Analyzing efficiency in the Chinese life insurance industry
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the efficiencies of China's foreign and domestic life insurance providers and to explore the relationship between ownership structure and the efficiencies of insurers while taking into consideration other firm attributes. Design/methodology/approach The data envelopment analysis (DEA) method is used to estimate the efficiencies of the insurers based on a panel data between 1999 and 2004. Findings The results indicate that the average efficiency scores for all the insurers are cyclical. Both technical and scale efficiency reached their peaks in 1999 and 2000 and gradually reduced for the rest of the period under examination until 2004 when average efficiency were improved again. The Tobit regression results show that the insurers' market power, the distribution channels used and the ownership structures may be attributed to the variation in the efficiencies. Research limitations/implications Based on the research findings and the discussion, the study provides several recommendations for policy makers, regulators and senior executives of insurers. Practical implications The research results highlight the importance of deregulating the sector to allow a further expansion of each individual insurer or encourage mergers and acquisitions of insurers so more efficient resource utilization can be achieved through economies of scale. It also suggests that it is imperative for insurers to recruit motivated insurance agents and offer them on‐the‐job training as a part of the management strategies for gaining technical efficiency. Originality/value The paper reports the development within China's insurance industry and is one of the few studies analyzing the efficiencies of China's insurers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".