Concentration and Competition in the Pharmaceutical Sector in an Era of Challenges
Bibliographic record
Abstract
In 2018, the Central Bureau of Statistics noted that the pharmaceutical industry grew 7.36 percent and slowed by 5.59 percent during the Covid-19 pandemic in 2020. Fluctuations in the growth of the pharmaceutical sector before and during the Covid-19 pandemic encouraged increased competition and concentration. This study examines the concentration and competition of pharmaceutical businesses listed on the Indonesia Stock Exchange from the first quarter of 2018 to the third quarter of 2020. The method used in this study is a quantitative approach with a concentration ratio model (CR) and the Hirschman-Herfindahl index (HHI). The calculation results show that the Kalbe Farma company controls over 65 percent of the market share, while 9 pharmaceutical companies contest the other 35 percent. KLBF is a company with the most sustainable competitive advantage compared to others; this can be seen from product differentiation, use of technology, and a superior market share of 65.39%. In addition, from the aspect of market competition, it shows that the pharmaceutical industry before and during the Covid-19 pandemic was in a tight oligopoly market with scores of 99.20 and 99.22. The results show the implications that pharmaceutical sector actors can carry out our policies related to competitive price competition. Another procedure is that companies must constantly observe and analyze the actions of other pharmaceutical companies in making business decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".