Investigating the Effects of Institutional, Economic and Environmental Factors on Healthy Life Expectancy

Objective: The relationship between health and other sectors of society, including the economy, is a two-way relationship. In other words, health has an impact on the economy, and the economy can also affect health. For example, improving health should be considered an important factor in economic development and growth today, just as improving economic variables can lead to expanded health outcomes. Health evaluation encompasses various indicators, such as life expectancy at birth, mortality rates, and healthy life expectancy. Life expectancy indicates the average number of years a person is expected to live without experiencing long-term illness. The notable distinction of life expectancy lies in considering the health dimension. Fluctuations in this indicator are generally attributed to changes in life, health, or both. Enhancing this indicator has significant implications for the economy, as it can impact different sectors, including the market and government spending. Therefore, studying and striving for a healthy life is of paramount importance. Additionally, the dimensions of healthy life expectations can vary, and each of them requires careful examination to determine their specific effects and extent. Consequently, this study primarily focuses on exploring the impact of economic, institutional, and environmental variables on life expectancy.

‏ Method: The present study examines the impact of economic, institutional, and environmental variables on health in 37 countries that share similarities in social and economic factors, including press freedom, freedom of religion, and the percentage of GDP (Gross Domestic Product). The study covers the period from 2000 to 2021 and utilizes the threshold panel approach. The threshold panel regression is a type of regression model based on panel data. In these models, coefficients can vary over time and for cross-sectional units, and the panel observations are categorized into one or more homogeneous groups based on the specified threshold variable, which is less than the threshold value. In other words, the regression analysis classifies individual observations into different classes based on the value of an observed variable. I made several corrections to improve the sentence structure, clarity, and grammar of your paragraph. If you have any more paragraphs or text you'd like me to review, please feel free to share them.

Results: The reliability of variables is a necessary condition for using the threshold regression method. Therefore, the reliability of the variables is checked, and the results confirm that this condition is met, rendering the issue of false regression irrelevant. The F_Limmer test rejects the constant width of the origin for all sections, and the Hassman test confirms that the model remains constant through the method. The Hansen LR test verifies the existence of a threshold. It is important to note that the variable of good governance is considered the threshold variable, while health costs, CO2 emissions, and the misery index are regarded as control variables. The results indicate that for countries above the threshold, health costs and the misery index have the highest impact on healthy life expectancy, whereas for countries below the threshold, the coefficients of health and carbon dioxide have the most significant effect. Therefore, comparing the two groups of countries suggests that health costs have the greatest impact on healthy life expectancy. Consequently, it is crucial to consider health costs to increase life expectancy in these countries. However, it is worth noting the influence of the misery index and carbon dioxide emissions. Comparing the coefficients of these two variables between countries above the threshold and those below it reveals that the misery index significantly affects healthy life in the upper threshold group, while its coefficient is lower for the lower threshold group. As for carbon dioxide emissions, it is important to emphasize that the impact of this variable on the expansion of healthy life expectancy is highly significant in the lower threshold group, but its effect on countries above the threshold is minimal. It is important to highlight that the effect of per capita income on improving healthy life expectancy is relevant in both groups.

‏ Conclusion: Government officials and policymakers should prioritize attention to health costs in order to improve public health. Increased health costs facilitate access to healthcare services and reduce treatment expenses for individuals. However, it is important to note that the effectiveness of increased health costs in improving health outcomes relies on the presence of a robust healthcare system in countries, ensuring equitable access for all individuals, particularly those with low incomes. Reducing misery should also be a priority, as it maximizes the impact of health cost policies. An increase in the misery index results in higher health costs for both the government and the public. Additionally, it leads to rising household expenditure on essential goods, potentially compromising community health. Carbon dioxide emissions play a significant role in healthy life expectancy and should be addressed to enhance this indicator. Governments hold a key role in controlling and reducing pollution within their countries, as they can implement effective policies and monitor their enforcement. Therefore, improving air quality should be a priority for governments.