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Some Empirical Evidence on the Effectiveness of Antimerger Relief in the United States

Author

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  • Mikhail Kouliavtsev

    (Philadelphia University)

Abstract

The paper seeks to fill the void in the empirical economics literature of U.S. antimerger policy. A new empirical measure of relief secured in a Section 7 case is proposed. The extent of obtained relief is modeled as a fraction of the competitive overlap subject to structural divestiture. The model of determination of the relative size and scope of divestiture is applied to a sample of recent Section 7 cases. The estimated model is reasonably successful at predicting the outcomes of several out-of-sample cases.

Suggested Citation

  • Mikhail Kouliavtsev, 2004. "Some Empirical Evidence on the Effectiveness of Antimerger Relief in the United States," Industrial Organization 0404001, University Library of Munich, Germany.
    • Handle: RePEc:wpa:wuwpio:0404001
    Note: Type of Document - pdf; pages: 49
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    File URL: https://econwpa.ub.uni-muenchen.de/econ-wp/io/papers/0404/0404001.pdf
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    Cited by:

    1. Marcos Avalos & Rafael E. De Hoyos, 2008. "An Empirical Analysis of Mexican Merger Policy," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 32(2), pages 113-130, March.
    2. Mikhail Kouliavtsev, 2007. "Measuring the Extent of Structural Remedy in Section 7 Settlements: Was the US DOJ Successful in the 1990s?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 30(1), pages 1-27, February.

    More about this item

    Keywords

    Antimerger relief; U.S. Department of Justice; structural remedies; merger policy; Section 7;
    All these keywords.

    JEL classification:

    • L44 - Industrial Organization - - Antitrust Issues and Policies - - - Antitrust Policy and Public Enterprise, Nonprofit Institutions, and Professional Organizations
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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