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Record W2539744645 · doi:10.1515/bejeap-2016-0106

What Do Regulators Value?

2016· article· en· W2539744645 on OpenAlexfundno aff
Dennis L. Weisman

Bibliographic record

VenueThe B E Journal of Economic Analysis & Policy · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
FundersMcMaster University
KeywordsEconomicsRegulatorMicroeconomicsEconomic surplusCompetition (biology)Merger controlProfit (economics)WelfarePublic economicsMarket economyCommissionFinance

Abstract

fetched live from OpenAlex

Abstract: In a pioneering article entitled “Taxation by Regulation,” Judge Richard Posner challenged the prevailing orthodoxy that regulation emulates competition along the lines of the Public Interest Theory of regulation. He argued that regulation is best viewed as a branch of public finance in which the power of the state is leveraged to achieve non-competitive outcomes. We develop an indirect test of Posner’s theory by specifying the regulator’s welfare function as a convex combination of consumer surplus, profits shared with the regulator and profits retained by the regulated firm. The welfare weights cannot be observed directly, but can be inferred from the regulator’s behavior in equilibrium. To wit, when the regulator permits the regulated firm to earn positive profits and authorizes higher prices in response to a greater degree of profit sharing this establishes both an upper bound on the consumer surplus weight and a higher weight on shared profits than on profits retained by the regulated firm. Applying this test to the implementation of the 1996 Telecommunications Act lends support to Posner’s theory.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.439
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.015
GPT teacher head0.245
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2016
Admission routes1
Has abstractyes

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