank : +table : −2.541∗∗∗ 0.477∗∗∗ 1.772∗∗∗ −0.291∗∗∗ 1.946∗∗∗ 0.463∗∗∗ + : CHR (0.015) (0.080) (0.006) (0.005) (0.015) (0.001) + : ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ + : Alternative −0.817 1.350 0.583 −0.141 3.152 0.267∗∗∗ + : Urban (0.008) (0.018) (0.025) (0.002) (0.005) (0.027) +blank : +blank : +table : News/Talk 0.329∗∗∗ 1.228∗∗∗ 0.237∗∗∗ 0.093∗∗∗ −0.321∗∗∗ −1.649∗∗∗ + : (0.002) (0.012) (0.009) (0.005) (0.001) (0.005) +blank : +table : Country 0.062∗∗∗ −0.149∗∗∗ 0.133∗∗∗ −0.125∗∗∗ −1.548∗∗∗ −1.717∗∗∗ + : (0.004) (0.022) (0.004) (0.003) (0.009) (0.002) +blank : +table : −0.024∗ −0.908∗∗∗ −0.328∗∗∗ −1.140∗∗∗ −2.560∗∗∗ 0.797∗∗∗ + : Spanish (0.013) (0.012) (0.018) (0.002) (0.004) (0.003) +blank : +table : 0.263 0.624∗∗∗ 0.338∗∗∗ −0.031 0.498∗∗∗ 0.238∗∗∗ + : Other (0.373) (0.003) (0.006) (0.063) (0.001) (0.002) +blank : +blank : +blank : +blank : +text : Table 2.4: Interaction terms between listeners’ demographics and taste for radio + : programming. +blank : +text : format has the smallest. The values on the diagonals of the matrices represent the + : formats’ own effect of the quantity of advertising supplied on per-listener price. They + : are usually bigger than the off-diagonal values, that suggests that it is mostly the + : ad quantity in the same format that influences a per-listener price. In accord with + : an intuition, the formats with the most demographically homogenous listener pools, + : Urban/Alternative and Spanish, have the highest values of the own effects. On the + : other hand, general interest formats like CHR and Rock are charaterized by the + : smallest values of the own effect, measuring the fact that their target population of + : listeners is more dispersed across other formats. For cross effects, one notices that + : News/Talk is close to AC and Urban is close to CHR. This can be explained by, for + : example, the age of the listeners. In the former case the formats appeal to an older + : population while in the latter case to a younger one. + : Estimates of the slope of inverse demand are presented in Table 2.6. In mar- + : kets with less than 0.5m people radio stations have considerable control over the + : per-listener price. However, such control significantly drops in markets from 0.5m +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 26 +blank : +blank : +text : Los Angeles, CA +table : AC + : Alternative +table : SmoothJazz Rock CHR News/Talk Country Spanish Other + : Urban + : New AC + : AC + : SmoothJazz 0.22 0.10 0.11 0.09 0.17 0.14 0.00 0.17 + : New AC + : Rock 0.15 0.21 0.12 0.09 0.16 0.13 0.01 0.12 + : CHR 0.18 0.12 0.16 0.16 0.10 0.13 0.03 0.13 + : Alternative + : 0.11 0.05 0.17 0.44 0.06 0.05 0.00 0.12 + : Urban + : News/Talk 0.17 0.10 0.05 0.05 0.30 0.13 0.00 0.21 + : Country 0.16 0.10 0.09 0.07 0.15 0.22 0.01 0.21 + : Spanish 0.03 0.04 0.11 0.02 0.01 0.03 0.72 0.04 + : Other 0.18 0.07 0.06 0.08 0.20 0.17 0.00 0.23 + : Total impact 1.20 0.79 0.87 0.99 1.15 1.00 0.77 1.23 +blank : +blank : +text : Atlanta, GA +table : AC + : Alternative + : SmoothJazz Rock CHR News/Talk Country Spanish Other + : Urban + : New AC + : AC + : SmoothJazz 0.20 0.10 0.12 0.09 0.14 0.18 0.00 0.18 + : New AC + : Rock 0.14 0.21 0.13 0.10 0.12 0.17 0.01 0.13 + : CHR 0.17 0.13 0.17 0.14 0.09 0.17 0.01 0.13 + : Alternative + : 0.11 0.06 0.16 0.40 0.06 0.08 0.00 0.13 + : Urban + : News/Talk 0.16 0.10 0.05 0.05 0.25 0.17 0.00 0.22 + : Country 0.15 0.09 0.08 0.06 0.13 0.26 0.01 0.22 + : Spanish 0.04 0.04 0.12 0.02 0.01 0.03 0.71 0.03 + : Other 0.16 0.07 0.06 0.07 0.16 0.23 0.01 0.25 + : Total impact 1.11 0.78 0.88 0.94 0.95 1.31 0.75 1.29 +blank : +blank : +text : Knoxville, TN +table : AC + : Alternative + : SmoothJazz Rock CHR News/Talk Country Spanish Other + : Urban + : New AC + : AC + : SmoothJazz 0.20 0.11 0.16 0.11 0.10 0.16 0.01 0.16 + : New AC + : Rock 0.13 0.21 0.14 0.11 0.10 0.18 0.01 0.12 + : CHR 0.16 0.12 0.18 0.14 0.08 0.17 0.02 0.13 + : Alternative + : 0.12 0.06 0.16 0.38 0.06 0.08 0.00 0.13 + : Urban + : News/Talk 0.16 0.13 0.10 0.09 0.17 0.16 0.01 0.18 + : Country 0.15 0.13 0.14 0.10 0.09 0.22 0.01 0.16 + : Spanish 0.05 0.05 0.11 0.02 0.02 0.04 0.66 0.05 + : Other 0.17 0.09 0.11 0.12 0.12 0.18 0.01 0.21 + : Total impact 1.12 0.90 1.11 1.05 0.74 1.21 0.72 1.14 +blank : +blank : +blank : +blank : +text : Table 2.5: Product closeness matrices for chosen markets +blank : +text : to 2m people, and it disappears completely in markets with more than 2m people, + : making radio stations essentially price takers. I suspect that this phenomenon can be +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 27 +blank : +blank : +table : Market population less than .5m between .5m and 1.5m more than 1.5m + : 1.34 (0.046) 0.35 (0.026) 0.00 (0.008) +blank : +text : Table 2.6: Slope of the inverse demand for ads θ2A , by market size +blank : +text : explained by the fact that in larger markets there are more outside options for radio + : advertising. This can lead to tougher competition between media outlets, and make + : the inverse demand for advertising flatter. However, in small markets radio might be + : a primary advertising channel, because other media like the Internet or billboards are + : not as widespread. This gives radio stations more control over price. +blank : +blank : +heading : 2.6.3 Supply +text : The marginal costs of selling advertising minutes are presented in Table 2.7. The + : values of this cost range from $356 per minute of advertising sold in Los Angeles, + : CA to $11 in Ft. Myers, FL. 66% of the variation in marginal cost can be explained + : by variation in market population. A population increase of one thousand translates + : to about a 2 cent increase in marginal cost (with t-stat equal to 12). The high cor- + : relation between population and marginal costs can be explained by the fact that + : revenues per-minute of advertising are an increasing function of total market popula- + : tion. Suppose this surplus is split between radio station owners and advertisers’ sales + : people according to the Nash Bargaining solution. In this case, the high correlation + : of revenue with population will translate into a high correlation of marginal cost with + : population. + : From the revenues and marginal cost estimates, I can calculate variable profit + : margins. These are presented in the last last column of Table 2.7. The range is + : from 92% in Shreveport, LA to 15% in Honolulu, HI and Reno, NV. It is interesting + : that 38% of the profit margin variation can be explained by the variance in total ad + : quantity supplied and markets with high profit margins firms supply more advertising. + : The marginal effect of extra minute per day of broadcasted advertising translates into + : 0.6% of extra profit margin. +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS +table : Marginal Profit Marginal Profit + : Market Population (mil) Market Population + : cost ($ per-miute) margin cost margin + : Los Angeles, CA 13,155 356.4 (5.15) 30% Tulsa, OK 856 72.8 (2.13) 21% + : Chicago, IL 9,341 180.0 (2.70) 34% Knoxville, TN 785 54.3 (1.99) 27% + : Dallas-Ft. Worth, TX 5,847 198.6 (5.60) 28% Albuquerque, NM 740 27.4 (1.04) 36% + : Houston-Galveston, TX 5,279 199.7 (4.20) 28% Ft. Myers-Naples-Marco Island, FL 737 11.3 (0.94) 57% + : Atlanta, GA 4,710 95.4 (3.37) 43% Omaha-Council Bluffs, NE-IA 728 48.0 (0.91) 28% + : Boston, MA 4,532 172.2 (3.68) 33% Harrisburg-Lebanon-Carlisle, PA 649 29.7 (1.44) 42% + : Miami-Ft, FL 4,174 134.3 (3.70) 28% El Paso, TX 619 41.8 (4.12) 20% + : Seattle-Tacoma, WA 3,776 128.7 (2.21) 29% Quad Cities, IA-IL 618 51.3 (1.30) 23% + : Phoenix, AZ 3,638 63.7 (1.84) 39% Wichita, KS 598 38.9 (0.85) 25% + : Minneapolis-St. Paul, MN 3,155 160.8 (4.66) 26% Little Rock, AR 577 45.2 (1.64) 26% + : St. Louis, MO 2,689 190.6 (5.38) 18% Columbia, SC 577 60.0 (2.10) 23% + : Tampa-St, FL 2,649 102.7 (2.09) 26% Charleston, SC 569 59.6 (1.74) 19% + : Denver-Boulder, CO 2,604 99.9 (1.40) 32% Des Moines, IA 564 21.3 (0.92) 40% + : Portland, OR 2,352 48.6 (1.35) 41% Spokane, WA 540 24.5 (0.63) 28% + : Cleveland, OH 2,134 170.6 (3.34) 24% Madison, WI 520 93.6 (3.02) 22% + : Charlotte, NC-SC 2,127 67.1 (1.96) 38% Augusta, GA 510 30.9 (0.60) 24% + : Sacramento, CA 2,100 47.6 (1.30) 42% Ft. Wayne, IN 509 37.8 (1.35) 27% + : Salt Lake City, UT 1,924 58.1 (1.19) 26% Lexington-Fayette, KY 495 36.8 (1.59) 35% + : San Antonio, TX 1,900 75.0 (2.27) 24% Chattanooga, TN 471 41.5 (2.53) 29% + : Kansas City, MO-KS 1,871 152.5 (2.87) 19% Boise, ID 469 46.2 (3.73) 30% + : Las Vegas, NV 1,752 47.7 (1.49) 32% Jackson, MS 453 18.6 (2.03) 59% + : Milwaukee-Racine, WI 1,713 74.6 (1.27) 25% Eugene-Springfield, OR 439 27.4 (1.29) 31% + : Orlando, FL 1,686 42.4 (1.77) 41% Reno, NV 400 99.7 (1.64) 15% + : Columbus, OH 1,685 70.2 (1.53) 30% Shreveport, LA 359 19.8 (4.25) 92% + : Indianapolis, IN 1,602 86.8 (2.32) 26% Fayetteville, NC 337 38.1 (2.48) 46% + : Norfolk, VA 1,583 196.8 (4.64) 17% Springfield, MA 336 20.8 (0.87) 55% + : Nashville, TN 1,342 40.5 (1.84) 38% Macon, GA 276 34.4 (2.29) 26% + : Greensboro-Winston, NC 1,329 53.5 (2.34) 32% Binghamton, NY 255 37.5 (1.51) 27% + : New Orleans, LA 1,294 91.2 (2.44) 24% Lubbock, TX 248 57.7 (1.98) 18% + : Memphis, TN 1,278 53.2 (1.82) 30% Odessa-Midland, TX 231 21.4 (0.99) 27% + : Jacksonville, FL 1,271 66.1 (1.64) 29% Fargo-Moorhead, ND-MN 200 48.6 (2.42) 25% + : Oklahoma City, OK 1,268 75.6 (1.35) 25% Medford-Ashland, OR 184 27.7 (0.90) 28% + : Buffalo-Niagara Falls, NY 1,150 141.5 (3.63) 19% Duluth-Superior, MN-WI 159 43.3 (0.79) 20% + : Louisville, KY 1,100 92.9 (2.36) 21% Parkersburg-Marietta, WV-OH 157 31.7 (1.41) 21% + : Richmond, VA 1,066 55.3 (1.47) 28% Abilene, TX 149 23.0 (1.14) 26% + : Birmingham, AL 1,030 85.8 (2.50) 24% Eau Claire, WI 149 31.6 (2.77) 28% + : Honolulu, HI 938 78.2 (2.39) 15% Williamsport, PA 130 31.0 (1.13) 23% + : Albany, NY 909 113.9 (3.18) 16% Monroe, LA 124 14.2 (1.49) 64% + : Grand Junction, CO 902 24.5 (0.67) 24% Sioux City, IA 118 26.1 (0.96) 24% + : Tucson, AZ 870 41.1 (0.93) 27% San Angelo, TX 104 26.4 (1.36) 16% + : Grand Rapids, MI 864 37.9 (0.79) 38% Bismarck, ND 99 32.8 (1.65) 22% +blank : +blank : +text : Table 2.7: Estimated marginal cost (in dollars per minute of broadcasted advertising) and profit margins (before + : subtracting the fixed cost) for a chosen set of markets +blank : +blank : +blank : +blank : +footer : 28 +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 29 +blank : +blank : +table : Consumer Advertiser Mean price + : Average ad load Advertising minutes + : surplus surplus index + : Impact of + : ownership change and 6.6pdm -6.4pdm -158.3m -2,491min + : +0.60% + : format switching +1.3% -12.6% -16.3% -1.5% + : No ad adjustment + : Impact of -1.9pdm 1.6pdm -146.1m -9,838min + : +2.09% + : ad adjustment -0.4% +3.6% -18.0% -5.9% + : Total impact of + : ownership change 4.7pdm -4.8pdm -304.4m -12,329min + : +2.67% + : format switching and +0.9% -9.5% -31.4% -7.3% + : ad adjustment +blank : +text : Table 2.8: Counterfactuals for all markets +blank : +heading : 2.7 Counterfactual experiments +text : In this section I investigate the impact of consolidation on listener and advertiser + : welfare. First, I investigate the changes in the surplus of listeners and advertisers. In + : particular, I calculate how much market power was exercised on both of those groups. + : Second, I decompose market power into a variety component and extra market power + : that is manifested in changes in quantity supplied. + : Before performing counterfactual calculations, consider descriptive relationships + : between concentration and prices. First, I regressed market Price Per Rating Point + : on a market’s HHI, including market fixed effects. I find that higher concentration is + : correlated with higher prices in the advertising market, suggesting that radio station + : owners are exercising some amount of market power on