“Contribution of the non-life insurance sector to the economic growth of Nepal: Analysis from the EGLS approach”

Nepal’s non-life insurance sector holds immense potential to drive economic growth and boost the nation towards a secure financial future. Embracing this potential is a goal and a pivotal catalyst for substantial change. The study aims to determine how the non-life insurance sector can drive economic growth in Nepal. The methodology of this study uses quantitative analysis of financial data from 2013 to 2022 from 20 non-life insurance companies in Nepal and econometric modeling to assess the sector’s impact on economic growth. Using Panel EGLS (Estimated Generalized Least Squares) regression analysis, the findings show that with one-unit increments in total investment, total premium, and total tax paid, GDP is expected to change by approximately 591.52, –920.54 and 8,470.65 units, respectively. In contrast, the coefficient for total profit is –910.3477 and is not statistically significant. The study’s main conclusion implies that the insurance sector contributes to the country’s economic growth by investing in productive activities and paying taxes to the government. Still, it also imposes a cost on the economy by charging high premiums to the insured. The profitability of the insurance sector does not affect the GDP, which indicates that the insurance sector is competitive and efficient or that other factors determine the GDP besides the insurance sector. This study contributes to a deeper understanding of the non-life insurance sector’s role in Nepal’s economic development and informs evidence-based policy decisions.


INTRODUCTION
Non-life insurance companies play a vital role by collecting premiums from policyholders, creating a steady source of funds for investments and profit.They act as financial intermediaries, directing these resources into various sectors of the economy, fostering growth and overall economic development.Additionally, the taxes generated by this sector contribute to government revenue, supporting public infrastructure, education, and healthcare, thereby advancing economic progress.
The non-life insurance sector's growth creates various job opportunities in sales, underwriting, claims processing, and administration.This positively affects household income, spending habits, and the overall economy.Adapting to market changes, this sector has the potential to boost economic growth further.It fosters consumer confidence and financial stability, promoting increased economic participation and benefiting various sectors, ultimately enhancing the overall economic environment.

LITERATURE REVIEW AND HYPOTHESES
In the midst of these insights, Arena (2008)  This fascinating look at the insurance sector's regulatory landscape reveals its complex issues.In the field of management, Hamal (2020) emphasizes risk management in insurance.This clarion cry helps the industry expand sustainably and achieve its objectives.In this orchestration, insurance firms excel at risk management to create a symphony of value for their stakeholders.In this harmonious tale, policyholder financial security and sustainable prosperity combine perfectly.
Insurance penetration and economic development are resonant chords in economic dynamics (Khadka, 2020).This harmonic finding shows that nations with increasing insurance penetration have a powerful economic boom.Regarding developing countries, Barsby (1968) argued that countries such as Nepal can benefit from the experience of early-developing countries by adopting more advanced financial institutions, such as the insurance sector.This is because the insurance sector can accelerate the development of other financial markets and institutions, which can help promote economic growth.Shi et al. (2022) argued that the insurance sector can help improve the productivity of firms and industries in an economy.This is because insurance can help reduce the uncertainty faced by firms, which can lead them to invest more and innovate more, which can in turn lead to higher productivity and economic growth.
Furthermore, numerous empirical studies have demonstrated a strong correlation between the non-life insurance sector and economic growth, complementing the existing theoretical frameworks.As an illustration, Upadhyaya et al. ( 2023) discovered a correlation between a 1% rise in nonlife insurance penetration (the ratio of non-life insurance premiums to GDP) and a 0.08% increase in GDP growth.
The research gap identified in the text highlights the importance of further research on the impact of insurance on financial stability.This study can assist in mitigating risk and fostering risk awareness, thereby contributing to financial stability.
Further research is necessary to fully comprehend the scope of this impact.This research highlighted in the text emphasizes the importance of further investigation into the influence of insurance on economic growth in developing countries.The existing studies have shown that insurance can play a significant role in driving economic growth in developed countries.However, further studies are required to fully comprehend its impact on economic growth in developing nations.
Here are some specific hypotheses that can be tested: H1: Increased insurance penetration has no significant impact on higher economic growth rates in developing countries.Specifically, as the percentage of the population covered by insurance policies rises, there has not found the equal effect on GDP growth.
H2: In developing countries, the enhancement of financial stability and economic growth via insurance does not depend on particular mechanisms like risk pooling.This process does not diffuse financial risks among a wider populace, thus failing to foster investment and entrepreneurial activities, consequently hindering economic expansion.
H3: The challenges facing the development of the insurance sector in developing countries, including regulatory barriers, lack of infrastructure, and low levels of financial literacy, negatively impact economic growth.Specifically, overcoming these challenges through targeted interventions will not lead to improved economic performance and stability in these nations.

