bb599nz4341.ttx

title         | ESSAYS ON HORIZONTAL MERGERS AND ANTITRUST
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text          |                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
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text          |                Przemyslaw Jeziorski
              |                     June 2010
              |                 © 2010 by Przemyslaw Jeziorski. All Rights Reserved.
              |          Re-distributed by Stanford University under license with the author.
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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/
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text          | This dissertation is online at: http://purl.stanford.edu/bb599nz4341
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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.
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text          |                                                                Peter Reiss, Primary Adviser
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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.
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text          |                                                                                 Ali Yurukoglu
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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.
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text          |                                                                            C. Lanier Benkard
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text          | Approved for the Stanford University Committee on Graduate Studies.
              |                                  Patricia J. Gumport, Vice Provost Graduate Education
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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.
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title         | Abstract
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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.
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title         | Acknowledgments
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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.
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title         | Contents
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text          | Abstract                                                                                iv
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text          | Acknowledgments                                                                         v
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text          | 1 Introduction                                                                          1
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text          | 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
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text          |         2.7.2   Effects of product variety and market power . . . . . . . . . .         31
              |   2.8   Robustness analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . .     32
              |   2.9   Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    33
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text          | 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
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text          | A Additional material to Chapter 2                                                      57
              |   A.1 Advertising demand: Micro foundations . . . . . . . . . . . . . . . . .           57
              |   A.2 Numerical considerations . . . . . . . . . . . . . . . . . . . . . . . . .        59
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text          | 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
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text          | Bibliography          68
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title         | List of Tables
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text          |  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
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text          |  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
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text          | 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
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title         | List of Figures
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text          |  3.1   Dynamics of station acquisition and format switching . . . . . . . . .   50
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title         | Chapter 1
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title         | Introduction
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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
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              | CHAPTER 1. INTRODUCTION                                                                2
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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
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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
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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.
title         | Chapter 2
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title         | Mergers in two-sided markets:
              | Case of U.S. radio industry
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title         | 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).
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              | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                              6
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title         | 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
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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.
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                      8
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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
meta          |   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.
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                               9
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         | 
              | 
title         | 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         | 
text          |                                    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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                              10
blank         | 
              | 
text          |                          ∂rj
              | similar to price, i.e.   ∂qj
              |                                < 0.
              |    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         | 
text          |                                        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         | 
text          |                                         max pj (q, rj (q)|q−j )qj                  (2.3)
              |                                           qj
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         | 
text          |                                       ∂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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                              11
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              | 
              | 
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         | 
              | 
title         | 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.
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                    12
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         | 
              | 
title         | 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         | 
text          |                                  L        L
              |                           uij = θ1i ιj − θ2i qj + θ3L FMj + ξj + ji                    (2.4)
blank         | 
text          |        L
              | 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         | 
text          |                 L
              |                θ1i = θ1L + ΠDi + ν1i ,    Di ∼ Fm (Di |d),     ν1i ∼ N (0, Σ1 )
blank         | 
text          | 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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                        13
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         | 
text          |                        δj = δ(qj |ιj , ξj , θL ) = θ1L ιi − θ2L qj + θ3L FMj + ξj
blank         | 
text          | an interaction part
blank         | 
text          |                        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,
              |                                     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         | 
              | 
title         | 2.3.3     Advertisers
text          | 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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                    14
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
              | as                                                                               !
              |                                                           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         | 
text          |                                             1       X                                

              |                             ωf f 0 = P        2
              |                                                            ra|f ra|f rf 0 |a             (2.6)
              |                                          a∈A ra|f a∈A
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                 15
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         | 
text          |           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
              |                   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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                                        16
blank         | 
              | 
              | 
text          | compose the profits of the radio station owners.
blank         | 
              | 
title         | 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
              |                                                        X
              |        π̄k (qk |q−k , ξ, θ) = max                             rj (q|ξ, θL )pj qj − MCj (qj ) =
              |                                          {qj ;j∈sk }
              |                                                        j∈sk
blank         | 
text          |                                       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         | 
              | 
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         |                                                                           
text          |                          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         | 
              | 
              | 
text          | Additionally, I assume that station unobserved quality is exogenous but serially cor-
              | related. It evolves according an AR(1) process such that
blank         | 
text          |                                                               ξjt = ρξjt−1 + ζjt                                             (2.9)
blank         | 
text          | where ζjt is an exogenous innovation to station quality.
