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Spectrum Licensing, Policy Instruments and Market Entry

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Abstract

Competition policy attempts to address the potential for market failure by encouraging competition in service markets. Often, in wireless communication service markets, national regulatory authorities seek to encourage entry via the spectrum assignment process. Instruments used include the assignment mode (auction or beauty contest), setting aside licenses and providing bidding (price and quantity) credits for potential entrants, and making more licenses (spectrum blocks) available than there are incumbent firms (excess licenses). The empirical analysis assesses the effectiveness of these policy instruments on encouraging entry. The econometric results show that the probability of entry is enhanced by using auction assignments and excess licenses. Furthermore, quantity, but not price, concessions encourage entry.

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Notes

  1. The environment in which Western European NRAs assigned spectrum to MNOs during 1999–2001 was shaped by the European Commission (1997) Directive 97/13/EC, which stated that new operators should be encouraged to enter markets to ensure the development of European telecommunications service markets. In particular, the UMTS Forum argued that market entry was required to stimulate competition and that optimum subscriber benefit would only be achieved when competing infrastructures provide advanced and innovative services (UMTS Forum 1998).

  2. Potential entrants are firms that do not operate second-generation (2G) networks in the nation that assign spectrum. However, if NRAs want to influence aftermarket competition by setting aside licenses they need perfect information about potential licensee valuations. This is important as the inefficiency of allocating a license to a low-value (inefficient) firm may outweigh any positive effect on social welfare due to market entry (Hoppe et al. 2006).

  3. However, to assure entry, bidding credits must raise entrants’ willingness to pay above an incumbent’s pre-emptive willingness to pay (Hoppe et al. 2006; Gruber 2007; Azacis and Burguet 2008; Ansari and Munir 2008). Recently, Cramton et al. (2011) theoretically demonstrated that the impact of bidding credits on enhancing competition is ambiguous.

  4. Beauty contests require that MNOs submit plans or bids including spectrum-use plans. NRAs then hear proposals and award spectrum to operators. Importantly, spectrum price is only one aspect of NRA decisions. Conversely, auctions require operators to make price bids for spectrum lots. Thus, auctions are competitive, price-based mechanisms that should result in allocations to operators with the highest spectrum valuations (Cramton 2002).

  5. For the sampled assignments, operators can win only one license. Hence, the presence of excess licenses provides potential entrants with an enhanced opportunity of winning spectrums.

  6. Hoppe et al. (2006) studied the relationship between the number of 3G spectrum licenses that were offered and aftermarket competition (or market structure proxied by the number of active firms). They found that incumbents were more willing to deter entry the greater is the potential fall in profit.

  7. Positioning costs include infrastructure deployment; establishing administrative functions; and marketing and promotion. The extent that these expenditures are barriers to entry varies by entrant.

  8. Importantly, the approach is applicable to a wide range of selection problems where data availability is limited.

  9. Infrequently, regulators attempted to encourage entry by setting aside licenses for potential entrants. That is, incumbents cannot bid. Accordingly, the licenses are not included in the sample for estimation.

  10. The authors are grateful to the Editor for clarification on this point.

  11. Also, Greece was the only country not to have made available excess licenses.

  12. The anticipated sign of the coefficient is negative.

  13. Further, the model is generalized for the case when the selection variable is not observed. In this case the selection mechanism is specified as a probability model to account for the latent selection variable. Another generalization is to allow nonlinear specification of the primary equation, see Greene (1992, 2006) and Terza (1998) for example of the approach.

  14. An increase in the entrant-to-bidder ratio error should be positively associated with gaining entry in the spectrum contest. The residual is a proxy for unobserved private information. Thus, including the omitted self-selection residual controls for and tests for the significance of private information in explaining ex post outcomes. The authors thank the Editor for this insight.

  15. The proxy for the selection mechanism is: \(s_i^{*} =\mathbf{w}^{\prime }_i \varvec{\delta } +e_i, s_i =0 (s_i^{*} \le 0), s_i = s_i^{*} (s_i^{*} >0), e_i \sim N[0,1],\) where the probability that \(y_i\) is observed increases with the value of \(s_i^{*}\).

  16. Terza et al. (2008, p. 534) asserted that the 2SRI method was first proposed by Hausman (1978) in a linear model context. Consistent 2SRI methods for specific nonlinear models were developed by Blundell and Smith (1989), Blundell and Smith (1993), Newey (1987), Rivers and Vuong (1998), and Smith and Blundell (1986).

