The effects of piracy on competition: Evidence from subscription TV

Accepted Manuscript

The Effects of Piracy on Competition: Evidence from Subscription TV Christian Rojas Associate Professor , Arturo Briceno ˜ PII: DOI: Reference:

S0167-7187(17)30309-0 https://doi.org/10.1016/j.ijindorg.2018.07.003 INDOR 2460

To appear in:

International Journal of Industrial Organization

Received date: Revised date: Accepted date:

18 May 2017 21 June 2018 9 July 2018

Please cite this article as: Christian Rojas Associate Professor , Arturo Briceno ˜ , The Effects of Piracy on Competition: Evidence from Subscription TV, International Journal of Industrial Organization (2018), doi: https://doi.org/10.1016/j.ijindorg.2018.07.003

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Highlights We study the effect of piracy on competition in the subscription TV market in Perú. We estimate a demand model considering illegal connections as an option. We measure market power and the degree of substitutability of the pirate option. We find that the illegal market exerts significant competitive pressure on legal operators Illegal market should be considered as part of the relevant antitrust market.

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The Effects of Piracy on Competition: Evidence from Subscription TV Christian Rojas

Abstract

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Arturo Briceño*

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Competition studies that focus on antitrust issues (e.g. market definition, market power) are typically conducted in markets where all firms are assumed to operate legally (competitors are tax-abiding entities, pay for all inputs used in their production process, have paid the proper government licenses to do so, etc.). We investigate competition issues in a market characterized by widespread piracy: subscription TV in Perú. Estimates suggest that 50% of subscription TV users in Perú (30% in Latin America) use an illegal provider. We make use of a unique dataset in which households provided crucial information regarding the (il)legality of their paid TV supplier. Using quantitative antitrust tools based on demand estimation techniques, we study the impact that the presence of the ‘informal’ sector has on competition. Our estimates suggest that the illegal operators constitute a close substitute for (and henceforth significantly constrain the pricing power of) legal operators. This finding can have important antitrust implications: the failure to account for piracy could lead to erroneous conclusions regarding market power measurement and the delineation of the relevant (antitrust) market. This may be particularly important in several industries (especially in the developing world) where the leading operator may be cataloged as “dominant” only in the absence of illegal providers.

Keywords: piracy, competition, subscription TV, demand estimation, antitrust analysis

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JEL classification: D62, D12, L86, L41

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* Christian Rojas is Associate Professor in the Department of Resource Economics at the University of Massachusetts Amherst (contact: [email protected]). Arturo Briceño is Principal at Business Economics Consulting LLC, Michigan.

1. Introduction

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While empirical industrial organization and antitrust studies have propagated in many industries and across the world, their use has been limited in developing countries. Developing countries have a myriad of limitations that thwart these endeavors, including data (un)availability, little or no antitrust enforcement, and fragile institutions. In this paper we focus on one important element that is present across industries in much of the developing world and that can have important implications for empirical analyses of markets: the presence of illegal competitors. Illegal provision can be defined in numerous ways. This can range from the provision of an illegal/banned product (e.g. drugs) where all the transactions that occur are outright illegal, to the provision of a legal product by a legally constituted firm that does not fully respect the law (e.g. a car manufacturer that holds all necessary permits for production but manages to evade carbon emissions regulations).

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In this paper, we are interested in a market where the service itself is legal (TV subscription), but there are a number of providers that sell it through different illegal means (we later detail the methods through which this occurs). In this paper we refer to this activity as piracy, but use the term piracy and illegality interchangeably throughout the text. Piracy in subscription TV in Perú is rampant: 50% of subscription TV users are estimated to obtain their subscription TV signal through an illegal provider (in Latin America this statistic sits at 30%; Alianza, 2015). Data for other countries in the developing world suggest this problem is also widespread. In 2011, an estimated 92% of TV subscriptions in Arab countries were considered to be illegal while it is estimated that cable and satellite operators in China (nearly 50% of TV subscriptions are in Asia; Satellite Markets, 2010) lose nearly $ 2 billion/year due to piracy (Havocscope, 2017).

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A central feature of our study is the availability of data on illegal TV subscriptions. Specifically, we make use of a representative household survey administered by the Peruvian telecom regulator in which households provided information that allows us to determine, for a large fraction of households, whether the TV content supplier is illegal. Further, the survey contains information regarding the service, including the price paid as well as several characteristics (e.g. basic v. premium content; cable v. satellite; etc.). Official statistics (which exclude illegal connections) indicate that 63% of subscription TV users are served by the leading operator (Telefónica). Employing the typical approach of using market share to proxy for market power, antitrust authorities might deem this company as dominant (or to have significant market power). Contrary to this conclusion, if one includes illegal connections as part of the relevant market, Telefonica’s market share falls by more than half, to 34%. This stark difference in market share suggests that (ignoring the usual pitfalls of using 3

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market share as proxy for market power) antitrust authorities may obtain an erroneous conclusion about the existence of a dominant firm in the market.

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This large difference in market shares between two different market definitions (one containing illegal providers, the other one excluding them) motivates us to study the impact of piracy on competition using quantitative antitrust tools (demand estimation, Lerner Indices and diversion ratios). Our estimates suggest that, after controlling for all product characteristics that drive demand (as well as endogeneity), not accounting for piracy leads to erroneous conclusions regarding the determination of substantial market power and the delineation of the relevant (antitrust) market. Specifically, we find that mark-ups of the three leading (legal) operators (which together capture 92% of the legal segment) are relatively modest and similar across firms (between 33.5% and 39.7%); interestingly, the largest firm, Telefónica seems to have limited market power as it registers the lowest Lerner Index (33.5%). While own-price elasticities (and the implied Lerner Indices) are suggestive of the degree of market power in the industry, diversion ratios provide direct evidence regarding the competitive pressure that legal providers (especially Telefónica) face from piracy. To illustrate this point, Telefónica’s diversion ratio to illegal providers (13.1%) is of similar magnitude than that observed towards its next two largest legal competitors (11.7% to 14.7%).

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To drive our point further, we re-estimate demand and Lerner Indices by removing pirate connections from the choice set (i.e. ignoring their existence) and find that, under this (incorrect) counterfactual, the leading operator would register the highest mark-up of all three leading operators thereby reversing the finding that is obtained when piracy is accounted for in the analysis. The intuition for this result is straightforward: if an important competitor is not accounted for in the analysis (in this case a fringe of illegal providers), the estimation fails to capture the competitive pressure that the removed competitor exerts over other firms.

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While the survey information allows us to determine whether some connections are illegal, we cannot do so for all survey respondents.1 This means that our empirical analyses utilize a conservative measure of piracy. This feature implies that our results would only be strengthened if we had more precise information regarding piracy. Specifically, if piracy were completely accounted for in the analysis, legal operators’ market power would be even lower than what we estimate.

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While we use the Peruvian subscription TV market as a case study, the widespread prevalence of various variants of illegal provision of goods and services in the developing world (e.g. informal markets, copyright infringement, counterfeiting, etc.) suggests that our findings may have important implications elsewhere. More generally, our results highlight a tension that policy makers in developing countries might encounter. On the one hand, turning a blind eye on the illegal provision of certain goods and services (e.g. copyrighted digital material, reversed1

The details of the information provided by households in the survey that allows us to classify whether a connection is illegal or not is described in section 2.2.