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                              17
blank         | 
              | 
              | 
title         | 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.
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                  18
blank         | 
              | 
text          |                      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%.
meta          |   2
text          |       Source: Arbitron Format Trends Report
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                             19
blank         | 
              | 
              | 
title         | 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         | 
              | 
title         | 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         | 
text          |                                         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         | 
text          |                                    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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                   20
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         | 
text          |                  min g 0 W g
              |                  θL ,ξ,g
blank         | 
text          |   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
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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 .
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              | 
title         | 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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                             21
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              | 
              | 
text          | section 2.3.3. The elements of the matrix Ω are specified as
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text          |                                                                  1      X                          

              |                                                   ωf f 0 = P        2
              |                                                                             ra|f    ra|f rf 0 |a
              |                                                                a∈A ra|f a∈A
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              | 
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
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text          |                                                    Em [η m |Z2 , θA , θC ] = 0 ∀m ∈ M                            (2.13)
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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))
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text          |                                                  ∂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
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              | 
text          |                                        A                              A       C
              | 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
meta          |   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.
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                               22
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              | 
              | 
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 .
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              | 
title         | 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).
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                23
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              | 
              | 
title         | 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.
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              | 
title         | 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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                            24
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              | 
              | 
              | 
text          |                                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)
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text          |                                    −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)
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text          |                  Country           −2.301∗∗∗               0.011∗∗∗
              |                                      (0.006)                (0.003)
blank         | 
text          |                  Spanish           −1.619∗∗∗               0.011∗∗∗
              |                                      (0.004)                (0.001)
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text          |                                    −4.657∗∗∗               0.005∗∗∗
              |                  Other               (0.004)                (0.002)
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text          |                                     0.568∗∗∗
              |                  ρ                   (0.091)
              |                                                               -
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.
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                25
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              | 
text          |                                           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)
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text          |                   −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)
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              | 
text          |     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)
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text          |     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)
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text          |                    −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)
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text          |                     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)
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              | 
              | 
              | 
text          | Table 2.4: Interaction terms between listeners’ demographics and taste for radio
              | programming.
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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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                              26
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              | 
text          |                                         Los Angeles, CA
              |                     AC
              |                                                Alternative
              |                     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
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              | 
text          |                                           Atlanta, GA
              |                     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
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              | 
text          |                                          Knoxville, TN
              |                     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
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              | 
              | 
              | 
text          |                Table 2.5: Product closeness matrices for chosen markets
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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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                             27
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              | 
text          |    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)
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text          |          Table 2.6: Slope of the inverse demand for ads θ2A , by market size
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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.
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              | 
title         | 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.
meta          |                                                                                                                                                         CHAPTER 2. MERGERS IN TWO-SIDED MARKETS
text          |                                                        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%
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              | 
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
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              | 
              | 
              | 
meta          |                                                                                                                                                         28
              | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                 29
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              | 
text          |                            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
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text          |                           Table 2.8: Counterfactuals for all markets
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title         | 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.
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              | 
title         | 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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                30
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              | 
text          |                           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
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text          |         Table 2.9: Counterfactuals for small markets (less than 500k people)
              |                           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
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text          |      Table 2.10: Counterfactuals for large markets (more than 2,000k people)
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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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                31
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              | 
              | 
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.
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              | 
title         | 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
meta          |   4
text          |       Similar results obtained using direct analysis of station playlists can be found in Sweeting (2008).
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                             32
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              | 
              | 
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.
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              | 
title         | 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,
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                                                   33
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              | 
text          |            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)
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text          |            Table 2.11: Slope of the inverse demand for ads θ2A , by market size
              |                              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%
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text          |                             Table 2.12: Robustness of counterfactuals
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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.
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              | 
title         | 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
meta          | CHAPTER 2. MERGERS IN TWO-SIDED MARKETS                                             34
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              | 
              | 
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).
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              | 
              | 
              | 
meta          |   5
text          |       Source: A.Richter (2006)
title         | Chapter 3
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title         | Estimation of cost synergies from
              | mergers without cost data:
              | Application to U.S. radio
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title         | 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.
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              | 
              | 
              | 
meta          |                                           35
              | CHAPTER 3. COST SYNERGIES FROM MERGERS                                             36
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              | 
              | 
title         | 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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                             37
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              | 
              | 
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,
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                               38
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              | 
              | 
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.
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              | 
title         | 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.
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              | 
title         | 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 ∈
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                     39
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              | 
              | 
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.