advertisers. Second, I re- + : gressed total advertising supplied on the market’s HHI with market dummies. Here I + : get a coefficient of 1.65(0.3). This is evidence of market power in the listener market. + : Because market power appears to be present in both market segments, I cannot defi- + : nitely conclude who had more surplus extracted by radio station owners if I just use + : quantities and prices. In the next subsection I present the structural counterfactuals + : that answer this question. +blank : +blank : +heading : 2.7.1 Impact of mergers on consumer surplus +text : To isolate the impact of the Telecom Act on a surplus division between advertisers + : and listeners, I perform a counterfactual in which I recompute new equilibrium ad + : quantities under the old 1996 ownership structure and 1996 formats. This calculation +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 30 +blank : +blank : + : Consumer Advertiser Mean price + : Average ad load Advertising minutes + : surplus surplus index + : Impact of + : ownership change and 11.7pdm -5.4pdm -118.1m -737min + : +1.34% + : format switching +2.5% -17.3% -15.8% -1.0% + : No ad adjustment + : Impact of 1.2pdm -2.2pdm -119.4m -8,216min + : +5.66% + : ad adjustment +0.3% -8.4% -19.0% -11.7% + : Total impact of + : ownership change 12.9pdm -7.5pdm -237.5m -8,953min + : +6.99% + : format switching and +2.8% -24.2% -31.8% -12.6% + : ad adjustment +blank : +text : Table 2.9: Counterfactuals for small markets (less than 500k people) +table : Consumer Advertiser Mean price + : Average ad load Advertising minutes + : surplus surplus index + : Impact of + : ownership change and 2.6pdm -6.0pdm -1.0m -835min + : +0.01% + : format switching +0.5% -11.0% -12.8% -2.0% + : No ad adjustment + : Impact of -4.4pdm 4.6pdm 0.7m 3,081min + : -0.02% + : ad adjustment -0.8% +9.5% +9.9% +7.7% + : Total impact of + : ownership change -1.8pdm -1.4pdm -0.3m 2,245min + : -0.01% + : format switching and -0.3% -2.5% -4.2% +5.5% + : ad adjustment +blank : +text : Table 2.10: Counterfactuals for large markets (more than 2,000k people) +blank : +text : is motivated by the fact that in 1996 many markets were at their ownership caps. + : The total impact of consolidation on advertiser and listener welfare is presented + : in the last row of Table 2.8. It turns out that mergers decrased total ad quantity + : by roughtly 14 thousand minutes. That resulted in lowering average ad exposure + : by 4.8 persons-day-minutes (pdm), which is about 10% of the total ad load. The + : changes translated to about a 4.7 pdm increase in consumer welfare. Because we + : do not observe dollar prices in the listenership market we cannot compute the dollar + : value of this compensating variation. However, we can compute a rough estimate + : using the prices for the satellite radio. If we assume people buy satelite radio just + : to avoid advertising, we get a rough estimate of 1.5 cents per minute, or 730million + : dollars for each person-day-minute per year. The total effect would amount to $3.5b. + : This is of course a very loose upper bound on the overall welfare gain, however if + : make a conservative assumption that only 10% of the value of satellite radio is lack + : of advertising, we get $350m. + : For advertisers, a decrease in quantity supplied leads to about 2.57% increase in +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 31 +blank : +blank : +blank : +text : per-listener prices, or a $300m decrease in advertiser surplus. I therefore conclude + : that the Telecom Act lead to a reallocation of surplus from advertisers to listenerss. + : Moreover, because the gain by listeners ($350m) is larger than the surplus lost by + : advertisers, I find that the Act created new surplus. This increase can be explained + : by the fact that listeners are more annoyed by ads than the value of an ad to the + : advertisers. + : A deeper story can be told by looking seperately at small versus large markets. + : As mentioned in the previous section, radio stations have considerable control over + : prices in small markets, and no control in the large markets. Motivated by this fact, + : I present counterfactuals for markets with less than 0.5 population and more than + : 2m population. In smaller markets (see Table 2.9), stations contract advertising to + : exercise market power on advertisers. They supply more than 10,000 minutes less of + : advertising. That translates into a 7.3pdm decrease in ad exposure, which increases + : consumer surplus by 11.6pdm. However, prices rise by 6.4%, and cause a $230m + : loss in advertiser surplus. On the other hand in large markets (see Table 2.10) firms + : supply more than 2,000 extra minutes of advertising, which lowers consumer surplus + : by almost 2pdm. On balance, this does not affect advertiser surplus. I conclude that + : listeners gained form the Telecom Act only in small markets. +blank : +blank : +heading : 2.7.2 Effects of product variety and market power +text : Berry and Waldfogel (2001) suggest that the negative effects of ownership consolida- + : tion on listeners might be mitigated by format switching. They find that post-merger + : repositioning results in spatial competition leading to more variety, which they as- + : sume is beneficial for the listeners 4 . To quantify this effect, I compare surpluses + : computed imposing 1996 ownership and formats with surpluses computed imposing + : actual ownership and formats without ad quantity adjustments. That is, I fix ad + : quantities computed with 1996 ownership and formats. The results of this experi- + : ment are presented in the first row of Table 2.8. It turns out that if I do not account + : for quantity changes, the assertion of Berry and Waldfogel (2001) is true. In this +footnote : 4 +text : Similar results obtained using direct analysis of station playlists can be found in Sweeting (2008). +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 32 +blank : +blank : +blank : +text : case, listeners have a 1.3% larger surplus (about 6.6pdm) after consolidation and for- + : mat switching. Listener surplus grows because of two factors: increased variety and + : decreased advertising exposure. The latter decreased even though I keep number of + : ad minutes fixed. However, in the real world, repositioning changes firms’ incentives + : to set ad quantity, because it softens competition in the advertising market. The im- + : pact of quantity readjustments is presented in the middle row of Table 2.8. It turns + : out that both listeners and advertisers are worse off due to quantity adjustments. + : Listeners lose 1.9pdm and advetisers lose additional $150m in surplus. +blank : +blank : +heading : 2.8 Robustness analysis +text : This section examines the robustness of my advertising model to different assumptions + : about competition among station formats. This step is motivated by the fact that + : the data concerning advertiser deals is incomplete. I deal with the incompleteness by + : proposing a stilyzed decision model for advertisers that uses publicly available data + : to predict substitution patterns between formats. These patterns directly detemine + : the market power of stations over advertsers, and can potentially alter the results of + : counterfactual experiments. + : To investigate the robustness of the results, I reestimated the model under two + : alternative assumptions. The first scenario represents the extreme situation in which + : formats compete only between themselves. In particular, suppose that advertiser + : types get utility from only one particular format. In this case, equation (2.6) has + : ωf f = 1 and ωf f 0 = 0 if f 6= f 0 . The second scenario represents another extreme in + : which formats are perfect substitutes, i.e., there is only one type of advertiser who + : values all formats in the same way. Formally this means that ωf f 0 = 1/8, because + : there are 8 possible formats. The estimated model is in a sense in-between the these + : extreme alternatives, because it assumes that formats are imperfect substitutes. + : Estimates of the inverse demand advertising slopes are presented in Table 2.11. + : The estimates show that the baseline model lies between the two extremes. When we + : assume oligopoly within a format, the estimated slope parameter θ2L is smaller than + : the one in the baseline model. On the other hand in the perfect substitutes model, +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 33 +blank : +blank : +table : Market population less than .5m between .5m and 1.5m more than 1.5m + : Baseline model 1.34 (0.046) 0.35 (0.026) 0.00 (0.008) + : Oligopoly within format 1.07 (0.036) 0.28 (0.061) 0.02 (0.009) + : Perfect substitutes 1.44 (0.035) 0.32 (0.030) 0.01 (0.009) +blank : +text : Table 2.11: Slope of the inverse demand for ads θ2A , by market size +table : Consumer Advertiser Mean price + : Average ad load Advertising minutes + : surplus surplus index + : 4.7pdm -4.8pdm -304.4m -12,329min + : Baseline model +2.67% + : +0.9% -9.5% -31.4% -7.3% + : 4.4pdm -4.5pdm -253.4m -9,056min + : Oligopoly within format +1.12% + : +0.8% -9.0% -31.3% -5.6% + : 4.9pdm -5.3pdm -314.7m -16,648min + : Perfect substitutes +2.57% + : +0.9% -10.3% -32.7% -9.0% +blank : +text : Table 2.12: Robustness of counterfactuals +blank : +text : the estimated slope tends to be higher. Despite the fact that there are statistical + : differences between the different models, the main qualitative assertion, that stations + : have more power in smaller markets, still holds. In order to assess the economic impli- + : cation of those differences, I recomputed the estimated profit margin under different + : models. It turns out that the model with format oligopoly predicts on average a 2.4% + : higher profit margins than the baseline model. Conversely the model with perfect + : substitutes predicts 2.1% lower profit margin. + : To draw final conclusions about the strength of the assumption about weights, I + : recomputed the main counterfactual using the alternative models. The results are + : presented in Table 2.12. The baseline again lies between the new counterfactuals. + : There is no qualitative change in the results. Moreover the percentage changes in + : consumer and advertiser surplus are almost the same. Consequently, I conclude that + : the results of the paper are not sensitive to changes in the assumption about substi- + : tution between formats. +blank : +blank : +heading : 2.9 Conclusion +text : In this paper I analyze mergers in two-sided markets on the example of the 1996-2006 + : consolidation wave in U.S. radio industry. The goal of this study is to describe and + : quantify how mergers in the two-sided market differ from a differentiated product +header : CHAPTER 2. MERGERS IN TWO-SIDED MARKETS 34 +blank : +blank : +blank : +text : oligopoly setting. I make two main contributions. First, I recognize the fact two- + : sided markets consist of two types of consumers, who may be affected by the merger in + : different ways. For example, if extra market power causes the radio station to increase + : advertising, it will benefit consumers but hurt advertisers. Second, I disaggregate the + : impact of a merger on consumers into changes in the variety of available products + : and changes in supplied quantity of ads. + : Radio is an important medium in the U.S., reaching about 94% of Americans + : twelve years old or older each week. Moreover, the average consumer listens to about + : 20h of radio per week and between 6am and 6pm more people use radio than TV + : or print media5 . In 1996 the Telecommunication Act deregulated the industry by + : raising local ownership caps. This deregulation caused a massive merger wave, that + : reshaped the ownership structure, by moving from family based ownership into more + : corporate structures. I estimate that this consolidation raised consumer surplus by + : 1%, but