RESEARCH METHODOLOGY
The methodology covers the different steps outlined: a) Data Collection: Secondary data have been collected from the Office of the Insurance Authority Nepal for analysis, and it has been sourced from reports, financial statements, official records, and academic literature.Prior to their merger, there were a total of 20 nonlife insurance companies operating in Nepal as presented in the Table 1.

Table 1. Non-life insurance companies in Nepal
Source: Nepal Insurance Authority, 2023.

Date of Establishment
where GDP is the gross domestic product, TOMP is the total premium paid, TOINV is the total investment, TOPF is the total profit, TOTXP is the total tax paid, α is the intercept term, β1, β2, β3, and β4 are the slope coefficients, and ε is the error term.The study tests the hypothesis that total investment has a positive and statistically significant effect on GDP.To do this, we would estimate the econometric model and then test the null hypothesis that β2 = 0.If the p-value of the test is less than the significance level, then we would reject the null hypothesis and conclude that total investment has a positive and statistically significant effect on GDP.

d) Policy and Regulatory Assessment:
A thorough examination of the current policies and regulations impacting the non-life insurance sector has been carried out.This assessment has provided valuable insights into the obstacles and potential areas for development, allowing for the creation of impactful policy suggestions.
e) Expert Consultations: Consultations with insurance industry experts, economists, policymakers, and academics have provided a wellrounded perspective on the sector's role in the economy.Feedback from these consultations has been integrated into the analysis and recommendations.
f) Scenario Analysis: Different scenarios, such as variations in premium income, investment patterns, and GDP contribution strategies, have been modeled to assess their potential impact on economic growth.Sensitivity analysis has been employed to understand the magnitude of change under different assumptions.
g) Policy Recommendations: Drawing from the quantitative findings, along with international best practices, a set of actionable policy recommendations has been formulated.These recommendations have been designed to harness the sector's potential for driving economic growth, enhancing financial stability, and promoting risk management.

h) Validation and Review:
The methodology and findings have undergone validation and review by experts in the field to ensure rigor and accuracy in the analysis.
By employing this multifaceted methodology, the study aims to provide a comprehensive understanding of the non-life insurance sector's contribution to Nepal's economy and its potential to drive economic growth.

RESULTS AND DISCUSSION
The results of this analysis reveal several key findings regarding the relationship between non-life insurance and economic growth in Nepal.

Table 2. Lagrange multiplier tests for random effects
Source: Results from data analysis.

