The Influence of Firm-Specific, Industry-Specific, Macroeconomic Factors, and Risk-Based Capital (RBC) on the Profitability of Life Insurance Companies in Indonesia
Bibliographic record
Abstract
This study aims to determine the influence of Firm-specific, Industry-specific, Macroeconomic factors, and Risk Based Capital (RBC) on the profitability of life insurance companies in Indonesia. This study uses secondary data, namely financial statement data of life insurance companies in Indonesia, macroeconomic data, and other information available on company websites, AAJI, OJK, and other sources. The data period used covers 2019 to 2023. The analysis method applied is regression analysis. Findings show that previous research conducted by Killins (2020) in Canada found that firm-specific factors such as liquidity and economic growth influence company profitability, while company size has a negative relationship with profitability. Industry-specific factors did not yield significant results related to profitability. Macroeconomic factors such as GDP growth and equity return show a significant influence on company profitability. In a study conducted by de Haan and Kakes (2010) in the Netherlands, it was shown that insurance companies with high profitability tend to have better solvency levels. The findings of this study can provide insights for life insurance companies in Indonesia regarding the factors that influence their profitability. This study contributes to understanding how Firm-specific, Industry-specific, Macroeconomic factors, and Risk Based Capital (RBC) affect the profitability of life insurance companies in Indonesia.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".