This study examines the impact of some selected macroeconomic variables on the performance of the non-life insurance companies of Bangladesh. Here, we consider 32 such companies that are operating in the country. These companies are observed over the period of 7 years (2009–2015) giving rise to 224 panel observations. In our study, we use two performance measures, like return on asset (ROA) and return on equity (ROE) as dependent variables. The explanatory variables are categorized as macroeconomic factors and firm-specific factors. Former includes variable such as inflation rate, GDP growth rate, interest rate, and exchange rate. To measure the firm-specific factors, we use eight proxy variables such as age, size, loss ratio, solvency margin, assets tangibility, liquidity ratio, debt ratio, and management competence index as explanatory variables. The research employs panel data regression methodology to examine the effects of macroeconomic variables on the performance of the aforementioned companies. The regression results of our study suggest that except interest rate, none of the macroeconomic variables has statistically significant influence on the performance of non-life insurance companies. These results, indeed, gainsay with economic theories. On the other hand, the firms’ specific factors; e.g., age, sizes, loss ratio, solvency margin, tangibility of assets, and management competence index have statistically significant impact on the performance of the non-life insurance sector of Bangladesh. Thus, the interest rate along with firm-specific factors can be identified as determinants of the performance of the Bangladeshi non-life insurance companies. This analysis obviously provides some noteworthy new information to different stakeholders of the Bangladesh non-life insurance sector. In particular, the findings of the study are expected to be useful to both domestic and foreign investors to make more rational decisions regarding selection of insurance companies’ stocks for their portfolios at Dhaka stock exchange. Public policy authorities may also use the same results to formulate sound policies to ensure economic growth and stability of the nation.
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The panel data has to be tested for problems of multicollinearity, heteroskedasticity, and autocorrelation. It is also important to ensure that the data for all variables are stationary (Karavias and Tzavalis 2014) to avoid spurious results. The paper employs Levin et al. (2002) test for panel unit roots.
The data are also checked for multicollinearity using Variance Inflation Factor (VIF) as in the presence of this problem model estimate can be unstable and misleading. The values of VIF reveal that there is no problem for multicollinearity.
White’s test is popularly used for testing heteroskedasticity with model. Accordingly, this study uses this test. The Chi-square values for both models are high (133.485 for Model-1 and 129.5900 for Model-2) with very low probability suggesting that the null hypothesis of no heterogeneity is rejected for the both models.
Field (2009) suggests that the test values under 1 or more than 3 are a definite cause for concern; i.e. the presence of autocorrelation. From the Tables 4 and 5, it is observed that DW test values for model-1 is 1.3207 and for Model-2 is 1.6248, which are remained within the given range (rule of thumb). Therefore, the models have no autocorrelation problem.
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Hasan, M.B., Islam, S.N. & Wahid, A.N.M. The effect of macroeconomic variables on the performance of non-life insurance companies in Bangladesh. Ind. Econ. Rev. 53, 369–383 (2018). https://doi.org/10.1007/s41775-019-00037-6
- Non-life insurance companies
- Macroeconomic variables
- Firm performance
- Panel data
- Fixed effect
- Random effect