Random Effect Cross-section Time Both
Breusch-Pagan 28.441 179.444 207.886 (0.000) (0.000) (0.000) The null hypothesis of these tests assumes the absence of any random effects, while the alternative hypothesis suggests the presence of random effects.According to Table 2, the p-values for all three tests are 0.000.This indicates that the null hypothesis can be confidently rejected at a significance level of 0.01%.This suggests that there are random effects in the model and that the fixed effects model is inappropriate.The results of the Lagrange multiplier tests indicate the presence of random effects in the model.Based on the analysis, the fixed effects model is not suitable for the situation at hand, suggesting that a random effects model would be more appropriate.Table 4 shows the results of a panel EGLS crosssection random effects regression analysis.The dependent variable is GDP, and the independent variables are total investment, total premium, total profit, and total tax paid.The coefficient of total investment is positive and statistically significant, indicating that an increase in total investment is associated with an increase in GDP.
The coefficient is 591.519,indicating that for a one-unit increase in TOINV, GDP is expected to increase by approximately 591.52 units.The coefficient of total premium is negative and statistically significant, indicating that an increase in total premium is associated with a decrease in GDP.
The coefficient is -920.542,suggesting that for a one-unit increase in TOPM, GDP is expected to decrease by approximately 920.54 units.
The coefficient of total tax paid is positive and statistically significant, indicating that an increase in total tax paid is associated with an increase in GDP.The coefficient is 8,470.650,indicating that for a one-unit increase in TOTXP, GDP is expected to increase by approximately 8,470.65 units.
The coefficient of TOPF is -910.348,however, it does not reach statistical significance at the conventional level (p-value is 0.114).Thus, it may not have a substantial effect on GDP.
The R-squared is a measurement of how much of the variance in the dependent variable the independent variables are responsible for explaining.The higher the R-squared, the better the model explains the variation in the dependent variable.In this case, the R-squared is 0.539, which suggests that the model explains a significant amount of the variation in GDP.
Overall, the panel EGLS cross-section random effects regression analysis indicates that the model accurately represents the data and that the independent variables are statistically significant predictors of GDP.Regression analysis reveals the correlations between GDP and the independent variables, the existence of random effects, and the goodness-of-fit statistics for the model.All three hypotheses align with the findings of the regression analysis.
The findings indicate that insurance, as reflected by the total premium, plays a crucial role in driving economic growth in developing nations.The analysis does not provide a detailed explanation of how this impact occurs or the difficulties faced by the insurance sector.The R-squared value of 0.538809 suggests that the model effectively ex- plains a substantial portion of the variance in GDP, further supporting the overall accuracy of the model's fit to the data.
The analysis's consistently low p-values of 0.000 in all three tests support random effects in the model.Based on the statistical analysis conducted at a 0.01% significance level, it can be concluded that the null hypothesis of no random effects is rejected.This suggests that a fixed effects model is not suitable for the given data.The importance of a random effects model is reinforced by the Lagrange multiplier tests for random effects.The Hausman test provides additional evidence supporting the superiority of the random effects model over the fixed effects model in terms of efficiency.The null hypothesis of equal efficiency is rejected based on the chi-squared statistic of 97.904 and a p-value of 0.000.
The hypothesis testing provides important information.The first hypothesis suggests a distinct correlation between total premium and GDP, with a negative trend indicating that as premium increases, GDP decreases.As per the second hypothesis, the results do not clearly explain how insurance contributes to financial stability and growth.The third hypothesis discusses the impact of various factors on GDP without addressing insurance related obstacles.
The panel EGLS cross-section random effects regression analysis reveals crucial GDP with independent variables relationships.The coefficient for total investment is positive and statistically significant, suggesting that it has a positive impact on GDP.A negative and statistically significant coefficient indicates that an increase in total premium has a negative impact on GDP.The coefficient of total tax paid also demonstrates a positive and statistically significant impact on GDP, indicating an increase in economic output.The coefficient for total profit does not show statistical significance at the usual significance level, suggesting that it may not have a significant impact on GDP.
The model's R-squared value of 0.54 indicates a significant level of explanatory power in relation to GDP fluctuations, confirming its strong data fit.
Based on the panel EGLS cross-section random effects regression analysis, it can be concluded that the model effectively fits the data and the inde-pendent variables have a statistically significant impact on GDP.Table 3 presents a comprehensive overview of the correlations, random effects, and goodness-of-fit statistics, providing a thorough evaluation of the model's performance.
The regression analysis reveals several key findings.Firstly, it indicates that total investment exerts a positive and statistically significant influence on GDP, aligning with Hypothesis 1.This hypothesis posits that insurance plays a pivotal role in fostering economic growth by mitigating risk and uncertainty, thereby stimulating investment and innovation.
Conversely, the analysis shows that total premiums have a negative and statistically significant impact on GDP.This outcome is likely due to insurance premiums representing a cost for businesses and households.However, it is crucial to acknowledge that insurance also delivers substantial benefits, including financial protection against unforeseen financial hardships.Consequently, the overall effect of insurance on economic growth is anticipated to be positive, even though premiums are a financial outlay.This conclusion aligns with Hypothesis 2, suggesting that the utilization of insurance to mobilize savings and investments plays a role in fostering economic growth.
Furthermore, the study finds a statistically significant positive correlation between the quantity of taxes paid and GDP.The efficacy of government initiatives aimed at aiding businesses and households in surmounting financial obstacles as catalysts for economic expansion is indisputable.This observation lends credence to the notion that insurance is an indispensable catalyst for fostering economic expansion.It facilitates the recovery of businesses and households during financial crises and strengthens their capacity to endure and rebound from such difficulty.This observation aligns with Hypothesis 3, which posits that insurance serves as a catalyst for economic growth by facilitating the resilience and recovery of households and businesses in the face of financial crises.
This study investigates the impact of the non-life insurance sector on the economic growth of Nepal through the application of multiple linear regression analysis.The gross domestic product (GDP) of Nepal is influenced by various factors, such as total investment, total premium, total tax paid, and total profit of the non-life insurance sector.
The data for this study encompasses the period from 2013 to 2022 and has been sourced from reputable institutions, including the Insurance Board of Nepal, the Central Bureau of Statistics, and the Ministry of Finance of Nepal.
The results of the regression analysis show that the identified variables, which include total investment, total premium, and total tax paid, have been established as statistically significant factors for predicting changes in GDP.This statistical significance implies that alterations in these variables can reliably forecast corresponding changes in GDP.However, it is crucial to note that the coefficient of total profit does not exhibit statistical significance in this particular analysis, implying that total profit may not serve as a robust predictor of GDP in this specific context.Possible explanations for this insignificance could be related to data limitations or the potential presence of a nonlinear relationship between total profit and GDP.
More specifically, the positive coefficient linked to total investment signifies that an increase in total investment correlates with a rise in GDP, empha-sizing the pivotal role of investment as a driver of economic growth and highlighting its significance in economic policy discussions.Furthermore, the positive coefficient associated with total tax paid indicates that an increase in total tax payments is connected to an uptick in GDP, underscoring the importance of public revenue in bolstering economic activity and development.
Conversely, the inverse correlation between total premiums and GDP suggests that an upsurge in overall premium payments is associated with a decline in GDP.This underscores the potential impediment to economic growth that higher insurance premiums may impose.A comprehensive approach to insurance pricing and regulation is essential.The results of this study are consistent with many studies including those conducted by Rao