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engineered patent-infringing medicines) may result in a (short-term) benefit since the market power of law-abiding firms might be constrained thereby resulting in increased consumer welfare. Conversely, strict enforcement of the law can afford the (long-term) benefit of providing the right environment (incentives) for increased investment and, thus, the delivery of services; this option, however, may come at the cost of a large (short-run) loss in consumer welfare (e.g. Chaudhuri, Goldberg, Jia, 2006).

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We are not aware of empirical studies using structural methods that carry out similar competition analyses in the presence of piracy. A related literature consists of reduced-form empirical studies that investigate the effects of digital piracy (i.e. counterfeit DVDs, illegal downloading, file-sharing) of video and/or audio content on industry performance. A typical study in this literature aims to find the causal relationship between piracy intensity on the music (or motion picture) industry, and sales. Earlier studies focused on hard good piracy (hard copies of the material such as DVDs or CDs). More recently, the focus has been on soft goods piracy (file sharing, illegal downloading). The results of most of these studies favor the hypothesis that piracy has had a negative effect on industry sales (e.g. Rob and Waldfogel, 2006; Zetner, 2006).2

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Two other studies are also related to our work; both endeavors estimate demand for differentiated products to study the role (or effect) of an informal/illegal fringe. Salvo (2009) studies competition in the Brazilian soft drinks industry with a focus on the emergence of a competitive fringe of informal suppliers characterized by a small-scale operation and low prices. Salvo uses the estimated demand parameters to evaluate whether the low prices offered by the fringe are the result of: a) tax avoidance by the informal competitors (as claimed by the incumbent large-scale manufacturers), or b) a larger price sensitivity (and hence lower market power) for the fringe products. Salvo finds evidence for the latter hypothesis; further, and related to our study, Salvo reports evidence that is consistent with the fringe imposing important competitive pressure on the established soft drinks companies. Conversely, Chaudhuri, Goldberg and Jia (2006) use the demand estimates to study the welfare effects of banning patent-infringing versions of anti-bacterials in India. The authors find that, because of the low prices offered by the patent-infringing products (and their corresponding competitive pressure on patented versions), consumer welfare would dramatically decrease as a consequence of the ban (prices could increase between 100% and 400%). Finally, there is a set of papers that have set out to estimate demand models for subscription TV in North America and Europe to address a variety of empirical questions. Chipty (2001) uses demand elasticities in a model aimed to study the effects of vertical integration in the U.S. cable TV industry. Goolsbee and Petrin (2004) use their estimates to study the competition and welfare effects of satellite TV entry in the U.S. market (previously dominated by cable 2

A few papers have found that piracy has not affected (and perhaps has helped) industry performance (e.g. Oberholzer-Gee and Strumpf, 2007). Extensive reviews of this empirical literature can be found in Oberholzer-Gee and Strumpf (2010) and in Liebowitz (2016).

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technology).3 Crawford and Yurukoglu (2012) estimate a model for the U.S. to study the welfare effects of bundling in the U.S. subscription TV market. Pereira, Ribeiro and Vareda (2013) carry out demand estimation exercises similar to ours to investigate whether bundled services in the Portuguese telecom industry are part of the same relevant market as standalone subscription TV. Finally, Byrne (2015) obtains demand estimates in the Canadian cable TV industry to study whether the marginal costs implied by different models of competition are validated by actual cost data. As we later discuss, our paper shares (and borrows from) methodological features of some of this literature, in particular Goolsbee and Petrin (2004), Petrin and Train (2010) and Pereira, Ribeiro and Vareda (2013).

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The paper proceeds as follows. Section 2 provides an overview of the subscription TV market in Perú and other important institutional details. Section 3 describes the model and provides a discussion of how we identify illegal connections. Section 4 describes the data and discusses the results. Section 5 concludes.

2. The Subscription TV Market in Perú 2.1 Market Structure

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Based on GDP, Perú (approx. population 32 million) is ranked as the 7th largest economy (out of 32 countries) in Latin America. As of 2013, approximately 20% of Perú’s 7 million households were subscribed to a legal operator (ERESTEL, 2013; OSIPTEL, 2015).4 When considering illegal providers, this share was 40.2% (ERESTEL, 2013). The three main legal operators are Telefónica (under the Movistar brand), América Móvil (under the Claro brand) and DirecTV. All three companies belong to telecom multinationals (Telefónica from Spain, América Móvil from México and AT&T) with presence in Latin America, Europe and the United States.

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The two largest operators (Telefónica and América Móvil) are multiproduct operators, providing several telecommunication services throughout the country. In addition to subscription TV, these operators are in a position to offer other traditional telecom services (fixed telephony, mobile, internet, etc.). Both companies initially started the operation of the subscription TV service through coaxial cable and later added satellite as an alternative. Telefónica was the first entrant in 1998. América Móvil formally entered in 2006 through the acquisition of Telmex (Telefónica’s main competitor in the subscription TV market at the time) and several other local operators. DirecTV was the latest entrant; it started operations in 2007.

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Using a subset of the data employed in Goolsbee and Petrin (2004), Petrin and Train (2010) carry out related work where they propose a control function approach to deal with price endogeneity. Shu (2010) extends and complements Goolsbee and Petrin’s work. 4 We focus the statistics on the year 2013, the year when the data available for our demand estimation exercises was available. The status of the market for the years 2014 and 2015 was not materially different (2016 data still not made available to the public at the time of this writing).

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Besides the three largest operators, there are over 90 companies that have secured a license to operate as a subscription TV provider. The main requirement for a license-holder (including the three largest firms) is to pay regulatory fees equivalent to 2% of gross revenues on a regular basis. As opposed to the three major players, these smaller companies do not have national presence and operate at a regional scale throughout the country.5 While these smaller-scale license holders may be considered legal operators, they are suspected to be important contributors to illegal provision through under-reporting (declaring to the regulator a smaller number of subscribers than actually served thereby reducing their regulatory fee payment) or illegal retransmission of other operators’ signals. Finally, there is an unaccounted number of outright illegal providers that operate in an informal fashion (with a phony company name, or no company name at all). In the next subsection we provide additional details on the different forms of illegality.

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The market structure in 2013 derived from the official regulator’s (OSIPTEL) statistics (obtained from the number of subscriptions that operators have “declared” to the regulator) puts Telefónica at the top with a 63.2% market share, leaving 14.8% to América Móvil and 13.6% to DirecTV. According to these official statistics, the Herfindahl-Hirschman Index of concentration, at 4,428, suggests subscription TV is a highly concentrated industry.6 2.2. Piracy

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Piracy in subscription TV can be carried out in several ways (Alianza, 2015)7: Illegal Signal Retransmission: This version requires “free-to-air” (FTA) equipment that “steals” the satellite signals through which legal operators transmit their content. Using decoding equipment and software, the pirated signal is then used to deliver the (decoded) service to the final user.8

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Under-reporting. As already stated, license-holders have an incentive to under-report subscribers to the regulator. In addition, many of these operators pay content providers (especially international providers) on a per-user basis, further intensifying the incentive to under-report.

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Physical plant piracy. Under this version, the actual infrastructure and service of the legal operator is put to illegal use. There are variants of this type of piracy, ranging from users “sharing” (without demanding payment) their legal subscription with neighbors (extending a cable from one dwelling to another) to more organized piracy through

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Market shares of these smaller operators are negligible. This index displays some fluctuation, declining from 2012 until 2014 but increasing again (to 2013) levels in 2015. 7 Alianza (www.alianza.tv) is a consortium of content providers (ESPN, FOX, etc.), broadcasters (DirecTV, Telefónica, etc.), and technology providers (e.g. Cisco) whose aim is to combat subscription TV piracy in Latin America. It was founded in January of 2013. 8 According to Telefónica, América Móvil and DirecTV, approximately 70% of piracy occurs under this variant. 6

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which a (pirate) technician “splits”, for a fee, the cable connection of the legal operator into several homes (either directly from a post or duct, or from a legally subscribed home). These users are informally known as colgados (“dangled” subscribers). 

