Relationship Between Macroeconomic Variables and Financial Performance of the Insurance Industry in Kenya
Bibliographic record
Abstract
The aim of this research project was to establish the relationship between macroeconomic \nvariables and the financial performance of the insurance industry in Kenya. Return on \ncapital employed was used as the financial performance indicator. The financial \nperformance was regressed against the macroeconomic indicators; average interest rates \nas computed by Central Bank rate, GDP growth rate, real exchange rate (Ksh/USD), \ninflation rate as computed by CPI and unemployment rate. Both the dependent and \npredictor variables were measured quarter yearly. A descriptive research design was \nemployed in the research study. The study population comprised 49 insurance firms that \nare registered in Kenya by the year 2015. The study utilized secondary data that was \ncollected quarter yearly. The data was collected from various sources; the World Bank, \nCentral Bank of Kenya, Kenya National Bureau of Statistics and the industry financial \nstatements as reported by IRA. The study was carried out in a ten year period from 2006 \nto 2015. The data was analyzed using multiple regression analysis, correlation analysis \nand descriptive analysis, using STATA software. The analyzed data was presented using \ntables and line graphs. The study found that GDP growth rate had a probability of \n(0.006<0.05) which is statistically significant while interest rates (0.4483>0.05), \nexchange rate (0.276>0.05), and un-employment rate (0.117>0.05) are statistically \ninsignificant. Therefore interest rates, exchange rates and unemployment rates are not \nsuitable predictors of the insurance industry’s financial performance. It is crucial that \nother factors both micro-economic and industry specific are considered while undertaking \nanother study in order to determine the drivers of performance of the insurance industry. \nThe research study recommends that the CBK should keep inflation, exchange rates and \ninterest rates in check. These variables have profound effect on the performance of the \ninsurance industry. For instance high exchange rates, translates to devaluation of the local \ncurrency and will cause a decrease in the performance of the industry. The study also \nrecommends that the government initiate policies and measures that increase the GDP \nwhich will lead to a positive effect on the industry and the economy as a whole.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".