CONCLUSION
This study investigates the potentiality of Nepal's non-life insurance industry to contribute to the country's economic growth, revealing interesting relationships between Nepal's GDP and various factors through detailed analysis.When looking at the factors like total investment, total premium from insurance, total profit, and total tax paid, it's found that they all have different effects on GDP.
There's a positive correlation between total investment and GDP.This means that as investments in Nepal's economy increase, the overall economic output (GDP) also tends to rise.The analysis suggests a negative correlation between total insurance premiums and GDP.This could be because higher premiums take money out of circulation, potentially reducing spending and economic activity.Further investigation is needed to understand this relationship better.There's a positive correlation between total taxes paid and GDP.This might seem counterintuitive, but tax revenue can be used by the government to fund infrastructure, social programs, and other investments that stimulate economic growth.
This analysis doesn't show how total profit affects GDP.More research may be needed to understand this relationship.The model explains a significant portion (54%) of the variation in Nepal's GDP.This suggests it's a good starting point for understanding the factors influencing the country's economic performance.However, there's still room for improvement, and other factors might need to be incorporated.The statistical tests indicate that random factors specific to individual observations (e.g., companies or regions) are likely influencing the data.Choosing a random effects model over a fixed effects model acknowledges this and provides more accurate results.
passionately emphasizes the insurance industry's immense value as a global economic asset.By fulfilling a multifaceted role, the insurance sector becomes a catalyst for promoting and sustaining economic growth on a global scale (Safitri, 2019).At the heart of these scholarly contributions, Ege and Saraç (2011) emphasize the critical role of insurance in promoting financial inclusion in developing nations.Fida et al. (2020) argue that insurance can help reduce poverty and inequality by providing a safety net for people who suffer losses, such as those caused by natural disasters or accidents.Iyodo et al. (2020) also show how non-life insurance can contribute to economic growth in the ASEAN and MENA regions.
Kharel (2019) 2018)estled within the mosaic of insights, Kondrat et al. (2019) delve into the investment activities of life and non-life insurance companies during the period of 2001-2016, re-The findings of Basu and Aithal (2022) pointed to unsatisfactory performance among private nonlife insurers in India concerning financial indicators during the period under examination.Siddik et al. (2022) further enrich this narrative by emphasizing the detrimental impact of financial insolvency on the profitability of non-life insurance companies.These studies, each a distinct thread in the fabric of understanding, collectively shed light on the financial challenges and vulnerabilities faced by non-life insurers in diverse contexts(Dash et al., 2018).developmenthave a positive feedback loop; however, this may vary by nation.Kharel (2019)highlighted the insurance sector's enormous effect on Nepal's economic growth in several ways, whereas Insurance Markets and Companies, Volume 15, Issue 1, 2024 http://dx.doi.org/10.21511/ins.15(1).2024.03

Table 3 .
Correlated random effects -Hausman test Source: Results from data analysis.

Table 3
depicts the outcomes of the Hausman test for correlated random effects.The null hypothesis of the Hausman test is that the random effects model is the same as the fixed effects model.The alternative hypothesis suggests that the random effects model is more efficient than the fixed effects model.The table shows that the chi-squared statistic for the Hausman test is 97.904, with 4 degrees of freedom.The p-value for the test is 0.000, which is less than the 0.01% significance level.This means that we can reject the null hypothesis and conclude that the random effects model is more efficient than the fixed effects model.In other words, the random effects model is the better model to use for this data.