On-line Piracy. An ever growing concern for the industry is on-line illegal streaming and downloading of visual content (in particular live sports events).

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Towards the end of 2013 OSIPTEL carried out a nationally representative survey aimed at obtaining information on the usage of telecommunication services in the residential market. 9 The survey, called ERESTEL (Encuesta Residencial de Servicios de Telecomunicaciones), contained a question regarding whether the household had access to subscription TV. The results of the survey (once extrapolated to the entire population) indicate that 2.87 million households were users of subscription TV. Compared to the 1.44 million subscribers recorded in the official statistics, ERESTEL’s results suggest the presence of over 50% of connected homes that would qualify as illegal.10 As we later explain, if illegal connections were to be considered as part of the relevant market, market shares of the three leading operators would fall by more than half. Finally, ERESTEL contained other questions regarding the characteristics (including price) of the subscription TV service; our demand estimation exercises use the data generated from this survey (we provide more details on the survey and the data in section 3).

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2.3 Subscription Plans

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TV subscription plans in Perú consist of a three-tier system. Basic plans contain local channels plus a set of non-premium content channels (ranging from 74 channels for Claro’s basic plan to DirecTV’s 105). The lowest-priced basic plan (Claro’s) is $20/month (58 soles) and the highest is $32/month (89 soles).11 Premium plans add premium content channels (e.g. HBO, more HD channels, etc.) to the basic plan. Super-premium plans add to premium plans features such as pay-per-view, video on demand, digital recording (as well as additional channels). Premium and super-premium plans are generally priced above $36/month (100 soles).12

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Another feature of commercial offers is the existence of bundled plans. These plans, however, are largely constrained to the two largest competitors (Telefónica and América Móvil) who offer double-play bundles (e.g. TV+internet; TV+phone; TV+mobile) as well as triple-play options. Bundling is still of limited popularity; according to ERESTEL, 84% of users subscribe to

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ERESTEL contracted with a private surveying company to carry out the survey. The surveys were conducted as one-on-one interviews, which yielded a high response rate (approximately 80%). The target sample size in the survey design factored in this response rate. 10 The proportion of illegal connections can potentially be larger since ERESTEL only covered the residential market. 11 We henceforth use an exchange rate of 2.8 soles per US dollar (the exchange rate in December 2013). 12 One drawback of ERESTEL is that it is not possible to determine the tier of the household’s TV plan.

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TV as a stand-alone service. While DirecTV does not offer bundles,13 it has richer variety of channels (its plans usually offer more channels overall as well as more channels in HD); these plans are generally more costly than those offered by its competitors. As a consequence, DirecTV is generally regarded as a higher-quality provider.

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While illegal providers also differentiate their plans by tiers, their offers are much more limited. For instance, pirate operators are unlikely (or unable) to provide options such as pay-per-view. Further, the quality of these providers is considered to be lower as service interruptions occur frequently and signal reception might be poorer (there is no customer service department nor can customers file complaints to the regulator). Given their illegal nature and their noted lower quality, pirate plans are cheaper than those provided by legal means (we later discuss pricing statistics for our sample).

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3. Model and Illegality 3.1 Model

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Our approach uses quantitative methods for measuring market power and delineating relevant markets that rely on structural parameters of demand (e.g. Nevo, 2001; Davis and Garcés, 2010). The underlying model assumes static Bertrand-Nash competition, differentiated products and constant marginal cost of production (arguably, a reasonable assumption in this market where the main cost of production is content fees14). The approach is straightforward. To operationalize market power measurement in the absence of cost data, the approach relies on the firms’ first order conditions and the estimated structural parameters of demand. The result is an index of market power (Lerner Index) that captures the margin of the firm (price over marginal cost).

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To operationalize relevant market delineation (evaluating whether a service or product is a sufficiently close substitute in the studied market), a well-accepted measure are diversion ratios (Davis and Garcés, 2010). Since the only ingredients needed for market power measurement as well as for the market delineation exercises are the own (and cross) price elasticities of demand, we focus this discussion on the demand model.

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We employ a logit model of demand, which is derived from the following utility maximization problem by consumers: [

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( )

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∑ ∫

) (

( )

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(

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where subscripts denote the household and the option being evaluated; refers to either one of the TV subscription plans available to the consumer (where plans of illegal operators are included) or the no-purchase decision ( ); subscript denotes the product characteristic (other than price) of the option being evaluated by the household. The variables denote price and the (value of the) product characteristic of the corresponding option. Coefficients and ( ) are the object of estimation. We consider simple and random coefficients versions of the resulting logit specification. The simple logit choice probabilities are obtained by assuming a type-I extreme value distribution for ; the (normally distributed) random coefficients logit choice probabilities are obtained by first ∑ defining ̅ in (1) and then assuming a type-I extreme value distribution for ̅ . The resulting choice probabilities for the two logit variants are (respectively): )

(̅ ) ( ) ∑ (̅ )

( )

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∑ ∑ where, , ̅ , and is household ’s vector of (independently distributed) standard normal draws. Estimation ) for the simple logit and ( of ( ) in the random coefficients case, is carried out via likelihood methods (simulated ML in the random coefficients case). We cluster standard errors at the department level (we later provide more detail regarding geographic heterogeneity in service provision).

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In both variants of the model, we employ the alternative-specific conditional logit specification, which is equivalent to including household-specific fixed effects (McFadden, 1974).15 We also include a household-specific variable (e.g. income) in the model (not shown in equations 1 and 2); since the model has household fixed effects, the household-specific variable enters the model through interactions with each of the options considered (i.e. the model becomes a mixed logit model; see Cameron y Trivedi, 2005). Own- and cross-price elasticities are calculated at the operator-level by simulating a 1% price increase across all plans of a given operator (in the case of illegal operators we assume a 1% price increase across all illegal operators).

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We also explored a variant of the model with a nesting structure, but was rejected since it produced results inconsistent with economic theory.

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3.2 Choice Sets and Endogeneity

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ERESTEL provides household-level information of both users and non-users of subscription TV. In either case, we do not observe the choice set the household was exposed to, only the option that they decided to choose. One solution for defining the choice set would be to assume that households observe all possible plans by all operators in their geographic area. In our application, however, this assumption does not seem reasonable. We define 25 mutually exclusive geographic areas corresponding to Perú’s 25 departments. Within each department, the universe of choices can be very large since the average number of operators per department is 13; further, within each operator, there exist several possible plans to choose from. Thus, it does not seem realistic to assume consumers consider this large full choice set when making a decision.