lowered advertiser surplus by $300m. I find that the mergers created extra + : variety that increased listener welfare by $1.3%. On the other hand they softened + : competition and decreased advertiser welfare by $147m per year. Subsequent ad + : quantity adjustments led to a 0.3% decrease in listener welfare (with the variety + : effect it totals to the 1% increase) and an additional $153m decrease in advertiser + : welfare (with the variety effect it totals $300m). +blank : +blank : +blank : +blank : +footnote : 5 +text : Source: A.Richter (2006) +header : Chapter 3 +blank : +text : Estimation of cost synergies from + : mergers without cost data: + : Application to U.S. radio +blank : +heading : 3.1 Preface +text : This chapter develops a new way to estimate cost synergies from mergers without + : using actual data on cost. The estimator uses a structural model in which companies + : play a dynamic game with endogenous mergers and product repositioning decisions. + : Such a formulation has several benefits over the widespread static merger analysis. + : In particular, it corrects for sample selection of more profitable mergers and captures + : follow-up mergers and post-merger product repositioning. + : The framework is applied to estimate cost efficiencies after the deregulation of + : U.S. radio in 1996. The procedure uses the data on radio station characteristics + : and numerous acquisitions, without explicit need for cost data. It turns out that + : between 1996 and 2006 additional ownership concentration generated $2.5b per-year + : cost savings, which is about 10% of total industry revenue. +blank : +blank : +blank : +blank : +footer : 35 +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 36 +blank : +blank : +blank : +heading : 3.2 Introduction +text : The extent to which a potential merger generates cost efficiencies is often mentioned + : by managers as a major motivation to merge. Moreover, potential fixed cost savings + : generated by a merger are recognized by the Horizontal Merger Guidelines as a fac- + : tor that can provide consumers with direct price-related as well as non-price-related + : benefits. Thus, for antitrust purposes one should evaluate cost savings in addition + : to measuring the decrease in competition. However, this approach is rarely used in + : practice, because in most cases reliable cost data are unavailable. This paper pro- + : vides a solution to this problem, by proposing a method to estimate cost synergies + : without using any data on cost. This method requires only panel data on the own- + : ership structure, product characteristics, and prices and quantities, information that + : in most cases is easily accessible. + : Evaluating the underlying causes of ownership consolidation requires a dynamic + : model in which mergers are endogenous. However, most past empirical work analyzed + : mergers in a static framework and treats market structure as given. Papers by Nevo + : (2000), Pinkse and Slade (2004), Ivaldi and Verboven (2005) exogenously impose + : changes in market structure on a static equilibrium model and calculate counterfactual + : changes in prices and welfare. These models are very useful in addressing the short + : run impacts of mergers but do not account for changes in market structure that + : might happen as a result of a merger. Benkard, Bodoh-Creed, and Lazarev (2008) + : evaluate the longer run effects of a merger on market structure, but still treat it + : as an exogenous one-time event. Neither of these approaches allows for estimating + : the supply side determinants of mergers, such as cost synergies. Furthermore, the + : assumption that mergers are exogenous may create a selection bias that results in + : overestimating the cost synergies (we might pick up other unobserved components + : correlated with the propensity to merge). Furthermore, recent models assume away + : follow-up mergers and post-merger repositioning of products. + : To address these issues, I propose a dynamic model in the spirit of Gowrisankaran + : (1999) in which mergers and product positioning are endogenous and are assumed to + : happen sequentially. Such an approach enables me to estimate the cost efficiencies +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 37 +blank : +blank : +blank : +text : of consolidation without any data on cost. It also eliminates the shortcomings men- + : tioned earlier, because it incorporates the dynamic processes directly into the model. + : Moreover, endogenizing mergers allows for correction of sample selection by using a + : procedure in the spirit of Heckman (1979), adjusted for a dynamic game environment. + : The model is subsequently applied to analyze ownership consolidation in the U.S. + : radio industry. The Telecommunications Act of 1996 increased local-market radio + : station ownership caps, triggering an unprecedented merger wave that had the effect + : of eliminating many small and independent radio owners. From 1996 to 2006, the + : average Herfindahl-Hirschman Index (HHI) in local radio markets grew from 0.18 + : to 0.26, the average number of owners in the market dropped from 16.6 to 12.4, + : and the average number of stations owned grew from 1.6 to 2.3. Such dramatic + : changes to the market structure have raised concerns about anti-competitive aspects + : of the deregulation (Leeper (1999), Drushel (1998), Klein (1997)). After estimating + : the model using the method of Bajari, Benkard, and Levin (2004), I find that the + : main incentives to merge in radio come from the cost side. Total cost side savings + : amount to $2.5b per year, constituting about 10% of total industry revenue. Such + : cost synergies are an order of magnitude higher than the anti-competetive effects of + : these mergers identified by Jeziorski (2010). Moreover, the fact that consolidation + : leads to substantial cost side synergies leads me to conclude that the Telecom Act + : made radio advertising more competitive against other media, such as TV or the + : Internet. + : To my knowledge, Gowrisankaran (1999) is the only applied paper that uses a + : dynamic framework to endogenize mergers. His analysis argued that merger dynamics + : are very important. The main drawback of his analysis is that it was never fit to + : real data. This was due in part to the complexity of his model and in part to + : the lack of a good dataset. To solve the complexity problem, I utilize the latest + : developments in the dynamic-games literature. These developments enable us to + : estimate very complicated models without explicitly solving them (Bajari, Benkard, + : and Levin (2004)). This paper also contributes to empirical literature on demand + : and cost curve estimation (this started with Rosse (1970) and Rosse (1967)), by + : accounting explicitly for the demand side incentives to merge. On the technical side, +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 38 +blank : +blank : +blank : +text : my model shares some similarities with Sweeting (2007). I concentrate on questions + : about incentives to merge and the impact of consolidation on welfare, while Sweeting + : focuses mainly on estimates of the format switching cost. My analysis also extends + : his model by adding a model of ad quantity choices and endogenous mergers. Another + : paper on a similar topic is O’Gorman and Smith (2008). They use a static oligopoly + : model to estimate the cost curve in radio. They find that the fixed cost savings when + : owning two stations is bounded between between 20% and 50% of per-station costs + : (I estimate this number to be 20%). I supplement their estimates by accounting for + : selection bias, follow-up mergers and post-merger repositioning as outlined above. + : This chapter is organized as follows. Section 2 contains a flexible, structural + : merger model that can applied to many industries. The estimation procedure is + : discussed in Section 3. Section 4 describes the application of the framework to analyze + : the merger wave in the U.S. radio industry. Section 5 concludes the paper. +blank : +blank : +heading : 3.3 Merger and repositioning framework +text : This section presents the dynamic oligopoly model of an industry with differentiated + : products in the spirit of Ericson and Pakes (1995). The industry is modeled as a + : dynamic game and the players are companies holding portfolios of different products + : (brands). The modeling effort emphasizes the actions of companies changing the + : profolio of owned products, specifically rebranding and acquisitions. The model is + : general enough to encompass a number of different industries and types of competi- + : tion, by allowing for a large range of different single-period profit functions and cost + : structures. +blank : +blank : +heading : 3.3.1 Industry basics +text : The industry is composed of M different markets that operate in discrete time over + : an infinite horizon. The payoff relevant market characteristics at time t are fully + : characterized by a set of covariates dmt ∈ D that include demand shifters. In each + : market m, there are up to Km operating firms and up to Jm active products. Let oj ∈ +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 39 +blank : +blank : +blank : +text : Km be the owner of the product j. I assume that each product j ∈ Jm is characterized + : by a triple stj = (fjt , ξjt , otj ). In particular, fjt ∈ F is a discrete characteristic, and + : ξjt ∈ Ξ is a continuous characteristic of the product. The state of the industry at the + : beginning of each period is therefore a duple (st , dt ) ∈ S × D. + : To simplify the further exposition define Okt to be the number of products owned + : t + : by the firm k, and O−k to be the number of products owned by its competitors. +blank : +blank : +heading : 3.3.2 Players’ actions +text : Firms can undertake two types of actions: product acquisitions and product repo- + : sitioning. I assume that acquisitions take place first and the results are common + : knowledge before the firms commence with repositioning. + : In general, the product acquisition process can be very complicated. Firms can + : acquire any subset of products owned by competitors, and multiple firms can bid to + : acquire the same product. Therefore, the most general model of this process is likely to + : be intractable both analytically and numerically. Additionally, the model of mergers + : without additional structure is likely to generate multiple equilibria, which will sig- + : nificantly complicate its estimation. To solve these problems, I follow Gowrisankaran + : (1999) and I assume that the station acquisition process is sequential. Owners move + : in a sequence specified by a function A : st 7→ i, where i is a permutation of the active + : owners’ index {1, . . . , K}. In addition, for notational purposes, I set i(K +1) = K +1. + : t + : Let ωi(k) be the state of the industry observed by the k-th mover in the merger + : t + : process, before making acquisition decisions. ωi(1) is set to be equal to st . Addi- + : tionally, every player observes a set of acquisition prices for all stations owned by + : competitors + : Pkt = {φtkj : otj 6= k} +blank : +text : These prices are the outcomes of a bargaining process that is only a function of the + : t t + : current observable state ωi(k) . This assumption holds if ωi(k) is the only payoff relevant + : variable for both the acquirer and the acquiree and the prices are determined by a + : Nash Bargaining Solution. + : In addition to prices, the potential buyer observes a set of additive payoff/cost +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 40 +blank : +blank : +blank : +text : shocks from acquiring any competitor owned product φtk = {φtkj : otj 6= k} that is his + : private information. A player’s i(k) action involves specifying which subset of stations + : are to be acquired. I restrict attention to