The impact of piracy on the structure of the Pay TV market: a case study for Latin America

ABSTRACT

The purpose of this paper is to show that piracy affects competition by reducing the ability of operators to enter and expand in the market. We expect this to be reflected in a more concentrated market. We empirically assess this by employing an unbalanced panel data set for 17 countries in Latin America (years 2013–2015). By using the Herfindahl Hirshman Index (HHI) as an indicator of market concentration, we find that halving piracy reduces market concentration by 6.5% in markets where the HHI index is above 1500. When controlling for endogeneity, halving piracy reduces market concentration by 11–15% for all levels of HHI. Since in most Pay TV markets in Latin America the HHI is above 1500, the results suggest that fighting piracy is a tool for promoting competition.

KEYWORDS:

• Piracy
• competition
• market concentration
• Pay TV
• HHI

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The expected expansion for the upcoming 5 years represents a slowdown in the growth rate of this market. The weakening of the purchase capabilities in Brazil, the largest Pay TV market in the region, and the high penetration rates in high-volume markets are some of the factors explaining the deceleration of the Pay TV market in the region.

2. Specifically, Hong (2004) finds that quarterly music expenditure of the average U.S. household has declined by 2 dollars and 46x cents as a result of using the Internet and plausibly Napster. Hong (2004) concludes that this accounts for 33% of the decrease in total recording sales in 2000, although he concedes that the changes in the household-level music expenditure he finds with the DDM method are due to not only Napster but also factors other than file-sharing of copyrighted music.

3. The incidence rate of this type of informality is 26% Bolivia, 23% Nicaragua, 22% Honduras,22% El Salvador,21% Dominican Republic, 18% Brazil, and 18% in Perú. Source: (Business Bureau, 2016).

4. Aprodica is the Spanish acronym of the Association of Programmers, Distributors and Agents of the Pay TV market in Central America.

5. According to Claro, DIRECTV and Telefónica.

6. Spanish acronym of the Confederation of North, Central American and Caribbean Association Football.

7. Spanish acronym of the South American Confederation Football.

8. The countries are: Argentina, Bolivia, Brazil, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Peru, Uruguay, Venezuela.

9. We want to thank Juan José Torres Figueroa for referring us to this method as well as for the estimation of the simulated HHI.

10. The average deviation between the simulated and the observed HHI is 0.18% for an assumed market size of 10 firms, 0.84% for a 15 firms-assumption and 1.10% for a 20 firms-assumption.

11. The value of a country’s private investment is converted to 2015 constant dollars in order to adjust for inflation.

12. The relationship between market concentration and investment is not linear; it is rather a down-ward “U”. See Aghion, Bloom, Blundell, Griffith, and Howitt (2005), and Friesenbichler (2007).

13. Notice that the investment in the telecommunications sector is the addition of the investment specific in the Pay TV market and that in other services. Given that the concentration in the Pay TV market has incidence in the level of investment in this sector, the overall investment in the telecommunications sector may still be a function of the concentration in the Pay TV market. This is the composition effect.

14. Investments generally translate into a greater variety of contents (e.g. live and on-demand video content), higher image quality (e.g. HD) and more flexible services (e.g. DVR and multiple screen offerings) which benefit consumers.

15. In particular, the random effects assumptions include the seven fixed effects premises specified in Wooldridge J. M. (2009a) plus a few additional requirements.Fixed effects model rely on the following assumptions: (i) the model can be described by an equation like y = β₁x_it1 + … + β_kx_itk + α_i +ε_it (t = 1,…,T), where the β_j are the parameters to estimate and α_i is the unobserved effect. (ii) we have a random sample from the cross section; (iii) each explanatory variable changes over and no perfect linear relationships exist among the explanatory variables; (iv) for each time period the expected value of the idiosyncratic error given the explanatory variables in all time periods and the unobserved effect is zero (strict exogeneity assumption); (v) variance of the error term (ε_it) given α_i and all explanatory variables is constant (homoskedasticity of the error term); and (vi) no serial correlation of the idiosyncratic errors (conditional on all explanatory variables and α_i).In turn, random effects, first, relaxes the third fixed effects assumption by allowing time-constant explanatory variables. At the same time, it requires two additional premises: (i) the expected value of α_i given all explanatory variables is constant; and (ii) the variance of a_i given all explanatory variables is constant (homoskedasticity on α_i). If these assumptions hold random effects is consistent and more efficient that fixed effects.

16. We perform the Hausman test to check whether the statistical evidence supports that the idiosyncratic heterogeneity is accurately captured under a random effects model rather than under a fixed effects model. For the specifications (1) to (2) we obtain the following associated p-value, respectively: 0.916 and 0.884. Therefore, we confirm the null hypothesis that the preferred model is random effects.

17. Similarly, in 23 out of 27 countries in Europe HHIs are above this threshold of 1800 (Fontaine & Deirdre, 2016).

18. In some countries there are regional Pay TV operators.

19. We note that it cannot be argued that more concentration in the market always lead to less competition. However, for the sake of the argument, we presume that if endogeneity were claimed, that could be one justification, that is, in this particular case, concentration leads to higher prices.

20. For more information on obtaining material refer to http://www.heritage.org/index/.

21. For the specifications (3) to (6) we obtain the following associated p-value, respectively: 0.964, 0.959, 0.920, and 0.560. Therefore, we confirm the null hypothesis that the preferred model is random effects.

22. Given that with instrumental variables random effects regressions STATA does not provide direct commands to perform this test, in order to run the Wu-Hausman test we perform this manually in STATA, as suggested by Wooldridge J. M. (2009b). Specifically, first, we regress the supposedly endogenous variable (piracy) on the exogenous variables and estimate the residual. Subsequently, we regress the dependent variable of the HHI on the model including the endogenous and exogenous variables including the residual previously estimated. The t statistic on the residual tests the null hypothesis that piracy is exogenous. With p-values of 0.183 and 0.429 (for the regressions which include population and GDP per capita as explanatory variables, respectively), we cannot reject the null hypothesis. The test is fully robust to serial correlation and heteroskedasticity.

Additional information

Notes on contributors

José Maria Rodriguez Ovejero

José Maria Rodriguez Ovejero is an Associate Director in Frontier Economics. As a consultant he has been involved in the main regulatory and competition policy issues in the telecommunications industry for over 20 years in Europe and Latin America. He is an economist and holds a Master of Industrial Economics from Universidad Carlos III, Madrid

Luigi Stammati

Luigi Stammati is a consultant at Oxera Consulting LLP. He has advised regulators and companies on competition and regulatory issues in Italy, Europe, Latin America, Africa, and the Middle East. Recent projects include the econometric forecasting of telecoms services demand and the modelling of the USO net cost. He holds a Master in Specialized Economic Analysis from the Barcelona Graduate School of Economics.

Maria Paula Torres Figueroa

Maria Paula Torres Figueroa is a consultant at Frontier Economics where she has advised operators in regulation and competition issues in Europe, Latin America and the Middle East. She holds a Master in Economics from Universidad de los Andes, Colombia, and a Master of Economics of Markets and Organizations from Toulouse School of Economics, France.