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To circumvent this issue, we employ an imputation technique introduced by Train, McFadden and Ben-Akiva (1987) that has been employed in recent work (Pereira, Ribeiro and Vareda, 2013). In a nutshell, the technique randomly samples a subset of choices (where a choice is defined as an operator-plan pair) from the universe of choices potentially available in the geographic area the consumer lives. This random sampling is done one household at a time and considers all plans made available by providers (legal and illegal) in the area. We make the size of the subsampled choice set to be proportional to the number of operators in the area; that is, imputed choice sets of households living in departments where there are many operators will have a larger imputed choice set.

operators in department

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The results we report below use a choice set of size and

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is the number of imputed “inside option” choices. Thus, a

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consumer choosing a plan from an operator in a region where 10 operators provide service will have a choice set size equal to 7 (the chosen option plus

additional possible “inside

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options”, and the no-purchase option). While this choice for the imputed choice set may seem arbitrary, it does not have any material impact on our results (we experimented with values larger and smaller than without any changes in our conclusions). Further, our results were not

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sensitive to different (randomly sampled) imputed choice sets.17 To reiterate, the imputed

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When a household chooses the no-purchase option in the survey, we impute (instead of just ) “inside option” choices. We do this to preserve consistency of imputed choice set sizes across households within a region. 17 We carried out this sensitivity analysis by employing different seeds in the random sampling algorithm. Our estimated coefficients remained unchanged to the second (and often third) decimal place.

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choice set is obtained by sampling over all operator-plan pairs (including legal as well as illegal firms) in the geographic area where the household resides.18

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A second issue to be addressed is price endogeneity. We employ Petrin and Train’s (2010) control function approach. Specifically, we control for the endogeneity of price by running a first stage regression with price as a dependent variable and all exogenous variables (included and excluded instruments) on the right hand side (in the next section we detail the instruments used). We then employ the residual of this regression as an additional explanatory variable (the control) in the estimation of (2 and 2’). 3.3. Illegality

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A crucial feature of our study is the availability of data that captures illegal subscriptions. Here, we elaborate on the features of the survey that allow us to determine illegal provision, but state at the outset that, while we are able to determine with great confidence the illegality of an important portion of subscriptions (approximately 32% of surveyed households)19, we cannot do so for all households.

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In one of ERESTEL’s questions, households are asked to provide the name of their subscription TV supplier. A priori, one would imagine that the identity of the supplier can be used to determine illegality (by contrasting the provided name to the list of operators registered with the regulator). The main problem with this approach is that many illegal users actually report the name of a legal operator; this is not surprising given the piracy methods discussed earlier (i.e. if an illegal connection steals Telefónica’s signal, the user would view Telefónica-related content/channels and associate the service to Telefónica and not to the pirate company/individual that provided the service).20 This issue can be observed in Table 1, which contrasts the difference in subscription TV users between ERESTEL (survey weights used to extrapolate results to the entire population) and those reported by legal providers to the

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Alternatively, one can carry out the imputation at the provider level. That is, one samples a subset of providers and chooses all plans offered by that provider in the imputed choice set. This assumption implies that the household considers all the plans offered by a given provider. This could be a reasonable assumption for some households but not others (i.e. those that are cash-constrained). We decided to define a choice as a provider-plan pair because it seems to be a more reasonable assumption in a developing country (where many households may be cash-constrained). In alternative specifications (available upon request), we verified that defining choices at the provider level did not alter our conclusions. 19 This number increases to 40% when results are extrapolated to the entire population using survey weights. 20 In our data, all illegal connections are reported to arrive to homes via terrestrial transmission. However, it is not feasible for us to determine the type of piracy that an identified illegal connection belongs to; the illegal connection can correspond to an illegally retransmitted signal (one that uses aerial technology to steal the signal and then is terrestrially transmitted to homes) or to physical plant piracy (see section 2.2).

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regulator (OSIPTEL). An exception is DirecTV, which shows a larger official statistic than what is reported by households.21

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To create a measure of illegality, we, instead, use the information provided by two other questions in ERESTEL: “Do you share your TV subscription with another household?” and “How much do you pay for the service?” A shared connection is outright illegal and therefore we proceed to classify an affirmative answer to the first question as an illegal connection (18% of households answered affirmatively to this question). We note, however, that since some households may be concerned about reporting their subscription as being informal/illegal, the actual fraction of households sharing a connection is certainly higher than what we are able to identify.22

OSIPTEL [2] 896 210 193 119 1,418

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Telefónica América Móvil DirecTV Other Operators Total

ERESTEL [1] 1,430 414 109 925 2,877

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Table 1: Survey (ERESTEL) versus Official (OSIPTEL) Statistics on TV Subscribership (‘000 subscribers) Absolute Difference [1]-[2] 498 214 -106 932 1,437

% Difference [1]/[2]-1 60% 97% -44% 677% 100%

Note: data corresponds to the 2013 year. ERESTEL figures are obtained by applying survey weights.

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We also use the answer provided to the second question, together with a bound on the lowest feasibly legal price, to generate a second criterion for illegality. One option for this lower bound is the lowest price offered by the legal operators ($20 at the time of the survey). We err on the conservative side, however, and use a lower threshold. Specifically, we reached out to legal operators and asked about the per-subscriber fee that they pay to content providers. We were told that an absolute lower bound (the minimum price for which a basic plan can be obtained in the legal wholesale market) was $14/month/subscriber (~40 soles). ERESTEL’s surveyors

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We inquired about this result but did not obtain a reasonable explanation. One possibility may be that corporate/commercial users (not included in the survey) are disproportionally more likely to use DirecTV than other providers. 22 The fact that an important share of surveyed households (18%) admit (indirectly) to using an informal provider is noteworthy. This fact is consistent with the widespread nature of informality in the market, suggesting that informality might be a well-accepted norm in society.

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requested to see, if available, a receipt for the subscription TV service (39.5% of surveyed households presented a receipt).23

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Using these two questions, we construct two criteria for illegality. Under the first criterion, a household is classified as having an illegal subscription if it paid less than $14/month and the household showed a receipt to the surveyor; 17.5% of households that have a TV subscription fall into this definition of illegality. The second criterion is broader and classifies a household as having an illegal subscription if it reported to have a shared connection (first question) or if it reported a price below $14 (with receipt); 32.5% of households with a TV subscription fall into this category).24

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We carry out estimations under both criteria to assess the robustness of our results. We note, however, that both our measures of illegality underestimate the actual extent of piracy (recall that ERESTEL’s results revealed that more than 50% of households might be illegal users). An important implication of our under-measurement of piracy is that our main result (that piracy is creates significant competitive pressure on legal operators) would be strengthened if we used a more precise measure of illegality.

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4. Data Preparation, Data Description and Results 4.1. Data Preparation

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The original ERESTEL database contains 13,716 households. We exclude households for which subscription TV is not a viable option; these households include those that do not own a TV and those that do not have electricity. This selection procedure results in 11,273 households.25 For those households that do not show a receipt to the surveyor, the monthly price of their subscription is registered as a categorical variable: less than 50 soles ($18), between 50 and 100 soles ($18-$36), between 100 and 200 soles ($36-$72) and greater than 200 soles. For these households, we code the price variable as the midpoint in the range (for the >200 soles category, we use 250 soles). Households provided information about the operator they were subscribed to. Using this information, we create indicator variables for the different operators; to operationalize this, we group operators in 5 mutually exclusive categories: Telefónica, América Móvil, DirecTV, Others and Illegal. The category “Others” lumps all operators (141 in 23

The large fraction of households presenting a receipt is notable. This is likely due to the one-on-one interview design, which is arguably more effective at obtaining this evidence (i.e. the respondent does not have to go through the trouble of making a copy of the receipt and including it in the mail). 24 The two criteria do not define mutually exclusive households (3.4% of surveyed households meet both criteria). 25 We also excluded households that reported to have a satellite provider that was not part of the only three operators that provide this service (DirecTV, Telefónica and América Móvil). The reason is that these types of users are likely to have a service that is not comparable to those offered by other operators in the market.