Markov strategies, so the acquisition policy + : is a mapping +math : t t + : ak : (ωi(k) , φtk , Pkt , dt ) 7→ {0, 1}O−k + : t +text : After the decisions are made, a new ownership ωi(k+1) is determined, and it becomes + : common knowledge. Player a(k + 1) proceeds with acquisitions, or if there are no + : move active players, the game moves to product repositioning. + : A product repositioning involves decisions about changing discrete characteristics + : fjt of owned products, in exchange for paying a switching cost C(fj , fjt+1 ). It is, + : similarly to acquisitions, a sequential process, and it is assumed that firms proceed + : according to the same sequence i(k)1 . + : The first mover i(1) in the repositioning process conditions his decision on the + : t + : state of the industry after the acquisitions, i.e., the observable state ω̃i(1) is equal + : t + : to ωi(K+1) . In the same way the k-th mover i(k) observers the repositionings done + : t + : by all the previous movers. This information is summarized in ω̃i(k) . In addition + : t + : to observing the state ω̃i(k) , the k-th mover observes payoff/cost shocks for all the + : products of any potential type ψkt = {ψkjf + : t + : : otj = k, 1 ≥ f ≥ F }. The product + : repositioning policy is a Markov strategy given by the mapping +blank : +math : t t + : bk : (ω̃i(k) , ψkt , dt ) 7→ F Ok +blank : +text : t + : When the choices of player i(k) are made a new industry state ω̃i(k+1) becomes a + : common knowledge. + : After repositioning the new industry state (st+1 , dt+1 ) is determined. st+1 is con- + : t + : structed by combining ω̃i(K+1) with the values of a new continuous product charac- + : teristic ξ t+1 The following assumptions restrict the dynamics of ξ. +blank : +text : Assumption 3.3.1. ξjt evolves as an exogenous Markov process, for example +blank : +math : ξjt = ρξjt−1 + ζt (3.1) +footnote : 1 +text : This assumption is made for the simplicity of exposition and might be easily relaxed. +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 41 +blank : +blank : +blank : +text : where ζt is a mean zero IID random variable. +blank : +text : Moreover, market covariates are also assumed to be exogenous and Markov +blank : +text : Assumption 3.3.2. dt evolves as an exogenous Markov process. +blank : +text : These assumptions are made for simplicity of estimation. They could be poten- + : tially relaxed if more data is available. For example, if ξ is a product quality, one + : could assume that it is also a dynamic choice variable and estimate it directly from + : the observed investment. + : When the new industry state is (st+1 , dt+1 ) realized firms then play a static com- + : petition game that yelds profits given by π̄k (st+1 , dt ). +blank : +blank : +heading : 3.3.3 Payoffs and equilibrium +text : Given the realizations of (st , st+1 , P t , ψ t , φt , dt ) the per-period payoff for player k is + : given by the equation +math : X + : πk (st , st+1 ,P t , ψ t , φt , dt ) = π̄k (st+1 , dt ) − F (stk ) + (φtkj − Pkj + : t + : )+ + : j:otj 6=k,ot+1 + : j =k + : X X h i (3.2) + : t+1 t+1 + : + Pott+1 j + t t t + : ψkjf t+1 − I(fj 6= fj )C(ff , fj ) + : j j + : j:otj =k,ot+1 + : j 6=k j:ot+1 + : j =k +blank : +blank : +text : where F (stk ) is the fixed cost of owning portfolio stk , and π̄k is a one-shot profit from + : the portfolio. + : Let g = (a1 , . . . , aK , b1 , . . . , bK ) be a Markov strategy profile. It can be shown that + : this profile and an initial condition (s, d) determine the unique, controlled Markov + : process over states, acquisition prices P , payoff shocks ψ and φ, and market covariates +math : d + : P(g, s, d) ∈ ∆(S × P × Ψ × Φ × D × T ) +blank : +text : where T is a time horizon, and ∆ is a set of probability measures. P is therefore a + : discrete time stochastic process on S × P × Ψ × Φ × D. This process is also supplied + : with a filtration, such that the strategy profile g is measurable. +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 42 +blank : +blank : +blank : +text : Each owner is maximizing the expected discounted sum of profits taking the strate- + : gies of opponents g−k as given. The value function for player k is defined as +blank : +math : ∞ + : X + : Vk (s, d|gk , g−k ) = EP(g,s,d) β t πk (st , st+1 , P t , ψ t , φt , dt ) (3.3) + : t=0 +blank : +blank : +text : It is assumed that the markets are in a Markov Perfect Equilibrium, i.e., firms choose + : strategy profile g∗ , such that for all k +blank : +math : Vk (s, d|g∗k , g∗−k ) ≥ Vk (s, d|gk , g∗−k ) ∀gk . (3.4) +blank : +text : For simplicity, I restrict my attention to symmetric equilibria. The next section + : describes the estimation procedure. +blank : +blank : +heading : 3.4 Estimation +text : Consider parameterizations of the fixed cost F (stk |θF ) and the switching cost + : C(fjt , fjt+1 |θC ). This section outlines a procedure, based on Bajari, Benkard, and + : Levin (2004), to obtain consistent estimators of θF and θC without using direct data + : on cost. + : The procedure has two stages. The fist stage infers equilibrium behavior from the + : data on one or a set of similar industries. The second stage estimates the cost param- + : eters for a particular industry by imposing the dynamic game equilibrium inequalities + : 3.4. The following subsection describes the data needed for this procedure to work. +blank : +blank : +heading : 3.4.1 Data +text : Consider an industry, or a set of similar industries, operating in M markets over the + : discrete time span T . Data is given by the set X = {xtm : 1 ≤ m ≤ M, 1 ≤ t ≤ T }. + : Each point in the data xtm describes the state of the industry at the beginning of + : the period stm = (f tm , ξ tm , otm ), market covariates/demand shifters dtm , and a set of + : transaction prices P mt . The data does not have to contain any direct information on +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 43 +blank : +blank : +blank : +text : the cost. This is convenient since most of the data on cost suffers from accounting + : issues. Therefore direct cost estimates from the data might be unreliable. + : To facilitate the inference process a standard assumption about the data gen- + : erating process is made: that it is generated by a single MPE strategy profile g∗ . + : Crucially, the dataset needs to contain a reasonable amount of within market acqui- + : sitions and repositioning to allows it to identify equilibrium strategies. Sometimes it + : is possible to obtain such datasets within one industry (see U.S. radio in the appli- + : cation), however for most industries such datasets are unavailable. In this case, it is + : possible to pool similar industries to construct one dataset. To make this work one + : needs a slightly stronger assumption that equilibrium behavior is the same across the + : pooled industries. + : The transaction prices are helpful but not necessary to identify the cost parame- + : ters. Estimation is possible without them but it requires more assumptions about the + : bargaining process during the acquisition, as well as much more computing power. + : The extra steps needed to proceed without the prices are mentioned in Appendix B.1. + : In order to simplify the exposition all state variables are assumed to be observed. + : However, the procedure also applies to problems in which some payoff relevant in- + : formation is unobserved to the econometrician. In many cases one can infer the + : unobserved state variable from a static estimation of the one-shot profit function π̄. + : One example of such a case is Berry, Levinsohn, and Pakes (1995) estimator, which + : uses differences of static market shares to identify unobserved product quality. More- + : over, there are numerous ways to proceed in case one cannot directly infer all the + : latent state variables. For example, one could supply the procedure from this chapter + : with an EM algorithm proposed by Arcidiacono and Miller (2010). +blank : +blank : +heading : 3.4.2 Policy estimation +text : For any strategy profile +math : g = (a1 , . . . , aK , b1 , . . . , bK ) +blank : +math : let ProbM R + : k (ak |ωk , dk ), and Probk (bk |ω̃k , dk ), be the probabilities of taking acquisition + : and repositioning actions. The former is a probability measure on {0, 1}O−k , and the +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 44 +blank : +blank : +blank : +text : latter on {1, . . . , F }Ok . They are constructed by integrating out unobservable payoff + : shocks φ and ψ. The goal of this subsection is to provide a procedure that allows us + : to obtain the estimates of these probability measures. This procedure leverages on + : the sequentiality assumptions made in the previous section. + : The first step of the procedure is constructing an auxiliary dataset using a sequen- + : tial structure of the acquisition and repositioning process. For each t, the predefined + : sequence of player moves i = I(st ) specifies a mapping +blank : +math : (st , st+1 ) 7→ (ωi(1) , . . . , ωi(K) , ω̃i(1) , . . . ω̃i(K) ) +blank : +text : This mapping is used to construct 3 sets. The first set describes the acquisition + : dynamics +blank : +math : Y1 = {(ωktm , dtm , atm + : k ) : 1 ≤ k ≤ K, 1 ≤ m ≤ M, 1 ≤ t ≤ T } +blank : +blank : +text : where atm + : k is a vector of zeros and ones that indicates acquisition decisions for player + : k. The second set describes acquisition prices +blank : +math : Y2 = {(ωktm , dtm , Pktm ) : 1 ≤ k ≤ K, 1 ≤ m ≤ M, 1 ≤ t ≤ T } +blank : +text : where Pktm is a vector of prices for all acquisitions of player k. The last set describes + : the repositioning +blank : +math : Y3 = {(ω̃ktm , dtm , Fkmt ) : 1 ≤ k ≤ K, 1 ≤ m ≤ M, 1 ≤ t ≤ T } +blank : + text : where Fkmt is a vector of chosen characteristics for products owned by firm k. + : Set Y1 is used to estimate the acquisition probability distribution ProbM + : k as a + : function of (ω, d). In a perfect world, one would like to employ a form of non- + : parametric multi-dimensional discrete choice estimator. However, in practice, the + : researcher is likely to face two problems: the large dimensionality of covariates (ω, d) + : and the large dimensionality of the ProbM + : k support (due to a big number of active + : products/companies that can be acquired). +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 45 +blank : +blank : +blank : +text : The solution to the first problem is to employ a flexible parametric form +blank : +math : M + : [ k (ak |ωk , dk , θM ) + : Prob +blank : +text : that exhausts most of the information in the data. The asymptotics of such an + : estimator are similar to the non-parametric estimators in which the dimensionality + : of pseudo-parameters θM grow as the dataset becomes large. + : The second problem is more severe and in most cases cannot be solved without + : additional assumptions. The following examples suggest different possible approaches. +blank : +text : Example 3.4.1 (One acquisition per period). If the acquisitions in the data tend to + : be rare, one could potentially assume that only one acquisition per owner is allowed + : each period. This reduces the decision space to only one dimension and enables direct + : application of any discrete choice model (for example logit or probit) on the data set + : Y1 . +blank : +text : The second example suggests how to deal with multiple acquisitions +blank : +text : Example 3.4.2 (Independent acqusitions). In the case where the acquisition deci- + : sions are uncorrelated conditional on ωk and dk one could employ a discrete choice + : regression directly on Y1 , fixing ωktm for all decisions in ãtm + : k . +blank : +blank : +text : The next solution makes more assumptions about the structure of the acquisition + : decision making within the firm. +blank : +text : Example 3.4.3 (Sequential acqusitions). Suppose that the acquisition decisions are + : made in a sequence, i.e., after observing ψj for a