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              | 
title         | 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}
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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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                          40
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              | 
              | 
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
              |                                        t                                 t
              |                                 ak : (ωi(k) , φtk , Pkt , dt ) 7→ {0, 1}O−k
              |                                                t
              | 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
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text          |                                              t                      t
              |                                      bk : (ω̃i(k) , ψkt , dt ) 7→ F Ok
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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.
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text          | Assumption 3.3.1. ξjt evolves as an exogenous Markov process, for example
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text          |                                            ξjt = ρξjt−1 + ζt                                  (3.1)
meta          |   1
text          |       This assumption is made for the simplicity of exposition and might be easily relaxed.
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                                          41
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         | 
              | 
title         | 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
              |                                                                                 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         | 
              | 
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
              | 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.
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                               42
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         | 
text          |                                                  ∞
              |                                                  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         | 
              | 
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         | 
text          |                         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         | 
              | 
title         | 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         | 
              | 
title         | 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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                      43
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         | 
              | 
title         | 3.4.2     Policy estimation
text          | For any strategy profile
              |                                 g = (a1 , . . . , aK , b1 , . . . , bK )
blank         | 
text          | 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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                   44
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         | 
text          |                       (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         | 
text          |              Y1 = {(ωktm , dtm , atm
              |                                   k ) : 1 ≤ k ≤ K, 1 ≤ m ≤ M, 1 ≤ t ≤ T }
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         | 
text          |              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         | 
text          |              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).
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                   45
blank         | 
              | 
              | 
text          |        The solution to the first problem is to employ a flexible parametric form
blank         | 
text          |                                          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         | 
              | 
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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                                             46
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         | 
              | 
title         | 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.
              |                                      Vkt = Atk + θφ Bkt + θψ Ckt + Dkt
blank         | 
text          | 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         | 
meta          |    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.
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                           47
blank         | 
              | 
              | 
text          | is the expected stream of advertising revenues,
              |                                            ∞
              |                                            X                  X
              |                                 Bkt   =E         β r−t                       φrkj
              |                                            r=t           j:orj 6=k,or+1 =k
              |                                                                     j
blank         | 
              | 
              | 
text          | is the expected stream of acquisition payoff/cost shocks,
blank         | 
text          |                                            ∞
              |                                            X               X
              |                                  Ckt = E         β r−t                t
              |                                                                      ψkjf r+1
              |                                                                              j
              |                                            r=t           j:or+1 =k
              |                                                             j
blank         | 
              | 
              | 
text          | is the expected stream of repositioning payoff/cost shocks, and
              |                                                                                       
              |                      ∞
              |                      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         | 
              | 
              | 
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
meta          |    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.
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                          48
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         | 
text          |                                           1     X 1
              |               (θ̂M , θ̂R ) = argmin                       max{Ṽktm − Vktm , 0}
              |                                       K × T × M k,t,m Atm
              |                                                        k
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         | 
              | 
title         | 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         | 
              | 
title         | 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
meta          |    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.
meta          |   5
text          |     Source: A.Richter (2006)
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                 49
blank         | 
              | 
text          |                   # of active stations Old ownership cap       New cap
              |                           45+                 4                   8
              |                          30-44                4                   7
              |                          15-29                4                   6
              |                          0-14                 3                   5
              | Table 3.1: Change in the local ownership caps introduced by the 1996 Telecom Act.
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.
meta          |   6
text          |    Data is constructed using the software provided by BIA Financial Network Inc. and Media
              | Market Guides by SQAD
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                               50
blank         | 
              | 
              | 
              | 
text          |           Figure 3.1: Dynamics of station acquisition and format switching
blank         | 
title         | 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
              |                                         X
              |                         π̄k (s, d) =             rj (q ∗ , s, d)pj (q ∗ , s, d)qj∗
              |                                        j:oj =k
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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                            51
blank         | 
              | 
              | 
text          | for computational reasons.
blank         | 
              | 
title         | 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         | 
text          |                                                                Ot
              |                                     (ωkt , atk ) 7→ (ωjk
              |                                                       t
              |                                                          , atjk )j=1
              |                                                                   −k
blank         | 
              | 
              | 
              | 
text          |        t
              | 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
meta          |    7
text          |     Choice of ξj as an ordering characteristic is motivated by the fact that it is a vertical measure
              | of profitability.