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our sample) that do not correspond to the top three and that are not deemed to be illegal by our measure (the overwhelming majority of these operators have negligible market shares; this group can hence be considered as a competitive fringe); if a household did not recall the name of the operator they are subscribed to, we assign the household to the “Others” category.

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4.2 Description of Data and Variables Used

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Table 2 shows the descriptive statistics of the variables used in the estimation. While the number of operators per department is not directly used in the estimation, it is used to generate the imputed choice sets (see section 3.2). The logit model includes price, operator dummies (Telefónica, Claro, DirecTV, Illegal)26, a satellite dummy27, a dummy for bundled service (double or triple play), and monthly household expenditures. As explained earlier, we consider two possible definitions for the dummy variable “Illegal”: when the household shares its TV subscription plan or pays less than $14 (which we label “Illegal”) and/or when the household shows a receipt with an amount lower than $14 (“Illegal, Price”).28 While we would have liked to have variables to control for the quality of the package (i.e. dummies for the plan tier), the survey does not provide enough information to include this control.

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Table 2: Descriptive Statistics of Variables Used

S.D. 8.90 643 5,046 41.66 n.a. n.a. n.a. n.a.

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Variable Mean Number of Operators per Department* 13.12 For All Households (11,273): Total Household Expenditure (soles/month) 1,177 Density at District Level (pop. per square kilometer)¥ 2,432 For Households with TV Subscription (4,103; 36.4%):‡ Price (soles/month) 52.61 Unbundled Service 83.65% Doble-Play Bundle 4.22% Triple-Play Bundle 12.14% Satellite 13.00% 26

The reference category is “Other Provider”. Since the survey measures reported here have not been adjusted with survey weights, the mean of the operator dummy variables should not be interpreted as representative market shares for the whole country. 27 DirecTV is a satellite-only provider. The Satellite dummy is included since Teléfonica and América Móvil provide their service to some users via satellite. Providers in the “Other” or “Illegal” categories do not offer satellite service. 28 The percentage of users using an illegal provider (using our broader criterion for piracy) varies substantially across departments (mean: 30%; max: 66%, min: 5%). One way our model accounts for this variation through the procedure used to identify a household’s choice set (which is specific to the department where the household resides).

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Telefónica América Móvil DirecTV Other Provider Shares Connection or Price < 40 soles/month [“Illegal”] < 40 soles/month (with receipt) *“Illegal, price”]

n.a. n.a. n.a. n.a. n.a. n.a.

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There are 25 departments. There are 259 districts. For dummy variables we only report the mean.

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*

26.69% 7.58% 4.29% 29.00% 32.43% 17.50%

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The main challenge of the first stage regression is obtaining valid instruments. An ideal candidate would be a cost shifter that varies by operator and region. Unfortunately, no such data exists. We employ an approach suggested by Villas-Boas (2006) and successfully used in other work (Chintagunta et al., 2002; Hellerstein, 2008): we interact operator dummies with a proxy of cost. Since we do not have cost-level information for the industry, our measure (proxy) of cost is population density at the district level (there are 259 districts in Perú).

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Population density is arguably related to the cost of providing the service as more populated (urban) areas are likely to have higher marginal costs (e.g. salaries). On the other hand, there might be some economies of scale related to providing the service in more densely populated areas (e.g. a technician needs to travel less in between installations, one antenna or cable might be used to connect multiple homes, etc.). Further, it is plausible that the costs of providing the service for illegal operators also varies across districts because of the varying degree with which regulatory enforcement is carried out: piracy is combatted with greater intensity in more densely populated areas which, in turn, decreases the relative cost of illegal providers in rural areas. In short, population density (to the extent it proxies for the cost of subscription TV provision) may be related to operators’ pricing in a heterogeneous way. Using this intuition, we create instruments by interacting operator dummies with district-level population density; these interactions capture (proxy for) an operator-district specific cost shifter (which, in turn, should be correlated with price and arguably uncorrelated with the demand shock). 29 In addition to these interactions, we also include department fixed effects in the first-stage regression. Our first-stage regressions (reported in the appendix) and the reduction on the price coefficient bias that we observe when including the instruments provide confidence in this approach. 4.3 Results

Regression Results

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The 259 districts in Perú are narrowly defined and their sizes vary inversely to population density, in accordance with the intention of what our proposed IV intends to capture. Peruvian districts are not as fine as U.S. 5-digit zip codes, but seem to be significantly finer than 3-digit zip codes in the U.S.

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In the tables below we present 4 specifications: simple logit OLS, simple logit IV (without survey weights), simple logit IV (with survey weights) and random coefficients logit IV (without survey weights). Instrumental variables (IV) regressions include the coefficient on the residual obtained from the first stage regression (the control function). Table 3 presents results with our first criterion of piracy (Illegal, Sharing) while table 4 reports results for the second criterion (Illegal, Price). In section 4.4 we discuss several robustness checks performed.

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The estimated price coefficient is always negative. Importantly, the magnitude of the price coefficient increases by an order of magnitude (in both tables) when instruments are included (first-stage regressions are shown in the appendix). This evidence indicates that our approach for correcting price for endogeneity is adequate (the elasticities implied by the estimated models that we later report are consistent with this assessment). Further, the control function coefficient (which can be interpreted as a direct test of endogeneity) is always statistically significant.

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There is some inconsistency in the estimated coefficients for the Telefónica and DirecTV dummies, but the América Móvil and the Illegal dummy is always estimated to be negative (the dummy for América Móvil is not always statistically significant and tends to be smaller in absolute value than the dummy on Illegals). This suggests that the illegal option is perceived as the least preferred option. Conversely, once endogeneity is controlled for, bundled and satellite services are preferred over other plans.

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Finally, the coefficients on the interaction of the household expenditure variable and the operator dummies make economic sense. All interaction coefficients in the IV regressions are positive which indicates that households with larger expenditures (and therefore income) place a greater value to subscription TV (the left out category is the no purchase option). In addition, the relative magnitudes of the interaction coefficients are consistent with what one might expect: DirecTV and Telefónica display the largest magnitudes whereas Others and Illegals display the lowest values. Table 3: Demand Estimation Results, First Definition of Piracy (Sharing a Connection and/or Price < 40 soles / month)

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Variables Price

DirecTV

Telefónica América Móvil Illegal

Spec. 1 (logit OLS) -0.00868*** (0.001) -2.873*** (0.181) -3.187*** (0.087) -2.735*** (0.112) -2.611***

Spec. 2 (logit IV)

Spec. 3 (logit IV)

-0.0703*** (0.002) 0.827*** (0.199) -0.167 (0.112) -0.522*** (0.123) -1.119***

-0.0627*** (0.002) 0.267 (0.286) -0.119 (0.164) -0.572** (0.178) -1.177***

Spec. 4 (R.C. Logit IV) Mean SD -0.0729*** -0.0079** (0.006) (0.004) -2.649 -3.165** (2.536) (1.406) -0.959 -1.354*** (0.696) (0.439) -0.493 -0.255 (0.307) (0.424) -1.221*** -0.671