particular product, the firm decides + : about its acquisition without looking at the payoff shocks ψ for other stations. In + : this case one could further expand dataset Y1 to incorporate the sequence of decisions + : within the firm. Because of the additive structure of payoffs and the fact that ψj are + : IID, one could consistently estimate ProbM + : k by using a discrete choice estimator on + : the extended dataset. +blank : +text : If one were to observe the acquisition prices one could estimate the pricing function + : P (ωkst ) directly from the dataset Y2 . This could be achieved by employing the flexible +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 46 +blank : +blank : +blank : +text : parametric interpolation2 . + : When estimating the repositioning probabilities ProbR + : k one faces similar problems, + : but additionally one has to deal with multinomial vs. binomial choice. The three + : examples of solutions to that problem presented previously also apply here. + : Additionally, one could endogenize the continuous characteristic ξ and estimate it + : as a function of the state space using the methods presented in Bajari, Benkard, and + : Levin (2004). Depending on the interpretation of ξ, this might involve an additional + : model. In this paper however, ξ t as well as dt are treated as exogenous and Markov. + : The transition in this case can be estimated as a flexible parametric auto-regressive + : process. + : In the next subsection I describe a second stage of the cost function estimator + : that uses the estimators of equilibrium policy and the transition of ξ and dt obtained + : in the first step above. +blank : +blank : +heading : 3.4.3 Minimum distance estimator +text : For the second stage the parameters of the fixed cost θF and repositioning cost θR are + : estimated using a minimum distance estimator. The estimator is constructed using + : the MPE inequalities (3.4). The remainder of this section describes how I obtain + : estimates of the value functions in those inequalities. + : The value function Vk (defined on the equation (3.3)) can be separated into four + : parts. +math : Vkt = Atk + θφ Bkt + θψ Ckt + Dkt +blank : +math : where ∞ + : X X X + : Atk =E β r−t π̄k (st , dt ) + Porr+1 j − r + : Pkj + : j + : r=t j:orj =k,or+1 6=k j:orj 6=k,or+1 =k + : j j +blank : +footnote : 2 +text : Sometimes the dataset on prices is sparse, i.e., one does not observe prices for every deal. In + : this case more simplifying assumptions about the pricing process are needed. +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 47 +blank : +blank : +blank : +text : is the expected stream of advertising revenues, +math : ∞ + : X X + : Bkt =E β r−t φrkj + : r=t j:orj 6=k,or+1 =k + : j +blank : +blank : +blank : +text : is the expected stream of acquisition payoff/cost shocks, +blank : +math : ∞ + : X X + : Ckt = E β r−t t + : ψkjf r+1 + : j + : r=t j:or+1 =k + : j +blank : +blank : +blank : +text : is the expected stream of repositioning payoff/cost shocks, and +math :   + : ∞ + : X X + : Dkt = E β r−t F (srk |θF ) + 1(fjr+1 6= fjr )C(fjr , fjr+1 |θC ) + :   + : r=t j:or+1 =k + : j +blank : +blank : +blank : +text : is the expected stream of fixed costs and repositioning costs. The extra parameters + : θφ and θψ are needed because the first stage estimation requires normalization of the + : variances of φ and ψ. + : Accounting for Bkt in the simulation of profits from a merger takes care of selec- + : tion on unobservables, as apposed to the usual static approach to mergers. Given + : the merger decision atm tm tm + : jk , the contribution of unobserved profits is θφ E[φjk |ajk ]. Be- + : cause a company observes the payoff shock before making an acquisition, the merg- + : ers that occur are selected for high value of φtm + : jk When φ has zero mean, it is the + : case that E[φtm tm + : jk |ajk = 1] > 0. Failing to account for that (i.e. assuming that + : E[φtm tm tm + : jk |ajk = 1] = E[φjk ] = 0) would cause underestimation of profits from mergers + : and overestimation of fixed cost synergies 3 . The same point can be made about the + : selection on unobservables when repositioning products and inclusion of Ckt . + : Note that only the last part of Dkt depends on the parameters of interest θF and θC + : and the value function is linear θφ and θψ . Therefore, to compute the value function +footnote : 3 +text : When using any of the dynamic likelihood estimators proposed in the previous subsection and + : assuming that φ is a difference of two independent Type I extreme value random variables, E[φ|a = 1] + : can be reduced to − log(p) − 1−p p log(1 − p), where p is a probability of acquisition. +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 48 +blank : +blank : +blank : +text : for different parameter values one does not need to re-simulate the industry path + : (st , dt ); moreover, one does not need to recompute any of Atk , Bkt , Ckt 4 . This saves + : a large amount of processing power and makes the estimator feasible using today’s + : computers. + : Following the inequality (3.4), let Vkt be an equilibrium value function for player + : k, Vk (·|g∗k , g∗−k ). Additionally, define a suboptimal value function Ṽkt to be Vk (·|gk , g∗k ) + : for some off-equilibrium strategy gk . In equilibrium, I know that max{Ṽtk −Vkt , 0} = 0 + : for the true values of θM and θR . Thus, I define a minimum distance estimator +blank : +math : 1 X 1 + : (θ̂M , θ̂R ) = argmin max{Ṽktm − Vktm , 0} + : K × T × M k,t,m Atm + : k +blank : +blank : +text : According to the results in Bajari, Benkard, and Levin (2004) this estimator is con- + : sistent and asymptotically normal. This finishes the description of the estimator. An + : example of its application is contained in the next section. +blank : +blank : +heading : 3.5 Application +text : In this section, I describe how to use above framework to estimate merger synergies + : from ownership consolidation in the U.S. radio industry. In the next subsection I give + : a brief review of the industry. The second subsection presents the tailored version of + : the estimation algorithm. The last subsection presents and discusses the results. +blank : +blank : +heading : 3.5.1 Industry and data description +text : Radio is an important medium in the U.S., reaching about 94% of Americans twelve + : years old or older each week. Moreover, the average consumer listens to about 20 + : hours of radio per week and between 6am and 6pm more people use radio than TV + : or print media5 . There are about 13,000 commercial radio stations that broadcast + : in about 350 local markets nationwide. Before 1996, this industry had ownership +footnote : 4 +text : In most cases Atk is the hardest to compute because computing π̄ may involve solving a one-shot + : Nash equilibrium price or a quantity setting game. +footnote : 5 +text : Source: A.Richter (2006) +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 49 +blank : +blank : +table : # of active stations Old ownership cap New cap + : 45+ 4 8 + : 30-44 4 7 + : 15-29 4 6 + : 0-14 3 5 +text : Table 3.1: Change in the local ownership caps introduced by the 1996 Telecom Act. +blank : +blank : +text : limitations both nationally and locally, preventing big corporations from entering + : the market and thereby sustaining a large degree of family based ownership. This + : situation changed with the Telecom Act of 1996 which, among other things, raised + : the ownership caps in the local markets (see Table 3.1). + : This triggered an unprecedented merger and product repositioning wave that com- + : pletely reshaped the industry. Figure 3.1 contains the average percentage of stations + : that switched owners and that switched formats. Between 1996 and 2000 more than + : 10% of stations switched owners annually. After 2000 the number dropped to less + : than 4%. Greater ownership concentration in the 1996-2000 period was also associ- + : ated with more format switching. The percentage of stations that switched formats + : peaked in 1998 and 2001 at 13%. In effect, the Herfindahl-Hirschman Index (HHI) in + : the listenership market grew from 0.18 in 1996 to about 0.3 in 2006. + : The impact of this consolidation on consumer surplus has been studied before + : using a static demand and supply approach. For example Jeziorski (2010) (Chapter + : 2 of this thesis), finds that consolidation of ownership in this industry was harmful + : to advertisers, causing $300m loss in advertiser surplus, but beneficial to listeners, + : raising the welfare by 1%. + : In order to analyze the supply side effects of this consolidation, I compiled a + : dataset 6 . on stations in the 88 markets studied by Jeziorski (2010). The data + : contains ownership for each station oj , and station format fj . It uses the estimates of + : station quality ξj , contained in Jeziorski (2010). I also observe each acquisition made + : in this market and the average acquisition price. +footnote : 6 +text : Data is constructed using the software provided by BIA Financial Network Inc. and Media + : Market Guides by SQAD +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 50 +blank : +blank : +blank : +blank : +text : Figure 3.1: Dynamics of station acquisition and format switching +blank : +heading : 3.5.2 Static profits +text : The static profit function is taken directly from Jeziorski (2010). Radio station owners + : draw their revenue from selling advertising and each advertising slot is priced on a + : per listener basis. The total profit of the owner k is equal to +math : X + : π̄k (s, d) = rj (q ∗ , s, d)pj (q ∗ , s, d)qj∗ + : j:oj =k +blank : +blank : +text : where q ∗ are the equilibrium advertising quantities chosen in the static oligopoly + : game, rj is the number of listeners and pj is the price per listener. In this paper, I + : treat the estimates of this profit function as given; however, I do correct the standard + : errors of the dynamic estimates by accounting for the noise introduced by estimating + : profit function. + : The only difference between the baseline model in Jeziorski (2010) and the profit + : function used in this chapter is that the marginal cost of production is set to zero and + : format substitution matrix Ω is assumed to be diagonal. I made these assumptions +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 51 +blank : +blank : +blank : +text : for computational reasons. +blank : +blank : +heading : 3.5.3 Estimation details +text : The estimation is a direct application of the framework desribed in subsection 3.4. + : The model endogenizes acquisition decisions and format switching decisions. The + : dynamics in an unobserved radio station quality ξ is assumed to be exogenous. + : The first piece of the model that needs to be specified is the function I(st , dt ), + : that prescribes the sequence of moves firms make in the merger and repositioning + : process. Following Gowrisankaran (1999), I assume that firms with the biggest total + : market shares move first. This is motivated by the fact that the bigger players in the + : market might a have first-mover advantage over smaller players. The acquisition price + : is assumed to be constant within market and equal to the observed mean acquisition + : price. + : To estimate the merger probability I use the method outlined in the Example + : 3.4.3. Each owner considers, one at a time, stations to acquire, starting from the + : one with the highest quality measure ξj , and moving down according to ξj 7 . A flow + : chart of the merger process is presented in the Appendix B.2. Such structure enables + : expanding the data structure on acquisitions within the firm +blank : +math : Ot + : (ωkt , atk ) 7→ (ωjk + : t + : , atjk )j=1 + : −k +blank : +blank : +blank : +blank : +math : t +text : where O−k is the number of stations owned by competitors. If we assume that ψ is a + : difference of two extreme value distributions