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Record W4394858932 · doi:10.1016/j.econmod.2024.106734

Impact of access regulation on investment reconsidered

2024· article· en· W4394858932 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEconomic Modelling · 2024
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsCompetitor analysisIncentiveEconomicsInvestment (military)MicroeconomicsMarginal costValuation (finance)Industrial organizationMarket powerBusinessFinanceMonopoly

Abstract

fetched live from OpenAlex

In regulated industries like electricity, gas, and telecommunications, regulators often require vertically integrated incumbents to share their infrastructure with competitors in a related market. This paper demonstrates that such access regulation may strengthen an incumbent's incentive to invest in infrastructure even if the regulated access price of an input is set at its marginal cost. Specifically, we reconsider Kotakorpi's (2006) model under an alternative circumstance where downstream rivals would be foreclosed from the market without regulation. We find that the access regulation increases investment and improves social welfare under certain conditions. Our main conclusion is robust to an alternative way of modeling consumers' valuation of products. Furthermore, raising the access price above the marginal cost expands the parameters for which the regulation increases investment. Our analysis suggests that access regulation alone does not reduce an incumbent's investment incentive .

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.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

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

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 head0.065
GPT teacher head0.295
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