Determinants of Financial Performance of Insurance Companies: Empirical Evidence Using Kenyan Data
Bibliographic record
Abstract
The drivers of financial success of the insurance industry are of interest to several players in any economy including the government; policymakers; policyholders; and investors. In Kenya; there have been relatively few studies on this topic; most of which look at narrow elements that determine insurance companies’ performance. This article sought to explore the components contributing to the financial performance of insurance firms. We employed a sample consisting of 37 general insurers and 16 life insurers for the period running from 2009 to 2018 and utilised panel data methods in order to establish the determinants of financial performance of Kenyan insurers. The pooled OLS; fixed effects and random effects models were estimated with the financial performance measures (proxied by either ROA or ROE) as the dependent variables. The results of the study documented that insurer financial performance and size were positively related. The study also found that insurer financial performance was negatively related to the age variable. The study also unraveled that higher leveraged insurance companies performed better than their lowly geared peers. This article provides broad analyses of the various drivers of financial performance of the insurance industry in Kenya. The findings of this study contribute to the academic literature on the financial performance of the insurance sector in Kenya and Africa as a whole. Furthermore; it gives pointers to the management of insurance companies on the aspects of their business that would need greater attention to drive and sustain superior financial performance.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".