and is also revealed in a sequence, one + : can consistently estimate a probability of merger ProbM + : k , by running a regular logit + : regression on this extended dataset. + : The covariates in the logit regression should reflect the information about the state + : space contained in the data. In a perfect world one would use a very flexible index + : function of the state space variables. However, because of high dimensionality of + : the state space, such an approach requires too many degrees of freedom, and quickly +footnote : 7 +text : Choice of ξj as an ordering characteristic is motivated by the fact that it is a vertical measure + : of profitability. +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 52 +blank : +blank : +blank : +text : exhausts all the information available in the data. To overcome this problem, I use + : a linear index function of several statistics about the state space computed from the + : data 8 . The full set of covariates can be found in Table B.1 in Appendix B.3. + : A similar strategy can be employed to estimate the format switching process. The + : flow chart describing this process is contained in Appendix B.2. Assuming that firms + : switch formats sequentially dictates the following dataset expansion +blank : +math : Ot + : t + : (ωkt , atk ) 7→ (ωjk , atjk )j=1 + : −k +blank : +blank : +blank : +blank : +text : Using this auxiliary dataset one can apply a multinomial logit model to estimate + : the format switching probabilities ProbR + : k . The restriction on the index function also + : applies in this case, so I use only a limited set of covariates (given in Table B.2 in + : Appendix B.3). + : In the second stage of the estimation, I parametrize the fixed cost function +blank : +math : F (stm tm + : k ) = θC1 × POPm × nk θC2 (3.5) +blank : +text : where POPm is a population of the market m and nkt is the number of stations + : owned by player k at time t. Parameter θC2 dictates the amount of cost synergies + : from owning multiple stations. I also assume a constant format switching cost that + : is proportional to the population. Those assumptions are motivated by the fact that + : Jeziorski (2010) finds that most of the variation in marginal cost of radio operations + : between can be explained by the variation in total population. + : In the second stage, I simulate the value function only for the owner with the + : biggest market share at each data point (stm , dtm ). These simulations are done ac- + : cording to the Algorithms 2 and 3. The suboptimal value function Ṽk is obtained + : by multiplying the merger and format switching probability by a uniform [.95, 1.05] + : random variable. When choosing the size of the perturbations one faces a bias and + : variance trade-off. When the size is too small the estimator start picking up the + : noise from the simulations instead of the sub-optimality of the strategy, decreasing +footnote : 8 +text : a similar approach can be found in Sweeting (2007), Ryan (2005), Ryan and Tucker (2006), and + : Ellickson and Arie (2005). +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 53 +blank : +blank : +blank : +text : the efficiency of the estimator. When the size is chosen to be too big, the bounds of + : the estimator become very large creating potential bias. The chosen perturbation is + : a compromise between those two factors. +blank : +blank : +heading : 3.5.4 Results +text : This subsection describes the results of the estimation. The exposition is divided into + : two parts. First, I present the policy function estimates. Then, I report the main + : results on fixed cost and switching cost synergies. +blank : +heading : First stage: Policy function +blank : +text : Tables B.3 and B.4 report coefficients from a purchase strategy probit approxima- + : tion. They reveal that owners with larger market shares are more likely to purchase + : new stations and are less likely to sell. Also, there are synergies when purchasing + : multiple stations. The coefficient on the first purchase dummy PUR0 is negative while + : coefficients on dummies for multiple purchases are positive. This indicates that it + : is easier to negotiate the purchase of many stations, or even an entire company at + : once, than a single station. The number of owned stations in the format (the FORMAT + : variable in the table) has a negative influence on purchase decisions. This is evidence + : for diversification. The coefficient of station quality is positive which suggests that + : stations with higher quality are purchased more often. + : Table B.5 presents the influence on future format of the following covariates: + : change of ownership dummy, AM/FM status, and previous format. The negative + : coefficient of a Spanish format in the first row of the table suggests that when a + : station is purchased it is less likely to switch to Spanish format. On the other hard, + : the positive coefficient of AC tells us that change in ownership is correlated with + : switching to the Adult Contemporary format. The second column of the table shows + : that FM stations are likely be of Rock or CHR format, and not so likely to be of + : News/Talk format. The remaining rows of the table describe the Markov dynamics + : of formats. The diagonal cells have much higher numbers than the off-diagonal ones, + : which reflects the fact that staying in the current format is much more probable than +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 54 +blank : +blank : +blank : +text : switching. + : Table B.6 presents the relationship between the current demographic composition + : of the market format switching decisions. In addition, Table B.7 contains similar + : information concerning the dynamics of the demographics (the difference between + : two consecutive periods) and format switching. One can observe many patterns that + : suggest firms respond to the current state of population demographics as well as to the + : dynamics of population demographics. For example, a larger current population and + : growth of the Hispanic population is ralated to the stations switching to a Hispanic + : format. One can observe a similar pattern for Blacks and the Urban format, as well as + : for older people and the News/Talk format. Those patters largely reflect correlations + : between tastes for formats and demographics described in Jeziorski (2010). +blank : +heading : Second stage: Fixed and switching cost +blank : +text : The estimated parameters of the fixed cost equation (3.5) are as follows: θ̂C1 = 0.69 + : and θ̂C2 = 0.59. Table 3.2 interprets the economic significance of these parameters in + : terms the amount of saved fixed costs per year if two stations are commonly owned + : compared to being separate companies. Since the amount of cost synergies depends on + : the market population, only three representative markets are presented. Los Angeles + : is the biggest market in the sample and the cost savings in that market amount to + : about $4.4m per-year (roughly 10% of the revenue of a big station). Knoxville is + : representative of medium markets and has about $0.23m of such cost savings, and + : Bismark, a small market, has about $34k of savings. Table 3.3 presents total cost + : savings from all mergers after the Telecom Act was passed. It turns out that the + : merger activity lowered the fixed cost of providing radio programming by almost + : $2.5b, amounting to almost 10% of the total revenue of the industry. Compared to + : that, the impact on advertiser surplus identified in Jeziorski (2010) is very small. This + : leads me to conclude that the deregulation of 1996 provided substantial operational + : efficients that outweigh negative impacts on advertiser welfare. + : The last set of estimates concern the product repositioning costs. The estimate of + : the cost parameter θ̂C is 2.1. The repositioning cost for each market is the population + : of that market multiplied θ̂C . Examples of this cost are given in Table 3.4. The +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 55 +blank : +blank : +table : Market Los Angeles Knoxville Bismarck + : Population 13m .7m 100k + : Savings per year $4.4m $.23m $34k +blank : +blank : +text : Table 3.2: Savings when two stations are owned by the same firm vs. operating + : separately +blank : +table : Consumer Advertiser Fixed + : Surplus Surplus Cost + : Impact of + : +1% -$300m -$2.450m + : Telecom Act +blank : +blank : +text : Table 3.3: Total cost savings created by mergers after 1996, compared to demand + : effects from Jeziorski (2010) +blank : +text : table suggests this cost is about the yearly revenue of a big station. Such a huge + : repositioning cost can justify some of the behavior found when analyzing the merger + : probabilities; namely, stations tend to stay away from purchasing the formats they + : already have. If the format switching costs were low, the optimal thing to do would + : be to purchase stations close to your portfolio to get rid of competition and rebrand + : them to avoid cannibalization. However, if the switching costs are high, it might be + : optimal to avoid paying them and purchase a station further away. The previous + : subsection and Sweeting (2008) presest the evidence of the latter type of behavior, + : reinforcing the finding of high switching cost estimates. +blank : +table : Market Los Angeles Knoxville Bismarck + : Switching cost $27m $1.5m $0.2m +blank : +blank : +heading : Table 3.4: Format switching cost for chosen markets +header : CHAPTER 3. COST SYNERGIES FROM MERGERS 56 +blank : +blank : +blank : +heading : 3.6 Conclusions +text : This paper proposed a new estimator of a production cost curve that enables the + : identification of cost synergies from mergers. The estimation uses inequalities rep- + : resenting an equilibrium of a dynamic game with endogenous mergers and product + : repositioning decisions. + : The biggest advantage of this estimator is that it enables the identification of + : the cost curve just from merger decisions, without using cost data. Since reliable + : cost data is very hard to obtain, the cost side analysis of mergers was very hard to + : perform. This method is able to solve this problem, and provides a powerful tool for + : policy makers to improve their merger assessments. + : Since the proposed method is based on a fully dynamic framework, it additionally + : solves many of the problems of static merger analysis. First of all, endogenizing the + : merger decision allows for sample selection on unobservables in the estimation and + : correcting for the fact that only the most profitable mergers are carried out. Moreover, + : I allow for follow-up mergers and merger waves. Additionally, endogenizing product + : characteristics enables correction for post-merger product repositioning. + : The estimator belongs to a class of indirect estimators proposed by Hotz, Miller, + : Sanders, and Smith (1994) and Bajari, Benkard, and Levin (2004). Therefore, it + : shares all the benefits of those estimators, such as conceptual simplicity of imple- + : mentation and computational feasibility, because it avoids the computation of an + : equilibrium. However, it also shares their downsides, such as a loss in efficiency. + : The estimator was applied to analyze the cost side benefits of a deregulation of the + : U.S. radio industry. It turns out that the consolidation wave in that industry between + : 1996 and 2006 provided substantial cost synergies. These amounted to about 2 billion + : dollars per, year and constitute about 10% of industry revenue. Such benefits are an + : order of magnitude larger than potential losses in advertiser welfare found by Jeziorski + : (2010). This provides a significant argument for the supporters of a deregulation bill, + : and serves as an example of how cost curve estimation can provide additional insights + : supplementing traditional merger