Analysis of the efficiency of insurance companies in Indonesia
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.
Bibliographic record
Abstract
Financial system stability is not only supported by the banking sector, but also the role of insurance companies that operate efficiently. The study aims to analyze the efficiency performance of general insurance companies using two stages of data envelopment analysis during the 2017 – 2018 period. The first stage of efficiency measurement using a non-parametric data envelopment analysis (DEA) approach shows the efficiency level of general insurance companies experiencing a positive trend. The performance of general insurance companies in 2018 was more efficient than in 2017 based on the value of technical efficiency (CRS) and the value of pure technical efficiency (VRS). This means that in general there has been an increase in the efficiency of general insurance companies in Indonesia from 2017 to 2018. Testing the efficiency determinants in the second stage using the Tobit regression model found that the cost ratio is the only factor that significantly influences the efficiency level of general insurance companies in Indonesia. Meanwhile, company ownership and investment adequacy ratio have no significant effect on the efficiency level of general insurance companies in Indonesia. The results of the study provide recommendations to the management of general insurance companies that efficiency performance has not reached the maximum, and to improve it, it is necessary to control costs without disturbing routine operations and development activities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it