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Record W2005186639 · doi:10.1080/01402382.2012.749651

The Janus Face of Europeanisation: Explaining Cross-Sectoral Differences in Public Utilities

2013· article· en· W2005186639 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueWest European Politics · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationLiberalizationPublic sectorRevenueCompetition (biology)DirectiveEuropean unionBusinessPoliticsDeregulationEconomicsInternational tradeEconomic policyEconomyMarket economyFinancePolitical science

Abstract

fetched live from OpenAlex

Abstract Although policymakers have sought to liberalise network-based utilities, a more detailed look at privatisation pathways reveals remarkable sector-specific differences. This article examines why efforts to privatise public utilities have differed so greatly in the telecommunications, postal, and railway sectors. By estimating probit models, it is demonstrated that firm characteristics and sector-specific EU integration account for cross-sectoral differences in privatisation. More specifically, governments dispose of the most efficient firms first to maximise revenues from privatisation sales with low political costs. Regulations at the European level pushed governments to privatise their national postal providers, while privatisation in the telecommunications sector is a global trend. In the railway sector, exceptional clauses and regulations have decelerated privatisation. Notes 1. Australia, Austria, Belgium, Canada, Denmark, Finland, France, Greece, Germany, Ireland, Italy, Japan, the Netherlands, Norway, New Zealand, Portugal, Spain, Sweden, Switzerland, the United Kingdom, United States. 2. The results for the substantive variables do not differ from those reported. 3. Some countries, such as the UK and New Zealand, have at least partly revoked their privatisation decision. 4. Dummies account for the most important sector-specific EU legislation. The dummy equals 1 in the year of adoption of a certain legislation and in the years after the adoption and when a company is operating in an EU member country. The following legislation has been included: the Green Paper in 1987 (COM/87/290) that promoted the liberalisation of the telecommunication market and directive 96/19/EC concerning the implementation of full competition of telecommunications and networks by 1998. 5. One reason for the early privatisations in non-European countries might be that the PTT system has no tradition in non-European countries and governments did not have to disentangle the administrative organised postal and telecommunications services first when privatising telecommunications services. 6. It might be countered that technological progress in the telecommunications sector is responsible for the differences across sectors. Apart from the problem of measuring sector-specific technological advance, I argue that technological progress is a preceding variable, influencing the firm's efficiency rather than directly altering the likelihood of privatisation. Nevertheless, I estimated the models including dummies for the most important innovations which, however, do not influence the probability of the company's divestment.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.928

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.054
GPT teacher head0.225
Teacher spread0.171 · 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