analysis. +header : Appendix A +blank : +heading : Additional material to Chapter 2 +blank : +heading : A.1 Advertising demand: Micro foundations +text : In this section I present a model that rationalizes inverse demand for advertising (2.5) + : Assume that there are A types of advertisers. Each type a ∈ A targets a certain + : demographic group(s) da . Let γ2 be a total mass of advertisers and ASa be a share of + : advertisers of type a in market m. Advertisers are also heterogeneous in their value + : of the ad slot in format f , and I assume that those values are distributed uniformly + : on the interval [0, γ1f ]. An advertiser of type a gets utility only if a listener of type da + : hears an ad. To compute the exact expected value of an advertising slot, advertisers + : need to know the demographic composition of each station in the market. Because + : advertisers are small, and such detailed data is not offered by Arbitron, it seems + : unlikely that they would be able to do that. Instead, I assume that they approximate + : those calculations using publicly available data contained in Arbitron’s Radio Today + : publications. These publications provide nation-wide conditional probabilities rf |a + : of a consumer of type da choosing format f conditional on listening to the radio. + : Advertisers take these conditional probabilities as given and compute the market + : specific probabilities of obtaining correct listeners when advertising in each format. + : Such computations can be done by Bayes’ Rule, i.e. +blank : +math : rf |a LSa + : ra|f = + : rf +blank : +footer : 57 +header : APPENDIX A. ADDITIONAL MATERIAL TO CHAPTER 2 58 +blank : +blank : +text : P + : where rf = c rf |a LSa and LSa is the population share of demographic group da , + : which is assumed to be known to the advertiser. Having listeners’ distributions ra|f + : and station ratings rj (available on Arbitron’s website) at hand, advertisers compute + : the probability of successful targeting at station j to be rj ra|f , where f is a format of + : station j. + : Radio stations quote costs-per-point CPPaf individually for each advertiser type + : and format. Advertisers decide if they want to purchase advertising after observing + : the CPPs and station ratings. Because advertisers are small and likely do not have + : much market power over radio station owners, I assume that they are price and rating + : takers1 . Advertisers can purchase advertising from several stations at once; however, + : I assume away any potential complementarities. + : In equilibrium, advertisers purchase advertising as long as their expected value is + : above price. Let qa be the amount of advertising purchased by advertisers of type a. + : A marginal advertiser must be indifferent between purchasing advertising or not, so + : the clearing per-listener prices are given by +blank :   +math : 1 + : CPPaf = γ1f ra|f 1− qa + : γ2 ASa +blank : +text : Given the clearing prices CPPaf , advertisers are indifferent when choosing between + : formats, so I assume that advertising is purchased proportionally to the target lis- +math : P +text : teners’ tastes i.e. qa = ASa f rf |a qf . If I make the simplifying assumption that + : ASa ≈ LSa , then the arrival probability of an advertiser of type a at a station of + : format f would be equal to ra|f . Therefore, expected per-listener price in format f is + : given by +math : ! + : X + : 2 1 X + : CPPf = (ra|f ) γ1f 1 − rf 0 |a qf 0 = + : a + : γ2 f 0 + : ! !−1  + : X 1 X X X + : = γ1f (ra|f )2 1 − qf 0 (ra|f )2 (ra|f )2 rf 0 |a  . + : a + : γ 2 + : f0 a a +blank : +math : 1 +text : This assumption is is motivated by the fact that about 75% is purchased by small local firms. + : Such firms’ advertising decisions are unlikely to influence prices and station ratings in the short run. +header : APPENDIX A. ADDITIONAL MATERIAL TO CHAPTER 2 59 +blank : +blank : +blank : +text : Finally, I obtain Equation (2.5) +math : ! + : X + : A + : pj = θ1f rj 1 − θ2A ωfmf 0 qf 0 + : f 0 ∈F +blank : +blank : +math : 2 −1 1 + : P  P 2 A + : by setting ωjj 0 = a (ra|f ) a (ra|f ) rf 0 |a , θ2 = γ2 and assuming that θ1 = + : γ1f a (ra|f )2 for all f . The last assumption basically means that niche formats (with + : P +blank : +text : listenership concentrated in one demographic bin) are less profitable for advertisers + : than general interest formats. + : The presented model is only one of a number of ways to rationalize the weighted + : price equation (2.5) in which competition between formats is channeled though demo- + : graphics. Other possibilities include: a local monopoly in which each advertiser type + : draws utility only from advertising on one particular station, and a format-monopoly + : in which each advertiser type targets only one format. +blank : +blank : +heading : A.2 Numerical considerations +text : To solve the optimization problem (2.12), I used a version of the Gauss-Newton + : method implemented in the commercial solver KNITRO. Using this state-of-the-art + : solver avoids certain convergence problems that are common to many non-linear es- + : timators. + : The iteration step of the KNITRO solver requires computing constraints, a Jaco- + : bian of the constraint, and an inverse of the inner product of this Jacobian (used to + : compute the approximate Hessian of the Lagrangian). The objective function and its + : Jacobian come essentially for free because of their simple nature. + : To compute the constraints and their Jacobian, I employed a piece of highly opti- + : mized parallel C code. This allows the use a fairly large dataset (about 42,000 obser- + : vations) and many draws (500 draws from Normal and CPS per date/market) when + : computing the constraints. When parallelizing the code, I was careful to maintain + : independence of the draws within and between threads. To achieve this, I imple- + : mented a version of a pseudo-random number generator (described in (L’Ecuyer and +header : APPENDIX A. ADDITIONAL MATERIAL TO CHAPTER 2 60 +blank : +blank : +blank : +text : Andres 1997). This generator enables us to create a desired number of independent + : pseudo-random feeds for each thread. + : One iteration of the solver takes about two to three minutes on an 8-Core 3Ghz + : Intel Xeon processor and uses about 4GB of memory. About 90% of this computation + : is the inversion of a Hessian estimator within the KNITRO solver. This inversion + : cannot be parallelized because it is done inside the solver, without the user’s control. + : Appendix B +blank : +heading : Additional material to Chapter 3 +blank : +heading : B.1 Estimation without acquisition prices +text : r + : In case the pricing function P̂jk cannot be estimated in the first state because of data + : constraint, one could employ a bargaining model for infer it. Suppose one employs + : a parametrization P̂ (ω|θP ). For an initial value of parameters θP0 one could compute + : a surplus from acquisition of the product j by an owner k using simulated V̂kt and + : V̂kt0 where k 0 is the current owner of product j. Then using a bargaining model + : one could infer prices and fit a new parametrization θP1 . If repeating this procedure + : leads to convergence, then obtain a parametrization θ̂P and value functions V̂kt that + : are consistent with eachother. The detailed description of this procedure is given + : in the Algorithm 1. The big dowside of this approch is that one needs resolve this + : procedure for any set of cost parameters and cannot take advantage of linearing + : of the value function. It makes the procedure infeasible to use for large datasets + : because of computational burden. However, given the rapid hardware development + : it is reasonable to think it it would be feasible in the near future. +blank : +blank : +blank : +blank : +footer : 61 +header : APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3 62 +blank : +blank : +blank : +heading : Algorithm 1: Estimator without price data +math : Take any θP0 ; + : Let r = 0; + : repeat + : Simulate the value functions V̂ r using pricing process P̂ (ω|θPr ); + : Compute surplus from any acquisition using the simulated value functions; + : Compute acquisition prices P̂jm by applying any bargaining game; + : Fit new parameters θPr+1 using P̂jm ; + : until convergence of θPr ; +blank : +blank : +heading : B.2 Radio acquisition and format switching algo- + : rithms +text : This section of the appendix contains a detailed flows of the algorithms used to + : simulate the value function from section 3.5. +heading : Algorithm 2: Merger algorithm +math : Let ω1r = sr ; + : foreach firm k in a sequence I(sr ) do + : Let J−k be a set of stations not owned by k sorted by ξjr ; + : foreach station j in J−k do + : r + : Set purchase price Pjk = P̄ m ; + : M + : Compute acquisition probability Prob[ (ω r , dt ); + : k + : Draw a random number u from U [0, 1]; + : M + : if u ≤ Prob + : [ then + : Increase Arold owner by β r−t Pjk + : r + : ; + : r r−t r + : Decrease Ak by β Pjk ; + : Update ωkr for acqusition; + : Increase Bkr by β r−t E[φ|acquisition]; + : end + : end + : r + : Let ωk+1 = ωkr ; + : end +header : APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3 63 +blank : +blank : +blank : +text : Algorithm 3: Format switching algorithm +math : Let ω̃1r = ωK+1 + : r + : ; + : foreach firm k in a sequence I(sr ) do + : Let Jk be a set of stations owned by k sorted by ξjr ; + : foreach station j in Jk do + : R + : [ k (ω̃ r , dr ); + : Compute repositioning probabilities Prob k + : Simulate the future characteristic fjr+1 ; + : Increase Ckr by β r−t E[ψ|fjr ]; + : if the fj changed then + : Update ω̃kr ; + : Remember the repositioning for a computation of Dkr ; + : end + : end + : tm + : Let ω̃k+1 = ω̃ktm ; + : end +blank : +blank : +blank : +blank : +heading : B.3 Policy function covariates +text : This section of the appendix contains tables of covariates used in the first stage in + : the estimation in section 3.5. + : Format switching strategy + : PUR Dummy equal to 1 if station was recently purchased + : FM AM/FM dummy, equals to 1 if considered station is FM + : FORMAT Past format dummies + : PORT F Number of stations owner in format F + : PORT COMPJ F Number of stations competitor J owns in format F, competitors of + : ranking 4 or higher are pooled + : XI PORT F Average quality of stations owner in format F + : XI PORT COMPJ F Average quality of stations competitor J owns in format F, competi- + : tors of ranking 4 or higher are pooled + : - Demographic characteristics of the market +blank : +heading : Table B.1: Covariates for the format switching strategy multinomial logic regression. +header : APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3 + : Purchase strategy + : OWNER1. . . OWNER4 Dummies that are equal to the ranking of the player in terms of total market share of + : owned stations. If ranking is lower that 4 we activate the fourth dummy + : PAST OWNER1. . . PAST OWNER4 Ranking of the previous owner of the station amongst the competitors. + : TRIAL Describes how many stations did this player considered to purchase already this period. + : For explanation of sequential purchase decision process look in Section 3.5.3 + : PUR0. . . PUR3 Dummies describing number of stations already purchased + : FORMAT Number of stations owned in the format of considered station + : FORMAT COMP1. . . FORMAT COMP4 Number of stations owned by competitors in the considered station, by ranking. + : FORMAT COMP4 are pooled competitors with ranking of 4 or higher + : FM AM/FM dummy, equals to 1 if considered station is FM + : PORT F Number of stations owner in format F + : PORT COMPJ F Number of stations competitor J owns in format F, competitors of ranking 4 or higher + : are pooled + : XI Average quality of stations owned in the format of considered station + : XI COMP1. . . XI COMP4 Average quality of stations owned by competitors in the