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The Regulation of British Telecom: An Event Study

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  • Antony W. Dnes
  • Jonathan S. Seaton

Abstract

In this paper, we use event-study methods to analyze the post-privatization regulatory regime applying to telecommunications in the UK. Our results suggest that telecoms regulation has not been captured in an overall sense, although there is an heterogeneous pattern of regulatory events. The results are consistent with recent observations of Spiller and Vogelsang, who argue that the regulatory framework for UK telecoms is designed to maintain substantial checks on the regulator's discretion, operating mainly through the rule of law. Our broad conclusion is that the impacts of regulation indicate a relatively soft attitude to British Telecom up to 1995, when a tightening of regulation tended to offset the previous bias.

Suggested Citation

  • Antony W. Dnes & Jonathan S. Seaton, 1999. "The Regulation of British Telecom: An Event Study," Journal of Institutional and Theoretical Economics (JITE), Mohr Siebeck, Tübingen, vol. 155(4), pages 610-610, December.
    • Handle: RePEc:mhr:jinste:urn:sici:0932-4569(199912)155:4_610:trobta_2.0.tx_2-u
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    Citations

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    Cited by:

    1. Russell Smyth & Magnus Söderberg, 2010. "Public interest versus regulatory capture in the Swedish electricity market," Journal of Regulatory Economics, Springer, vol. 38(3), pages 292-312, December.
    2. Xinwen Ni & Wolfgang Karl Hardle & Taojun Xie, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," Papers 2009.12121, arXiv.org, revised Aug 2021.
    3. Bel, Germà & Trillas, Francesc, 2005. "Privatization, corporate control and regulatory reform: the case of Telefonica," Telecommunications Policy, Elsevier, vol. 29(1), pages 25-51, February.
    4. Aristeidis G. Samitas & Dimitris F. Kenourgios, 2006. "Financing Tourist Development through Stock Capital: Evidence from the Greek Hotel Sector," Tourism Economics, , vol. 12(1), pages 87-100, March.
    5. Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers 2020-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
    6. T. A. Robinson, 2000. "Modelling the effects of regulatory discretion: Carsberg vs Spottiswoode," Applied Financial Economics, Taylor & Francis Journals, vol. 10(2), pages 117-121.

    More about this item

    JEL classification:

    • K23 - Law and Economics - - Regulation and Business Law - - - Regulated Industries and Administrative Law
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

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