The Effects of Financial Structure on Life Expectancy (Cup-FM Approach)

Objective:To achieve high levels of health, identifying the nature of health and also the factors affecting it have the most important role. If the factors threatening health and their importance are not identified, the measures taken to promote the health of the individual and society will be taken in an atmosphere of doubt. On the other hand, the limited resources of some countries (especially poor and needy countries) have been allocated over the years in a way that has only resulted in reduced health and increased mortality. Thus, such questions have always been asked by economists and policymakers: what factors can affect the level of life expectancy? And what is the role of the financial structure in this regard?
Economists and policymakers have paid close attention to finding the optimal method and mechanism for promoting public health, and in recent years these efforts have had valuable effects on human health and well-being in many parts of the world. The level of increased life expectancy depends on how countries invest in improving social indicators such as health, education, retirement plans, health programs, food facilities, and improving the environment. The health status of people in the community is one of the things that health policy makers are always looking to improve. Life expectancy index is one of the components that shows the extent to which countries have achieved the goals of the health sector. The life expectancy variable is affected by several economic and social factors. In this regard, the present study intends to study the effect of factors affecting life expectancy with emphasis on financial structure.
Methods: Financial structure has important effects on life expectancy. These effects have been positive in some studies and negative in others. However, it is not possible to comment from the outset on the effects of financial structure on life expectancy. So the question is, what effect has the financial structure had on life expectancy in selected countries? To answer this question, the present study tries to use the combined data method to determine the effect of financial structure on life expectancy in selected Islamic countries (including: Iran, Turkey, Azerbaijan, Pakistan, Qatar, Oman, Bahrain, UAE). Study the United Arab Emirates, Saudi Arabia, Kuwait, Iraq, Jordan and Malaysia). In this research, data and information of selected countries in the period 2000-2020 have been extracted from the World Bank database and the research model has been estimated using the Cup-FM method.
Although several methods have been proposed to examine the panel co-integration relationship between variables, most of these methods only discuss the existence or absence of the relationship and do not provide information about the co-integration vector. To address this shortcoming, several methods have been proposed, including the Cup-FM method. Bai and Cao (2006) proposed the Cup-FM method, which uses a factor structure to determine the source of the cross-sectional dependence and presents the aggregate vector. This method calculates the coefficient vector coefficients by estimating the parameters and the long-term covariance matrix and the factor loads in reverse. This estimator, like the FMOLS estimator, is resistant to successive autocorrelation bias and endogenous bias, and in addition, is indifferent to the meaning and anonymity of the explanatory variables.
In this study, based on research experiences to investigate the effects of financial structure on life expectancy, the regression model used in Chireshe (2018) research has been modeled, so the model used in the leading research is as follows:
Where LE_it is life expectancy at birth, FS_it is a variable of financial structure (loans to the private sector by the banking system as a percentage of GDP), E_it is energy consumption (kg equivalent of crude oil), PGDP_it is GDP per capita and HEXP_it  is health expenditure ( Percent of GDP). U_it is also a regression disorder about which the classical assumptions are true. All data related to the model variables can be extracted from the World Bank database and the research model estimation method in this article is the data panel method.
 
Results: According to the research results, the effect of GDP growth at a fixed price on life expectancy is positive and indicates that with increasing economic growth, people have been able to invest more in education, improve their nutritional conditions and health facilities. Better and use more. Hence, GDP growth has a positive effect on people's life expectancy. This effect is statistically significant at the significance level of 5%. The effect of credit granted to the private sector by the banking system as a percentage of GDP on life expectancy is positive and these effects are statistically significant at the level of 5%. Because people, especially low-income or middle-income people, have been able to use bank credits and improve their economic activities by improving the financial system. This in turn can increase the use of educational and health services by these people and increase their quality of life and life expectancy. In contrast, the effect of energy consumption on life expectancy at birth is negative and this effect is statistically significant at the level of 5%. Because energy consumption, especially in recent years, causes more greenhouse gas emissions and environmental pollution, which can have a negative impact on health and life expectancy. Also, the effect of health expenditures (as a percentage of GDP) on life expectancy is positive and this effect is statistically significant at the level of 5%. Because high health expenditures lead to more and better health and medical services in the country and increase people's life expectancy.
Conclusion:According to the research results, to achieve superior health status, the development of financial structure should be part of health policies and strategies in countries, and the goal of policies should be to deepen and expand the financial sector to ensure inclusive financial development.