considered station, by ranking. + : XI COMP4 are pooled competitors with ranking of 4 or higher + : XI PORT F Average quality of stations owner in format F + : XI PORT COMPJ F Average quality of stations competitor J owns in format F, competitors of ranking 4 or + : higher are pooled + : - Dummies of the format of considered station interacted with demographic characteris- + : tics of the market +blank : +heading : Table B.2: Covariates for the purchase strategy logic regression. +blank : +blank : +blank : +blank : +footer : 64 +header : APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3 65 +blank : +blank : +blank : +heading : B.4 First stage estimates: Dynamic model +blank : +table : Top 1 Owner Top 2 Owner Top 3 Owner + : Buyer 0.5127 0.3423 0.2608 + : Seller −0.3772 −0.2792 −0.0257 +blank : +heading : Table B.3: Station purchase policy estimates - buyer/seller dummies +blank : +blank : +blank : +table : Estimator + : PUR0 −2.6082 + : PUR1 0.7548 + : PUR2 0.4279 + : PUR3 0.2463 + : FORMAT −0.0534 + : FORMAT COMP1 −0.0038 + : FORMAT COMP2 −0.0556 + : FORMAT COMP3 0.0728 + : FORMAT COMP4 −0.0428 + : FM 0.0151 + : STATION XI −0.1069 + : XI 0.0596 + : XI COMP1 0.0270 + : XI COMP2 0.0712 + : XI COMP3 0.0767 + : XI COMP4 −0.0117 +blank : +heading : Table B.4: Station purchase policy estimates - other variables +header : APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3 66 +blank : +blank : +blank : +blank : +table : AC Rock CHR Urban News Country Spanish Other + : Alt. Talk + : PURCHASE 0.30 −0.14 0.04 −0.07 0.05 0.03 −0.23 −0.22 + : FM 1.26 1.54 1.35 1.06 −0.25 1.31 0.56 0.85 + : AC 3.70 −0.47 −0.34 −0.86 −0.43 0.37 −0.66 −0.44 + : Rock −0.27 4.41 −0.58 −0.18 −0.10 0.48 −0.32 −0.21 + : CHR −0.24 −0.42 4.38 −0.06 −0.19 0.00 −0.14 −0.35 + : Urban −0.49 0.05 −0.35 4.06 −0.17 0.48 −0.15 −0.22 + : Alt. + : News −1.00 −0.84 −0.82 −1.29 3.89 0.25 −0.80 −0.93 + : Talk + : Country −1.14 −1.01 −1.06 −1.35 −0.63 4.76 −0.73 −1.15 + : Spanish −1.61 −1.45 −1.30 −1.61 −1.20 −0.29 3.10 −1.42 + : Other −0.89 −1.07 −1.31 −1.27 −0.86 0.00 −1.22 3.02 + : Dark −2.18 −2.42 −2.50 −2.62 −1.61 −0.72 −1.60 −1.31 +blank : +heading : Table B.5: Format switching policy estimates - format dynamics +blank : +blank : +blank : +blank : +table : AC Rock CHR Urban News Country Spanish Other + : Alt. Talk + : Age 12-17 0.00 −0.27 0.04 −0.50 −0.33 −0.67 −0.50 −0.32 + : Age 18-24 0.00 −0.31 −0.26 −0.69 0.31 0.00 −0.42 −0.36 + : Age 25-34 −0.54 0.00 0.02 −0.37 −0.14 −0.99 −0.06 −0.32 + : Age 35-44 −0.48 −0.00 −0.20 −0.32 −0.06 −1.17 −0.42 −0.08 + : Age 45-49 −0.46 0.00 −0.93 −0.61 0.23 −0.89 −0.81 −0.09 + : Age 50-54 −0.44 −0.41 −1.36 −0.67 0.42 −0.82 −0.62 −0.09 + : Age 55-64 0.00 −0.64 −1.49 −0.68 0.34 −0.77 −0.42 −0.16 + : Gender −0.41 −0.23 −0.43 −0.54 −0.00 −0.84 −0.34 −0.21 + : Some HS −0.38 −0.49 −0.41 −0.33 −0.27 −0.13 0.06 0.02 + : HS Grad. 0.19 0.00 −0.52 −0.32 −0.84 −0.29 −0.90 −0.19 + : Some College −0.12 −0.34 −0.72 −0.70 0.23 −0.45 −0.45 −0.03 + : Income 0-25k −0.16 −0.83 −0.32 −0.13 −0.35 −0.43 −0.52 −0.03 + : Income 25k-50k −0.06 −0.54 0.14 −0.39 −0.33 −0.34 −0.13 0.00 + : Income 50k-75k −0.07 −0.02 −0.54 −0.22 0.21 −0.39 −1.10 −0.17 + : Black −0.99 −0.58 0.00 1.25 −0.44 −1.11 −0.54 −0.26 + : Hispanic −0.55 0.19 −0.36 −0.06 −0.49 −0.20 2.42 −0.56 +blank : +heading : Table B.6: Format switching policy estimates - current demographics +header : APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3 67 +blank : +blank : +blank : +blank : +table : AC Rock CHR Urban News Country Spanish Other + : Alt. Talk + : Age 12-17 0.00 0.00 0.00 6.69 −5.06 0.00 9.33 0.00 + : Age 18-24 −7.73 3.44 17.89 0.00 0.00 −12.76 0.00 6.06 + : Age 25-34 4.29 0.00 0.00 0.00 −1.35 5.23 4.32 −3.59 + : Age 35-44 2.65 0.00 5.23 1.83 −4.83 0.00 2.67 1.73 + : Age 45-49 −3.31 0.00 9.04 0.00 2.31 −3.45 −2.98 2.59 + : Age 50-54 −3.27 0.00 −2.60 −1.95 1.63 0.04 −3.37 0.00 + : Age 55-64 −4.57 −3.19 −7.50 0.00 7.73 0.00 −1.12 0.00 + : Gender 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 + : Some HS −0.03 −0.06 1.14 0.33 1.08 −0.06 −0.34 −1.09 + : HS Grad. −0.56 0.00 1.18 0.90 0.84 −0.16 −0.31 −0.47 + : Some College −0.40 −0.64 0.50 0.24 0.36 0.00 1.33 −0.89 + : Income 0-25k 0.43 0.37 0.05 0.20 0.32 0.33 −0.63 0.18 + : Income 25k-50k −0.01 0.61 −0.19 −0.49 0.18 −0.36 −1.11 −0.44 + : Income 50k-75k 0.32 0.64 0.51 −0.02 −0.01 −0.01 0.17 0.41 + : Black 4.09 −21.64 −49.49 3.51 0.00 8.71 0.00 5.16 + : Hispanic −2.86 −1.55 −3.64 0.77 −0.24 −1.65 4.84 0.00 +blank : +heading : Table B.7: Format switching policy estimates - demographic dynamics +bibliography : Bibliography +blank : +ref : Ackerberg, D. A., and M. Rysman (2005): “Unobserved Product Differentia- + : tion in Discrete-Choice Models: Estimating Price Elasticities and Welfare Effects,” + : RAND Journal of Economics, 36(4), 771–788. +blank : +ref : Arcidiacono, P., and R. Miller (2010): “CCP Estimation of Dynamic Discrete + : Choice Models with Unobserved Heterogeneity,” Discussion paper, Duke Univer- + : sity. +blank : +ref : Argentesi, E., and L. Filistrucchi (2007): “Estimating market power in a two- + : sided market: The case of newspapers,” Journal of Applied Econometrics, 22(7), + : 1247–1266. +blank : +ref : A.Richter, W. (2006): Radio: Complete Guide to the Industry. Peter Lang Pub- + : lishing. +blank : +ref : Armstrong, M. (2006): “Competition in Two-Sided Markets,” The RAND Journal + : of Economics, 37(3), 668–691. +blank : +ref : Bain, J. (1968): Industrial organization. John Wiley & Sons. +blank : +ref : Bajari, P., C. L. Benkard, and J. Levin (2004): “Estimating Dynamic Models + : of Imperfect Competition,” NBER Working Papers 10450, National Bureau of + : Economic Research, Inc. +blank : +ref : Benkard, C. L., A. Bodoh-Creed, and J. Lazarev (2008): “Simulating the + : Dynamic Effects of Horizontal Mergers: U.S. Airlines,” Discussion paper, Stanford + : University. +blank : +footer : 68 +header : BIBLIOGRAPHY 69 +blank : +blank : +blank : +ref : Berry, S., J. Levinsohn, and A. Pakes (1995): “Automobile Prices in Market + : Equilibrium,” Econometrica, 63(4), 841–90. +blank : +ref : Berry, S. T. (1994): “Estimating Discrete-Choice Models of Product Differentia- + : tion,” RAND Journal of Economics, 25(2), 242–262. +blank : +ref : Berry, S. T., and J. Waldfogel (2001): “Do Mergers Increase Product Variety? + : Evidence From Radio Broadcasting,” The Quarterly Journal of Economics, 116(3), + : 1009–1025. +blank : +ref : Brynjolfsson, E., Y. J. Hu, and M. D. Smith (2003): “Consumer Surplus in + : the Digital Economy: Estimating the Value of Increased Product Variety at Online + : Booksellers,” Manage. Sci., 49(11), 1580–1596. +blank : +ref : Bulow, J. I., J. D. Geanakoplos, and P. D. Klemperer (1985): “Multimar- + : ket Oligopoly: Strategic Substitutes and Complements,” The Journal of Political + : Economy, 93(3), 488–511. +blank : +ref : Chandra, A., and A. Collard-Wexler (2009): “Mergers in Two-Sided Markets: + : An Application to the Canadian Newspaper Industry,” Journal of Economics & + : Management Strategy, 18, 1045–1070. +blank : +ref : CRA International (2007): “Expost Merger Review: An Evaluation of Three + : Competition Bureau Merger Assessments,” Discussion paper, CRA International. +blank : +ref : Deaton, A., and J. Muellbauer (1980): “An Almost Ideal Demand System,” + : The American Economic Review, 70(3), 312–326. +blank : +ref : Department of Justice (1997): “Horizontal Merger Guidelines,” Discussion pa- + : per, Department of Justice. +blank : +ref : Drushel, B. E. (1998): “The Telecommunications Act of 1996 and Radio Market + : Structure,” Journal of Media Economics, 11. +blank : +ref : Dukes, A. (2004): “The Advertising Market in a Product Oligopoly,” Journal of + : Industrial Economics, 52(3), 327–348. +header : BIBLIOGRAPHY 70 +blank : +blank : +blank : +ref : Ellickson, P., and B. Arie (2005): “The Dynamics of Retail Oligopolies,” 2005 + : Meeting Papers 829, Society for Economic Dynamics. +blank : +ref : Ericson, R., and A. Pakes (1995): “Markov-Perfect Industry Dynamics: A Frame- + : work for Empirical Work,” Review of Economic Studies, 62(1), 53–82. +blank : +ref : European Commission (2004): “Guidelines on the assessment of horizontal merg- + : ers,” Discussion paper, European Commission. +blank : +ref : Evans, D. S. (2002): “The Antitrust Economics of Two-Sided Markets,” SSRN + : eLibrary. +blank : +ref : Gowrisankaran, G. (1999): “A Dynamic Model of Endogenous Horizonal Merg- + : ers,” RAND Journal of Economics, 30(1), 56–83. +blank : +ref : Heckman, J. J. (1979): “Sample Selection Bias as a Specification Error,” Econo- + : metrica, 47(1), 153–161. +blank : +ref : Hotz, V. J., R. A. Miller, S. Sanders, and J. Smith (1994): “A Simula- + : tion Estimator for Dynamic Models of Discrete Choice,” The Review of Economic + : Studies, 61(2), 265–289. +blank : +ref : Ivaldi, M., and F. Verboven (2005): “Quantifying the effects from horizontal + : mergers in European competition policy,” International Journal of Industrial Or- + : ganization, 23(9-10), 669–691. +blank : +ref : Jeziorski, P. (2010): “Impact of mergers and changes in product diversity on split + : of surplus in two-sided markets: Case of U.S. radio industry,” Discussion paper, + : Stanford University, Working Paper. +blank : +ref : Kaiser, U., and J. Wright (2006): “Price structure in two-sided markets: Evi- + : dence from the magazine industry,” International Journal of Industrial Organiza- + : tion, 24(1), 1 – 28. +blank : +ref : Kim, J., G. M. Allenby, and P. E. Rossi (2002): “Modeling Consumer Demand + : for Variety,” Marketing Science, 21(3), 229–250. +header : BIBLIOGRAPHY 71 +blank : +blank : +blank : +ref : Klein, J. I. (1997): “DOJ Analysis of Radio Mergers,” speech delivered in Wash- + : ington DC, + : http://www.usdoj.gov/atr/public/speeches/1055.pdf. +blank : +ref : L’Ecuyer, P., and T. H. Andres (1997): “A Random Number Generator Based + : on the Combination of Four LCGs,” in Mathematics and Computers in Simulation, + : pp. 99–107. +blank : +ref : Leeper, S. E. (1999): “Game of Radiopoly: An Antitrust Perspective of Consoli- + : dation in the Radio Industry,” Fed. Comm. L.J., 52(473). +blank : +ref : Nevo, A. (2000): “Mergers with Differentiated Products: The Case of the Ready- + : to-Eat Cereal Industry,” RAND Journal of Economics, 31(3), 395–421. +blank : +ref : O’Gorman, C., and H. Smith (2008): “Efficiency Gain from Ownership Deregula- + : tion: Estimates for the Radio Industry,” CEPR Discussion Papers 6699, C.E.P.R. + : Discussion Papers. +blank : +ref : Pinkse, J., and M. E. Slade (2004): “Mergers, brand competition, and the price + : of a pint,” European Economic Review, 48(3), 617 – 643. +blank : +ref : Rochet, J.-C., and J. Tirole (2006): “Two-Sided Markets: A Progress Report,” + : The RAND Journal of Economics, 37(3), 645–667. +blank : +ref : Rosse, J. N. (1967): “Daily Newspapers, Monopolistic Competition, and Economies + : of Scale,” The American Economic Review, 57(2), 522–533. +blank : +ref : (1970): “Estimating Cost Function Parameters without Using Cost Data: + : Illustrated Methodology,” Econometrica, 38(2), 256–75. +blank : +ref : Ryan, S. (2005): “The Costs of Environmental Regulation in a Concentrated In- + : dustry,” Working Papers 0510, Massachusetts Institute of Technology, Center for + : Energy and Environmental Policy Research. +blank : +ref : Ryan, S., and C. Tucker (2006): “Heterogeneity and the Dynamics of Technology + : Adoption,” Working Papers 06-26, NET Institute. +header : BIBLIOGRAPHY 72 +blank : +blank : +blank : +ref : Sweeting, A. (2007): “Dynamic Product Repositioning in Differentiated Product + : Markets: The Case of Format Switching in the Commercial Radio Industry,” NBER + : Working Papers 13522, National Bureau of Economic Research, Inc. +blank : +ref : (2008): “The Effects of Horizontal Mergers on Product Positioning: Evi- + : dence from the Music Radio Industry,” working paper, Duke University. +blank :