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                        52
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         | 
text          |                                                               Ot
              |                                                      t
              |                                    (ωkt , atk ) 7→ (ωjk , atjk )j=1
              |                                                                  −k
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         | 
text          |                               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
meta          |    8
text          |      a similar approach can be found in Sweeting (2007), Ryan (2005), Ryan and Tucker (2006), and
              | Ellickson and Arie (2005).
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                              53
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         | 
              | 
title         | 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         | 
title         | 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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                54
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              | 
              | 
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         | 
title         | Second stage: Fixed and switching cost
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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
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                            55
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              | 
text          |                    Market          Los Angeles   Knoxville Bismarck
              |                  Population            13m         .7m       100k
              |                Savings per year      $4.4m        $.23m      $34k
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              | 
text          | Table 3.2: Savings when two stations are owned by the same firm vs. operating
              | separately
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text          |                                     Consumer Advertiser       Fixed
              |                                      Surplus  Surplus         Cost
              |                    Impact of
              |                                       +1%         -$300m    -$2.450m
              |                   Telecom Act
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              | 
text          | Table 3.3: Total cost savings created by mergers after 1996, compared to demand
              | effects from Jeziorski (2010)
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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.
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text          |                    Market         Los Angeles    Knoxville Bismarck
              |                 Switching cost       $27m         $1.5m     $0.2m
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              | 
text          |                 Table 3.4: Format switching cost for chosen markets
meta          | CHAPTER 3. COST SYNERGIES FROM MERGERS                                                56
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              | 
              | 
title         | 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.
meta          | Appendix A
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title         | Additional material to Chapter 2
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title         | 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.
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text          |                                                rf |a LSa
              |                                       ra|f =
              |                                                    rf
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meta          |                                                57
              | APPENDIX A. ADDITIONAL MATERIAL TO CHAPTER 2                                                         58
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              | 
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         |                                                                       
text          |                                                               1
              |                                  CPPaf = γ1f ra|f       1−        qa
              |                                                            γ2 ASa
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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-
              |                             P
              | 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
              |                                                        !
              |                  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
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meta          |    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.
meta          | APPENDIX A. ADDITIONAL MATERIAL TO CHAPTER 2                                             59
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              | 
              | 
text          | Finally, I obtain Equation (2.5)
              |                                                                            !
              |                                                     X
              |                                   A
              |                             pj = θ1f rj   1 − θ2A            ωfmf 0 qf 0
              |                                                     f 0 ∈F
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              | 
text          |                                      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.
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              | 
title         | 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
meta          | APPENDIX A. ADDITIONAL MATERIAL TO CHAPTER 2                                         60
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              | 
              | 
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.
meta          | Appendix B
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title         | Additional material to Chapter 3
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title         | B.1      Estimation without acquisition prices
              |                                r
text          | 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.
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              | 
              | 
              | 
meta          |                                            61
              | APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3                                     62
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              | 
              | 
text          |  Algorithm 1: Estimator without price data
              |   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 ;
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              | 
title         | 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.
              |   Algorithm 2: Merger algorithm
              |    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
meta          | APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3                                            63
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              | 
              | 
text          |  Algorithm 3: Format switching algorithm
              |    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
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              | 
              | 
              | 
title         | 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
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text          | Table B.1: Covariates for the format switching strategy multinomial logic regression.
meta          |                                                                                                                             APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3
text          |                                                   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
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text          |                            Table B.2: Covariates for the purchase strategy logic regression.
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              | 
              | 
              | 
meta          |                                                                                                                             64
              | APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3                                65
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              | 
              | 
title         | B.4    First stage estimates: Dynamic model
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text          |                           Top 1 Owner   Top 2 Owner       Top 3 Owner
              |                  Buyer       0.5127          0.3423         0.2608
              |                  Seller     −0.3772         −0.2792         −0.0257
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text          |       Table B.3: Station purchase policy estimates - buyer/seller dummies
blank         | 
              | 
              | 
text          |                                               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
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text          |          Table B.4: Station purchase policy estimates - other variables
meta          | APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3                                                              66
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              | 
              | 
              | 
text          |                    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
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text          |          Table B.5: Format switching policy estimates - format dynamics
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              | 
              | 
              | 
text          |                       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
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text          |       Table B.6: Format switching policy estimates - current demographics
meta          | APPENDIX B. ADDITIONAL MATERIAL TO CHAPTER 3                                            67
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              | 
              | 
              | 
text          |                   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
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text          |      Table B.7: Format switching policy estimates - demographic dynamics
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