Influencing Factors of Corporate Performance of Life Insurance Companies – Evidence from China
Bibliographic record
Abstract
At present, the life insurance industry in China is still in the initial stage of development, which is characterized by limited scale, low penetration rate and low intensity. However, the large population base, the proliferation of middle classes, and the continuously improving socio-economic environment in China imply underlying developmental opportunities for the life insurance industry. Gaps in state pension have appeared owing to the issue of aging population, which signals that insurance companies with commercial properties may become an integral part of resident endowment. Ever since 2014, Chinese government has implemented numerous policies that are beneficial to the life insurance industry, for instance, diversifying investment channels of premiums, allowing a certain proportion of premiums in risky investments, and removing the restriction that the rate of return on common stakeholders’ equity (ROE) of participating insurance is capped at 5%. This paper constructs a panel data of 36 Chinese life insurance companies from 2010 to 2014. A serial of preliminary tests are taken in order to avoid spurious regression. By dint of the fixed effect model and panel threshold model, the paper analyzes the relation between operation-related factors and the corporate performance of life insurance companies. According to empirical findings, bancassurance income rate, professional insurance agency income rate, participating insurance income rate, group insurance income rate, company scale and solvency adequacy ratio are negatively correlated with corporate performance. When life insurance companies are associated with banks in capitals, bancassurance income rate positively influences corporate performance. The paper also investigates the impact of specific marketing channel structure and product structure on corporate performance. Policy implications are proposed accordingly.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".