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Record W1858155706

The Effects of Competition Policy on TFP Growth: Some Evidence from the Malaysian Electricity Supply Industry

2009· preprint· en· W1858155706 on OpenAlexaboutno aff
Timothy Coelli

Bibliographic record

VenueRePEc: Research Papers in Economics · 2009
Typepreprint
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsTotal factor productivityElectricityMains electricityProductivityCompetition (biology)Industrial organizationElectric power industryEconomicsIndex (typography)Government (linguistics)BusinessQuarter (Canadian coin)Monetary economicsPower (physics)MacroeconomicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

The main objectives of this paper are to measure total factor productivity (TFP) growth in the electricity supply industry in Peninsular Malaysia from 1975 to 2005 and to assess the impact of private entry reforms upon TFP in this industry. Prior to 1995, a government-linked, vertically-integrated electricity utility, Tenaga Nasional Berhad (TNB), was essentially the sole operator. However, since 1995 privately-owned Independent Power Producers (IPPs) have also begun generating electricity, all of which is purchased by TNB under fixed Power Purchase Agreements (PPAs). The introduction of IPPs has reduced the need for TNB to find finance for new power plants. It has been argued that the participation of IPPs in the electricity generation industry should also facilitate improvements in industry productivity; however this proposition is yet to be tested. In this study we calculate TFP growth using TA¶rnqvist index methods, finding that there is no direct evidence of productivity improvements attributable to the privatization. Furthermore, it is not clear that consumers have benefited from this, since the PPAs have generally been quite generous to the IPPs in terms of risk sharing and prices paid.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
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 science0.0010.000
Research integrity0.0010.003
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 head0.013
GPT teacher head0.267
Teacher spread0.254 · 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.

Study designSimulation or modeling
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
Published2009
Admission routes1
Has abstractyes

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