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(0.072) (0.079) (0.105) (0.389) (0.620) 0.251*** 3.263*** 3.089*** 3.224*** -0.259 (0.053) (0.091) (0.126) (0.311) (0.158) Satellite -0.0692 0.218*** 0.122 0.204** (0.060) (0.061) (0.099) (0.066) HH Expenditure x : Telefónica 0.000780*** 0.0011*** 0.0009*** 0.0014*** (0.000) (0.000) (0.000) (0.000) América Móvil 0.000395*** 0.0008*** 0.0006*** 0.0008*** (0.000) (0.000) (0.000) (0.000) DirecTV 0.000721*** 0.0014*** 0.0015*** 0.0020*** (0.000) (0.000) (0.000) (0.000) Others -0.00159*** 0.0003*** 0.00002 0.0004 (0.000) (0.000) (0.0001) (0.000) Illegal, Sharing 0.000229*** 0.0064*** 0.0005*** 0.0007*** (0.000) (0.000) (0.000) (0.000) Control Function 0.0695*** 0.0633*** 0.0686*** (0.002) (0.002) (0.006) Log-Likelihood -17,290.73 -16,303.49 -11,759,652 -16,256.42 Chi2 (p-value) 0 0 0 0.00 Pseudo-R 0.3093 0.3487 0.28 Instrumental Variables? No Yes Yes Yes Model Simple Logit Simple Logit Simple Logit Random Coefficients Logit Survey Weights No No Yes No # Observations 123,877 123,877 123,877 123,877 Significance: *** (1%), ** (5%), * (10%). Standard errors are clustered at the department level.

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To conclude, we note that results are qualitatively similar across the two tables and that they do not appear to be very sensitive to the use of survey weights. Given these observations and the flexible nature of the random coefficients variant (which removes the independence of irrelevant alternatives property), we use the results from specification 4 (table 3) for the computation of elasticities and the indices that we report next.

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Table 4: Demand Estimation Results, Second Definition of Piracy (Price < $14/month)

Price

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América Móvil Illegal, Price Bundle Satellite

Spec. 1 (logit OLS)

Spec. 2 (logit IV)

Spec. 3 (logit IV)

-0.0119*** (0.004) -2.242*** (0.262) -2.772*** (0.204) -2.362*** (0.270) -2.314*** (0.315) 0.270*** (0.097) -0.329*** (0.080)

-0.0750*** (0.006) 1.154*** (0.344) 0.257 (0.241) -0.318 (0.203) -1.465*** (0.203) 3.212*** (0.280) 0.141** (0.062)

-0.0685*** (0.006) 0.684** (0.311) 0.401** (0.179) -0.381 (0.218) -1.611*** (0.354) 3.089*** (0.268) 0.0206 (0.102)

Spec. 4 (R.C. Logit IV) Mean SD -0.0778*** -0.0095** (0.006) (0.004) -1.034 -2.339*** (1.558) (0.908) -1.015 -1.733*** (0.655) (0.417) -1.456** -1.745*** (0.739) (0.527) -1.445*** -0.281 (0.285) (0.526) 3.153*** -0.104 (0.286) (0.139) 0.140** (0.056)

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DirecTV Others Illegal, Sharing Control Function Log-Likelihood Chi2 (p-value) Pseudo-R Instrumental Variables? Model Survey Weights # Observations

0.0011*** (0.000) 0.0008*** (0.000) 0.0015*** (0.000) 0.0004** (0.000) 0.0005*** (0.000) 0.0741*** (0.006) -16,291.74 0.0 0.3492 Yes Simple Logit No

0.0009*** (0.000) 0.0007*** (0.000) 0.0016*** (0.000) 0.0002 (0.000) 0.0003 (0.000) 0.0688*** (0.005) -11,759,652 0.00 0.28 Yes Simple Logit Yes

0.0015*** (0.000) 0.0009*** (0.000) 0.0019*** (0.000) 0.0005** (0.000) 0.0005*** (0.000) 0.0724*** (0.006) -16,256.42 0.00 Yes Random Coefficients Logit No

123,877

123,877

123,877

123,877

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América Móvil

0.0006*** (0.000) 0.0003* (0.000) 0.0006*** (0.000) -0.0014*** (0.000) 0.00002 (0.000) -17,664.12 0.00 0.2944 No Simple Logit No

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HH Expenditure x : Telefónica

Elasticities, Lerner Indices and Diversion Ratios

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Table 5 displays the resulting operator-level elasticities derived from the estimates in specification 4 in table 3. Telefónica and América Móvil display own-price elasticities of similar magnitude while DirecTV displays a less price-sensitive demand (consistent with the view that it is regarded as a higher quality competitor). Conversely, the own-price elasticity of Others and Illegal show lower absolute values, with Illegal displaying the least sensitive aggregate elasticity; this is not surprising as these elasticities do not correspond to a specific operator but rather to the aggregate group of operators in the category (we expect a single operator in each of these two categories, especially in Illegal, to have a price elasticity that is much larger in absolute value).

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Table 5: Elasticities from Random Coefficients Logit Model Operator

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Telefónica América Móvil DirecTV Others Illegal, Sharing No purchase (antenna) Aggregate Elasticity:

Telefónica -2.979 0.421 0.308 0.451 0.390 0.279

América Móvil 0.121 -2.949 0.081 0.131 0.123 0.111

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Others

0.053 0.050 -2.517 0.043 0.044 0.037

0.236 0.281 0.145 -2.258 0.279 0.253

Illegal 0.253 0.271 0.156 0.290 -1.764 0.224

-0.9371

Note: elasticities in a column are calculated by simulating a 1% price increase across all plans of the operator in that column.

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Central to our question is the magnitude of cross-price elasticities, especially those with respect to the Illegal category. The elasticity table reveals that regardless of the (legal) operator considered in a given column of the elasticity matrix, the Illegal category displays a cross-price elasticity that is of similar magnitude as any of the legal alternatives. For example, a 1% price increase in Telefónica would translate into a 0.39% increase in the demand of the Illegal category and into a 0.421% increase across all of América Móvil’s plans. This observation is confirmed by the diversion ratios obtained for Telefónica: 14.66% with respect to América Móvil, 11.68% with respect to DirecTV, 18.04% with respect to the Others category and 13.09% with respect to the Illegal category. This evidence strongly indicates that the Illegal segment of the market is, as a whole category, a close substitute for the legal options.

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Given that each operator offers a multitude of plans and that not all plans (or operators) are present in all regions, for the ensuing analysis we make the simplifying assumption that each of the top-three operators act, separately, as a single-product firm. This is akin to treating an operator’s profit maximizing problem as an average across all plans. Using this assumption, the resulting Lerner Indices (which, again, can be interpreted as an average across all plans for each operator) as well as the implied marginal costs are displayed in Table 6. We note that the lowest index of market power is displayed by Telefónica (the company with more than 60% of legal connections), a result that we interpret as evidence of relatively low market power by the leading operator. This result is consistent with the important competitive pressure that the Illegal category generates, as evidenced by the large cross-price elasticities (and diversion ratios) with respect to that category. The marginal costs implied by our estimated elasticities range from 37.5 soles (13.4 dollars) to 54.7 soles (19.5 dollars); we consider these values to be sensible since the lower bound (for content fees) provided to us by industry experts was $14 (see section 3).

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Table 6: Lerner Indices and Marginal Costs Operator

Telefónica América Móvil DirecTV

Lerner Index 0.335 0.339 0.397

Implied Marginal Cost (soles/month) 54.696 37.532 47.825

To summarize, the demand estimates as well as the indices derived from them (diversion ratios and Lerner Indices) strongly suggest that piracy not only is a close substitute to the legal operators, but that it significantly constrains the market power that the legal operators might enjoy otherwise. We, again, highlight the fact that our estimates should be treated as conservative since our measure of piracy is able to identify only a portion of the illegal market.

