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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".