JOURNAL ARTICLE
Agency objectives, organizational change, and optimizing enforcement.
Published In: Journal of Antitrust Enforcement, 2023, v. 11, n. 2. P. 272 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Sokol, D Daniel; Wickelgren, Abraham L 3 of 3
Abstract
Keywords: Antitrust; FTC; Competition policy; K21; L40 EN Antitrust FTC Competition policy K21 L40 272 277 6 08/11/23 20230701 NES 230701 I. CONCEPTUALIZING AND OPTIMIZING CHANGE Institutional structure helps shape the nature of accountability.[1] Literature in economics,[2] political science,[3] finance,[4] and management[5] all utilize principal-agent models. The good news is that better alignment between agency leadership and staff (leadership needs to better listen to staff) and more respect for analytical tools can fix these problems. In the case of the Federal Trade Commission (FTC), however, if Congress does not trust the Commission to listen to staff (we discuss below why that is a reasonable belief), then Congress has even less reason to trust the Commission. While many of the current leadership's most fervent supports have criticized antitrust policy as insufficiently accountable to the democratic process,[28] the FTC has assumed authority to make competition rules without explicit Congressional authorization (and indeed based on a broad reading of a case from the 1970s that certainly would not survive judicial scrutiny today).[29] Odder still is the selective textualism of the FTC. [Extracted from the article]
Additional Information
- Source:Journal of Antitrust Enforcement. 2023/07, Vol. 11, Issue 2, p272
- Document Type:Article
- Subject Area:Law
- Publication Date:2023
- ISSN:2050-0688
- DOI:10.1093/jaenfo/jnad027
- Accession Number:169851077
- Copyright Statement:Copyright of Journal of Antitrust Enforcement is the property of Oxford University Press / USA and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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