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Counterfactual: Antitrust Analysis that Ignores Piracy

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To drive our point that piracy matters further, we carry out the prior quantitative analyses under the counterfactual that pirate connections are not observed. This scenario resembles a situation in which the analyst, as it is generally the case, only has access to data from legal operators. To operationalize this counterfactual, we classify the connections that we determined to be illegal in the ERESTEL survey as being part of the outside (no purchase) option and proceed to estimate demand and the corresponding elasticities (and Lerner Indices). For this exercise, we utilize our broader illegality criterion (shared connection or price < $14) and a random coefficients specification. Figure 1 contrasts the Lerner Indices in Table 6 to those obtained under the naïve counterfactual just described.30

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Under the (erroneous) counterfactual, Lerner Indices display opposite results to those reported in Table 6: if illegal connections are ignored, the firm with greatest market power is Telefónica and the firm least market power is DirecTV. The differences in Lerner Indices within a firm seem to be significant: an analysis that does not account for illegality would be attributing a mark-up for Telefónica that is 18% (6 percentage points) larger than when illegality is accounted for.

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Figure 1: Lerner Indices of Top Three Firms, With and Without Identification of Illegal Connections

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Markup with Illegal Connections (Table 6)

0.41

39.45%

Markup Ignoring Ilegal Connections

38.96%

0.39

39.74%

0.37

0.35

33.91%

33.56%

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0.31 0.29 0.27 0.25 América Móvil

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While a mark-up of nearly 40% for Telefonica might not be sufficient to label the firm as dominant, we note that we can only partially identify piracy. Thus, a counterfactual that removed all pirate connections would increase Telefonica’s markup (and the resulting difference of between its Lerner Index and that of its rivals) thereby strengthening our finding and conclusions.

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We have illustrated the bias that can arise in quantitative (i.e. demand-based) antitrust analyses when informality is rampant and data is not available to the researcher. These results are consistent with the conclusion that would be obtained if antitrust analysis were to be based on a market-share approach. To illustrate this point, we compare the market shares of the topthree firms in the market under two scenarios: when piracy data is available and when it is not. For the first case, we use ERESTEL’s data, remove pirate connections as per our broader piracy criterion (shared connection or price <$14), and compute the resulting market shares of the top three firms. For the second case, we use Osiptel’s official statistics (see section 2.1) to compute the market shares of Telefónica, América Móvil and DirecTV. The differences are stark: 34.2%, 7.6% and 3.0% (respectively) when piracy is accounted for versus 63.2%, 14.8% and 13.6% when it is not. The HHI is impacted in a similar fashion: it decreases from 4,428 (a highly concentrated industry) when illegality is ignored, to 1,238 (a low-concentration industry) when it is accounted for.31

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Since all non-top-three firms have negligible (or close to negligible) market shares, the calculation of the HHI considers only takes into account the market shares of the top three firms. According to the DOJ/FTC’s merger guidelines, an industry with an HHI of less than 1,500 is considered to have a low concentration; industries with an HHI of 2,500 or more are considered as highly concentrated.

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4.4. Elasticities in the Literature and Robustness Checks

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One way to add credibility to our estimates is to contrast our demand elasticities to those obtained elsewhere in the literature. Most of the estimates reported in earlier work are carried out at either the tier (basic, premium, etc.) or service (satellite v. cable) level. While not comparable to our estimates (since we carry out the exercise at the operator level), we report some of these elasticities here and note that our estimates are of similar magnitude. Chipty (2001) computes elasticities for basic and premium, registering values of -5.90 and -2.00. Goolsbee and Petrin (2004) report own-price elasticities for basic, basic satellite, and premium of -1.53, -3.18 and -2.45, respectively (Petrin and Train 2009 find similar values). Crawford and Yurukoglu (2012) report higher values than the rest of the literature: -4.12 for basic, -6.34 for expanded basic, -13.11 for digital basic and -5.35 for satellite. Finally, Byrne (2015) calculates elasticities for basic cable at -4.21 and for non-basic cable at -5.45. More comparable to our study are the elasticities calculated by Pereira, Ribeiro and Vareda (2013) who report firm-level elasticities in the same ballpark as ours: the values range from -1.3 to -3.2.

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5. Conclusions

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We investigated whether focusing the analysis on the urban areas would yield similar estimates. The reason for doing this is that the presence of the largest company (Telefónica) is stronger in urban areas; taking the legal segment only, Telefónica’s market share in the capital city of Lima (which holds 66% of Perú’s population) is 75% (compared to 63% national). To do this, we constrain our estimation to households that are located in a Department’s capital. Our results also hold in this narrower definition of the market (where the illegal sector is likely to play a smaller role in constraining the market power of the leading operator) thereby providing additional confidence in our overall conclusion. The demand estimation results together with the implied elasticities, Lerner Indices and diversion ratios are reported in the Appendix.

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In this paper we exploit a unique feature of a dataset on subscription TV usage in a developing country that allows us to explore the role of piracy on competition. Applying standard structural industrial organization tools to this data to explore antitrust questions of market definition and market power lead us to a the conclusion that piracy is likely playing a crucial role in this market: illegal provision of subscription TV is a significantly close substitute to legal providers thereby generating an important constraint on the market power of legal operators (in particular the leading operator Telefónica). Further, since we are not able to completely identify all pirate subscriptions in this market, our results should be considered as a lower bound on the bias that the omission of an informal sector has on antitrust analyses.

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Our finding contrasts with an alternative definition of the market that excludes the illegal segment whereby the largest firm might, by the usual market share criterion, be catalogued as dominant. Our results are particularly relevant in the industry we study since the regulator has explored the possibility of imposing regulatory remedies if Telefónica is found to be a dominant operator.

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Some caveats apply to our paper. We utilize survey data which is less ideal than a revealed preference approach. Despite this shortcoming, (part of) our data is verified through receipts that a significant portion of households presented to the surveyor; in this sense, our data is superior to the usual survey databases typically employed. One conclusion of our study is that consumers might benefit from illegal provision because market power of legal operators might be curbed. This finding is in line with prior work that has shown that patent infringement can have large and positive consumer welfare impacts on developing nations (Chaudhuri, Goldberg, Jia, 2006). This result, however, is driven by the static nature of the analysis and the model. An analysis that considers the long-term implications of piracy would include possible negative effects on consumer welfare (e.g. poor investment incentives for legal operators that stall or negate network deployment, or quality upgrades). Further, to the extent that piracy is widespread across many countries (or regions), content developers’ incentives may be significantly impacted thereby reducing the availability of channels and programs for consumers. To sum up, turning a blind eye on digital piracy may have a sizeable positive impact on consumers at the risk of delaying an optimal rollout of the technology.

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References Alianza, 2015. “Piracy Report for Latin America,” Unpublished manuscript. Available at: www.alianza.tv Byrne, D., 2015. “Testing Differentiated Products Models: Consolidation in the Cable TV Industry,” International Economic Review, 56(3), 805-850, 2015.

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Cameron, C. and P. Trivedi, 2005. Microeconometrics: Methods and Applications. Cambridge University Press. Chaudhuri, S., P. Goldberg and P. Jia, 2006. “Estimating the Effects of Global Patent Protection in Pharmaceuticals: A Case Study of Quinolones in India,” American Economic Review, 96(5): 1477-1514.

