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Price competition in the market for business telecommunications services

Abstract

We estimate a two-step control-function model that relates incumbent prices for small-business telecommunications services to the number of facilities-based entrants, cost, demand, regulatory conditions, and a correction for endogenous market structure. Results show that the price effects from entry are understated in ordinary least squares regressions. When controlling for endogeneity, prices are negatively related to the number of entrants, indicating that markets without a competitive presence could exhibit market power. These findings should prove helpful to the Federal Communications Commission and other State regulators determining the conditions under which price and other forms of regulation may be relaxed.

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Notes

  1. 1.

    When the price effects from competition are linear, two-stage least squares and the control-function estimator will produce the same estimates. When the effects from competition are non-linear, the two methods will produce different estimates. Imbens and Wooldridge (2007) suggest that the control-function estimator, while less robust, might be more precise because it maintains the non-linear endogenous variable in the second step. We follow Imbens and Wooldridge by using the control-function estimator.

  2. 2.

    For ease of exposition when describing the econometric method we have assumed that all of the control variables in Xm are exogenous. The vector Xm, however, contains two variables, UNE Price and Section 271, that measure the impact of different aspects of the Telecom Act on market prices, and are likely endogenous. When estimating the model in Sect. 4 we omit these two variables from the vector of exogenous variables in Zm and construct two additional control functions to account for their potential endogeneity. See Sect. 3.2 for more discussion.

  3. 3.

    For robustness, we also estimated an alternative model specification under the assumption that the error term in Eq. 2 follows a logistic distribution. See Sect. 4.3 for the discussion of the results from this robustness test.

  4. 4.

    We considered using Cameron et al. (2008) wild cluster bootstrap-t method to obtain t-statistics. This produces empirical rejection rates that are close to theoretical values and is attractive when the empirical model includes a number of state-level indicator variables. However, this method applies cluster-level Rademacher weights to estimated residuals and cannot be readily applied to our analysis because the first-step latent variable model does not have traditional linear residuals.

  5. 5.

    We also investigated the use of bootstrap-t procedures where inference is based on the distribution of Wald statistics (Poi 2004). Unfortunately, the nested bootstrapping procedure is problematic when binary regressors are invariant within clusters. Initial draws that include a relatively large percentage of clusters with values of zero or one for the entire cluster inevitably lead to resamples where binary regressors take on values of zero or one for the entire resample.

  6. 6.

    The Census defines “places” as incorporated places, such as cities, towns or villages, and “census designated places,” which resemble cities, towns or villages, but lack their own governments.

  7. 7.

    This requirement results in the dropping of 443 markets.

  8. 8.

    One-hundred-and-ninety-five markets had more than 12 entrants. Greenstein and Mazzeo (2006) drop cities where 10 or more competitors had either entered the market or had stated an intention to enter the market. They also drop cities where firms had entered the market before the Telecom Act’s passage. Our data do not allow us to identify cities where entry is planned.

  9. 9.

    Telephone companies may serve more than one locality with a switch, so an entrant may provide service to a locality with a switch located in a different city.

  10. 10.

    See Rosston, et al. (2008) for a complete discussion of the price data. For robustness, we re-estimated the price equations using prices from the most densely populated areas in a market. Model results, not presented here, are similar to those reported in Table 4.

  11. 11.

    Section 271 of the Telecom Act ensures that interconnection agreements between incumbents and new entrants satisfy a checklist that lower barriers to entry. Incumbent RBOCs can provide in-region long-distance telephone service in the applicant state following Section 271 approval by the state and Federal regulators.

  12. 12.

    We estimated an alternative specification with three individual indicator variables for each level of regulatory climate. However, the inclusion of these indicator variables increased the incidence of perfect collinearity when bootstrapping standard errors clustered at the state level.

  13. 13.

    For the test, we use estimates of the corrected price equation to calculate the residuals, \( {\hat{\varepsilon }}_{\text{m}} \), and regress the exogenous variables on the residuals. An F statistic is calculated to test the null hypothesis that the coefficients on excluded variables jointly equal zero.

  14. 14.

    Because of mixed evidence on the endogeneity of UNE prices, for robustness, we estimated an alternative model specification where we treated UNE Price as an exogenous variable. First-step results are presented in the final two columns of Table 3 and second-step results are presented in the third column of Table 4. Results from this specification produce slightly larger estimates of the effects entry has on business prices.

  15. 15.

    Wallsten and Mallahan (2013) find a similar qualitative pattern for DSL service of 0.768 Mbps. Monthly prices are $3.53 less with two ISPs than without, and about $5.72 less with three ISPs than with one.

  16. 16.

    The full set of results from both of these robustness tests are available on request from the authors.

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Correspondence to Gregory L. Rosston.

Additional information

We thank seminar participants at the WEAI Annual Conferences 2014 and 2016, the editor of this journal, and an anonymous referee for helpful comments. Xuyi Guo and Alex Stum provided excellent research assistance. The usual disclaimer applies.

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Rosston, G.L., Savage, S.J. & Wimmer, B.S. Price competition in the market for business telecommunications services. J Regul Econ 54, 81–104 (2018). https://doi.org/10.1007/s11149-018-9359-9

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Keywords

  • Market power
  • Market structure
  • Prices
  • Telecommunications

JEL Classification

  • L1
  • L13
  • L96