The Effects of Competition Policy on TFP Growth: Some Evidence from the Malaysian Electricity Supply Industry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".