  17. The following discussion concerns the binomial probit specification. However, isomorphic econometric procedures apply to the Poisson specification.

  18. Cappellari and Jenkins (2003) argued that if the number of draws is greater than the square root of the sample size the parameter estimates will be robust to different initial seed values.

  19. The cluster estimator corrects estimated standard errors for panel data type effects that are present, but omitted from the model. A Lagrange multiplier test is initially used to detect heteroskedasticity (see Greene 2007). The null hypothesis of homoskedasticity is rejected for all models. Yatchew and Griliches (1984) found that maximum likelihood estimators for binary choice models were inconsistent and the covariance matrix inappropriate under conditions of heteroskedasticity. Greene (2008) noted that this test will likely detect other forms of misspecification when present, e.g., unmeasured heterogeneity, omitted variables, nonlinearity or an error in the distributional assumption (Greene (2008), p. 780). Given this, it is best to use robust corrections.

  20. Auctions can promote market entry by imposing allocation limits on individual firms or by specifying particular auction designs to achieve particular allocation outcomes (e.g., single or several licenses). Hoppe et al. (2006) argued that excessive supply capacity weakens pressure for competitive bidding, while reducing supply raises the prospect of new market entry (under specific cost conditions, tacit collusion is more difficult for incumbents). McAfee (1998) also indicated that excessive capacity supply undermined viable businesses.

    Table 9 Explanatory variable summary statistics, 1999–2008
  21. In auction assignments COVER, TIME, FEE, INITIAL, and RESERVE are usually specified by NRAs, and MNOs bid based on these predetermined conditions. However, beauty contests require multiple-dimension bids based on spectrum price and some (or all) of the spectrum assignment elements (viz., COVER, TIME, FEE, INITIAL, and RESERVE). Usually, regulators provide guidelines to potential bidders via supporting documents.

  22. As the control variable SETASIDE is determined by the regulator it is also potentially endogenous.

  23. The Appendix Table reports results from the restricted binomial probit model estimation.

References

  • Ansari, S., & Munir, K. (2008). How valuable is a piece of the spectrum? Determination of value in external resource acquisition. Industrial and Corporate Change, 17, 301–333.

    Article  Google Scholar 

  • Azacis, H., & Burguet, R. (2008). Incumbency and entry in license auctions: The Anglo-Dutch auction meets other simple alternatives. International Journal of Industrial Organization, 26, 730–745.

    Article  Google Scholar 

  • Blundell, R., & Smith, R. (1989). Estimation in a class of simultaneous equation limited dependent variable models. Review of Economics Studies, 56, 37–58.

    Article  Google Scholar 

  • Blundell, R., & Smith, R. (1993). Simultaneous microeconometric models with censored or qualitative dependent variables. In G. Maddala, G. Rao, & C. Vinod (Eds.), Handbook of statistics (Vol. 2, pp. 1117–1143). Amsterdam: North-Holland Publishers.

    Google Scholar 

  • Börgers, T., & Dustmann, C. (2003). European telecom licences. Economic Policy, 18, 215–268.

    Article  Google Scholar 

  • Cappellari, L., & Jenkins, S. (2003). Multivariate probit regression using simulated maximum likelihood. The Stata Journal, 3, 278–294.

    Google Scholar 

  • Cramton, P. (2002). Spectrum auctions. In M. Cave, S. Majumdar, & I. Vogelsang (Eds.), Handbook of telecommunications economics (Vol. 1, pp. 605–639). Amsterdam: North-Holland Publishers.

    Google Scholar 

  • Cramton, P., Kwerel, E., Rosston, G., & Skrzypacz, A. (2011). Using spectrum auctions to enhance competition in wireless services. Journal of Law and Economics, 54, S167–S188.

    Article  Google Scholar 

  • Das, Varma G., & Lopomo, G. (2010). Non-cooperative entry deterrence in license auctions: Dynamic versus sealed bid. Journal of Industrial Economics, 58, 450–476.

    Article  Google Scholar 

  • DotEcon. (2008). Spectrum awards database. London: DotEcon.

  • European Commission. (1997). Directive 97/13/EC. Retrieved September 16, 2013, from http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:31997L0013:EN:HTML

  • Global Competition Review. (2002-2008). Telecoms and media. London: Law Business Research Ltd.

  • Greene, W.: A statistical model for credit scoring. Working paper EC-92-29, Department of Economics, Stern School of Economics, New York University (1992)

  • Greene, W. (2006). Censored data and truncated distributions. In T. Mills & K. Patterson (Eds.), Palgrave handbook of econometrics, volume 1: Econometric theory. Palgrave: Hampshire.