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Chintagunta, P., A. Bonfrer and I. Song, 2002. “Investigating the Effects of Store-Brand Introduction on Retailer Demand and Pricing Behavior”, Management Science, 48 (10): 12421274 Chipty, 2001. “Vertical Integration, Market Foreclosure, and Consumer Welfare in the Cable Television Industry.” American Economic Review, 91(3): 428-53

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Crawford, G. and A. Yurukoglu, 2012. “The Welfare Effects of Bundling in Multichannel Television Markets,” American Economic Review, 102(2): 643-685.

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Davis, P. and E. Garcés, 2010. Quantitative Techniques for Competition and Antitrust Analysis. Princeton University Press. ERESTEL, 2013. Encuesta Residencial de Servicios de Telecomunicaciones. Data available at: http://www.osiptel.gob.pe

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Goolsbee, A. and A. Petrin, 2004. “The Consumer Gains from Direct Broadcast Satellites and The Competition with Cable TV.” Econometrica, 72(2): 351-381.

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Havoscope, 2017. Financial Losses due http://www.havocscope.com/tag/cable-piracy/

to

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Available

at:

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Hellerstein, R., 2008. “Who bears the cost of a change in the exchange rate? Pass-through accounting for the case of beer,” Journal of International Economics, 76(1): 14-32. Liebowitz, S. 2016. “How Much of the Decline in Sound Recording Sales is due to File-Sharing?” Journal of Cultural Economics, 40: 13-28. McFadden, D., 1974. “Conditional Logit Analysis of Qualitative Choice Behavior,” in Frontiers in Econometrics, edited by P. Zarembka, pp. 105-42. New York: Academic Press. Nevo, A., 2001. “Measuring Market Power in the Ready-to-Eat Cereal Industry,” Econometrica, 69(2): 307-342. 25

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Oberholzer-Gee, F. and K. Strumpf, 2007. “The Effect of File Sharing on Record Sales: An Empirical Analysis,” Journal of Political Economy, 115(2):1-41. Oberholzer-Gee, F. and K. Strumpf, 2010. “File-Sharing and Copyright,” Innovation Policy and the Economy, 10: 19-55.

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at:

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Train, K.E., McFadden, D.L. and M. Ben-Akiva, M., 1987. “The demand for local telephone service: a fully discrete model of residential calling patterns and service choices,” The RAND Journal of Economics, 18 (1): 109–123.

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Villas-Boas, S., 2006. “Vertical Relationships between Manufacturers and Retailers: Inference with Limited Data,” Review of Economic Studies, 74:625-652.

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Zentner, A., 2006. “Measuring the Effect of File Sharing on Music Purchases,” The Journal of Law and Economics, 49(1): 63-90.

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Appendix A.1 First-stage regression for Tables 3 and 4

PT

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Table 4 -4.718*** -0.423 6.459*** -13.29*** 2.430** 1.139 3.449*** -8.595*** -12.18*** 2.273 -4.732*** -3.997*** 5.878*** -0.957 3.590*** -0.615 5.967*** -13.46*** 3.500*** -4.095*** -2.448** -5.865*** -5.048 -5.669*** 3.726*** 43.56*** 0.000668** 0.000938* 0.00599*** -0.00137*** -5.96E-06 56.93*** 42.48*** 63.43*** 45.52*** 23.30*** -9.62E-05 0.000197*** 0.000523*** 0.00211*** -0.000575*** 0.000147* 1.659 0.43 0.00

AN US Telefónica América Móvil DirecTV Others Illegal

Telefónica América Móvil DirecTV Others Illegal Density Density x : Telefónica América Móvil DirecTV Others Illegal Constant 2 R F-Statistic*, p-value

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Table 3 -4.763*** 5.193*** 12.53*** -6.115*** 5.920*** 6.065*** 8.854*** -4.939*** -5.636*** 10.18*** 0.69 -2.986*** 10.08*** 5.215*** 8.286*** 5.806* 10.36*** -6.778*** 6.507*** 2.559** -1.62 0.92 -1.931 -0.723 4.984*** 47.03*** 0.000151 0.000225 0.00382*** -0.00194*** 0.000469* 54.92*** 41.98*** 63.64*** 46.35*** 31.12*** -0.000172** 0.000255*** 0.000495*** 0.00248*** -0.000605*** 0.000428*** -2.996*** 0.43 0.00

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ED

Variable 2.depa 3.depa 4.depa 5.depa 6.depa 7.depa 8.depa 9.depa 10.depa 11.depa 12.depa 13.depa 14.depa 15.depa 16.depa 17.depa 18.depa 19.depa 20.depa 21.depa 22.depa 23.depa 24.depa 25.depa Satellite Bundle HH Exp. X:

*F-Statistic of excluded instruments

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A.2 Demand Estimation that Ignores Illegality

Price DirecTV Telefónica América Móvil Bundle Satellite

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HH Expenditure x : Telefónica

Spec. 4 (R.C. Logit IV) Mean SD -0.0882*** 0.0127 (0.008) (0.0098) -2.142 3.219*** (1.733) (0.904) -2.032 -2.579*** (1.284) (0.792) -2.857 -2.818 (3.404) (1.992) 3.041*** -0.223 (0.307) (0.169) -0.010 (0.104) 0.0018*** (0.000) 0.0011*** (0.000) 0.0019*** (0.000) 0.0002 (0.000) 0.0797*** (0.007) -12,390.47 0.00 Yes Random Coefficients Logit No 123,877

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Variables

América Móvil

DirecTV

Others

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Control Function

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Log-Likelihood Chi2 (p-value) Pseudo-R Instrumental Variables? Model Survey Weights # Observations

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A.3 Results Including Urban Areas (Department Capitals Only) [Broader Illegal definition] Demand Estimates Variables Price

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DirecTV Telefónica América Móvil Illegal

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Bundle Satellite

HH Expenditure x : Telefónica

América Móvil

M

DirecTV

Others

ED

Illegal, Sharing Control Function

PT

Log-Likelihood Chi2 (p-value) Pseudo-R Instrumental Variables? Model Survey Weights # Observations

AC

CE

Spec. 4 (R.C. Logit IV) Mean SD -0.0772*** 0.0005 (0.007) (0.0008) -10.303 6.979*** (6.778) (2.910) -0.066 -1.066*** (0.493) (0.296) -0.459 0.0102 (0.281)* (0.172) -2.379** 2.082** (1.119) (1.019) 3.890*** -0.071 (0.381) (0.191) 0.089 (0.115) 0.0012*** (0.000) 0.0008*** (0.000) 0.0034*** (0.000) 0.0003 (0.000) 0.0008*** (0.000) 0.0790*** (0.006) -9,612.51 0.00 Yes Random Coefficients Logit No 76,388

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Elasticities Telefónica

Telefónica América Móvil DirecTV Others Illegal, Sharing No purchase (antenna) Aggregate Elasticity:

-3.546 0.826 0.329 0.865 0.569 0.578

América Móvil 0.141 -3.748 0.047 0.153 0.102 0.131 -1.024

DirecTV

Others

0.035 0.029 -1.780 0.028 0.026 0.021

0.243 0.291 0.078 -2.576 0.201 0.271

Illegal 0.212 0.216 0.084 0.227 -1.655 0.173

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Operator

Lerner Index and Marginal Cost Operator Telefónica América Móvil DirecTV

Implied Marginal Cost (soles/month) 59.114 41.637 34.778

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Lerner Index 0.282 0.267 0.562

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Telefónica’s Diversion Ratios: 16.64% with respect to América Móvil, 6.73% with respect to DirecTV, 18.59% with respect to the Others category and 10.16% with respect to the Illegal category.

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