    Google Scholar 

  • Greene, W. (2007). Limdep version 9.0. Software manual.

  • Greene, W. (2008). Econometric analysis (6th ed.). New Jersey: Prentice Hall.

    Google Scholar 

  • Gruber, H. (2007). 3G mobile telecommunications licenses in Europe: A critical review. Info, 9, 35–44.

    Article  Google Scholar 

  • Hausman, J. (1978). Specification tests in econometrics. Econometrica, 46, 1251–1271.

    Article  Google Scholar 

  • Hazlett, T., & Muñoz, R. (2009). A welfare analysis of spectrum allocation policies. RAND Journal of Economics, 40, 424–454.

    Article  Google Scholar 

  • Heckman, J. (1979). Sample selection bias as a specification error. Econometrica, 47, 153–196.

    Article  Google Scholar 

  • Heritage Foundation: Business Freedom. Retrieved June 13, 2013, from http://www.heritage.org/index/business-freedom (2012)

  • Hoppe, H., Jehiel, P., & Moldovanu, B. (2006). License auctions and market structure. Journal of Economics and Management Strategy, 15, 371–396.

    Article  Google Scholar 

  • Jehiel, P., & Moldovanu, B. (2003). An economic perspective on auctions. Economic Policy, 18, 269–308.

    Google Scholar 

  • Klemperer, P. (2002). How (not) to run auctions: The European 3G telecom auctions. European Economic Review, 46, 829–845.

    Article  Google Scholar 

  • McAfee, P. (1998). Four issues in auctions and market design. Revista de Análisis Económico, 13, 7–24.

    Google Scholar 

  • Newey, W. (1987). Efficient estimation of limited dependent variable models with endogenous explanatory variables. Journal of Econometrics, 36, 231–250.

    Article  Google Scholar 

  • Rivers, D., & Vuong, Q. (1998). Limited information estimation and exogeneity tests for simultaneous probit models. Journal of Econometrics, 39, 347–366.

    Article  Google Scholar 

  • Smith, R., & Blundell, R. (1986). An exogeneity test for a simultaneous equation Tobit model with an application to labor supply. Econometrica, 54, 679–685.

    Article  Google Scholar 

  • Terza, J. (1998). Estimating count data model with endogenous switching: Sample selection and endogenous treatment effects. Journal of Econometrics, 84, 129–154.

    Article  Google Scholar 

  • Terza, J., Basu, A., & Rathouz, P. (2008). Two-stage residual inclusion estimation: Addressing endogeneity in health economic modelling. Journal of Health Economics, 27, 531–543.

    Article  Google Scholar 

  • UMTS Forum: Considerations of licensing conditions for UMTS network operators. Report 4. Retrieved from http://www.umts-forum.org/content/view/1475/12 (1998)

  • Wright, A.: 3G auctions: Design options and global experience. Paper presented at the FICCI workshop on Spectrum Management.

  • Yatchew, A., & Griliches, Z. (1984). Specification error in probit models. The Review of Economics and Statistics, 66, 134–139.

    Google Scholar 

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Acknowledgments

The MIT Center for Digital Business and the Columbia Institute for Tele-Information provided support during the development of the paper. Helpful comments were provided by Andy Banerjee, Christian Dippon, Tom Hazlett, Mohsen Hamoudia, Rob Nicholls, Eli Noam, Robert Wright, and participants at the Australian Competition and Consumer Commission’s 10th Regulatory Conference. The authors are also grateful to two anonymous referees, the Associate Editor, and Editor of the Review of Industrial Organization for constructive and insightful comments. The authors are responsible for all remaining errors. Data provided by DotEcon Spectrum Awards database is gratefully acknowledged.

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Correspondence to Gary Madden.

Appendix

Appendix

The “Appendix” Table 16 reports the results from a restricted binomial probit model estimation: viz., with coefficients of selectivity and endogeneity arguments set to zero. The restricted equation results are clearly inferior to those reported in Table 14. In particular, while the estimated coefficient signs accord with expectations, the coefficients for excess licenses (EXCESS), quantity concessions (SCONC), population density (DENSITY), and national income (INCOME) are insignificant.

Table 16 Restricted ENTRANT binomial probit regression estimates

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Madden, G., Bohlin, E., Tran, T. et al. Spectrum Licensing, Policy Instruments and Market Entry. Rev Ind Organ 44, 277–298 (2014). https://doi.org/10.1007/s11151-013-9405-9

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