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1 Factor Affecting Life Expectancy 1

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1 Factor Affecting Life Expectancy 1.Introduction An individual's life expectancy is defined as the number of years an individual is expected to live. Based on the average life expectancy of an individual living in a given country, the health status of the population can be accurately measured. The factors affecting life expectancy have been previous researched by many researchers. According to healthgov.au, the 2 life expectancy of an individual is dependent on several factors, which include; socioeconomic status of an individual, the health quality system, the individual's ability to access the health system, health behaviors such as consumption of alcohol, tobacco, eating habits, genetic factors and external environmental factors such as overcrowding, quality of drinking water and sanitation. There have been several kinds of research about the factors affecting the life expectancy of an individual. In their research, Chan & Kamala Devi (2012) found that life expectancy is greatly influenced by the availability of healthcare resources and higher socioeconomic advantages. The demographic changes increase life expectancy by way of healthcare resources. According to the literature, the critical component of demographic and socioeconomic determinants include gender, age, education, and GDP per capita (Khang et al., 2010; Shkolnikov, 2006). Their research found evidence of improving healthcare coverage to improve life expectancy. This evidence was further supported by Hosokawa et al. (2020), where it was found that life expectancy is significantly affected by the number of doctors and therapists, support clinics for home healthcare, and home visits treatment. Blinova et al. ( 2020), in their study about the reasons for a low life expectancy of the rule population in Russia, found statistically significant social, behavioral, and economic factors affecting individuals' life expectancy. This topic is of interest to other researchers and me because identifying factors affecting life expectancy can help improve an individual's average life. For example, based on part research, it is clear that improving the given regions or the country's health condition can dramatically improve the individual's life expectancy. Further, shifts in life expectancy are often used to describe the trends in mortality. Life expectancy can predict how populations will age significantly on the planning and provisions of services and government support. 3 Although several types of research have found evidence of the effects of demographics, socioeconomic instability, and healthcare resources on LE, there has been little research concerning all countries of the world while considering the effect of both longitude (time-series) and cross-section (countries). 2.Material and Methods Aim The aim of the paper is to develop a panel data regression model to account for the factors that significantly affect the life expectancy of the target country. Settings The study is a cross-country study to collect the annual data for all the countries of the world. The data involved collecting demographic, socioeconomic and health resources of the countries from 2000 to 2015.I used the life expectancy data in age of countries instead of life expectancy at 1 or 5 years. Data and Sources The dataset for the study will be downloaded from the third-party Kaggle website. However, the primary source of the dataset is the World Health Organization that keeps track of the health status as well as different factors for all countries. The dataset used is secondary as it is directly downloaded from the Kaggle website. The type of research includes secondary source research. The data 4 Figure 1.The above diagram shows a conceptual model or factors being categorized into different categories that can affect the LE In the present study, the dependent variable is the life expectancy of an individual. The independent variables are shown in figure 1. The independent variables can be categories into three different themes; socioeconomic status, health resources and demographic sources. For the socioeconomic categories, I selected GDP and schooling as independent variables. The independent variables in the health resource category include; Hepatitis-B, Measles, Diphtheria, Aids, total expenditure, and alcohol. The independent variables in the demographic categories include; the population of the country, adult mortality rate, infant deaths, and BMI. In addition to these variables, a nominal variable was formed, which was divided into two categories. These categories include whether the country is developed or not. A value of 1 was assigned for developed countries and 0 for developing countries. 5 Correlation Matrix A correlation matrix between the independent and dependent variables is shown in Appendix A. Based on the correlation analysis, the variables having a positive correlation with the dependent variable include; alcohol consumption, percentage expenditure, Hepatitis B, BMI, polio, Diphtheria, GDP, and Schooling. The correlation of life expectancy with alcohol, Hepatitis B, BMI, polio, and Diphtheria is not expected. It is because a negative correlation was expected. Among the health resources, the independent variables with positive correlation include; polio, Diphtheria, and percentage expenditure. It may be due to the non-consideration of the effect of country and time series for the dataset. The variables with a negative relationship with Life expectancy include; adult mortality, infant deaths, alcohol, Measles, under-five deaths, HIV/AIDS, and population. The demographic factors with a negative relationship with the LE include; adult mortality, infant deaths, and under-five deaths. However population was found to have a very weak relationship with the LE, and BMI had a moderate positive relationship. Due to the presence of multicollinearity, only a limited number of independent variables were selected.These variables include;adult mortality,percentage expenditure, BMI,under-five deaths,polio,diphtheria, Regression Model To predict the life expectancy based on several predictor variables,the best model would be panel data regression model.The panel data regression model is best suitable for observation that have typically cross-section and longitudinal observation.Here,the cross-section observations means that the data about the life expectancy of different countries.The longitudinal observation implies the time series observation which include the year beginning from 2000 to 2015.The panel data model is the best model as N observations is measured on T occasions.Thus with observations 6 that span both time and individuals in a cross-section,more information is available giving more efficient estimates.Further,an important advantage of using panel data here is that we can use panel data to control for unobserved or unmeasurable sources of individual heterogeneity that vary across individuals but do not vary over time.Also we can control for omitted variable bias.Thus,for the present analysis,panel data regression is the best model. Panel Data Regression Model A panel data has both a cross-section and a time-series dimension. In this data, all time-series observations are observed during the whole time period. Xit,i=1,2,….n,t=1,.2…T T is generally small. The standard static panel data model with i=1,…N ,t=1,…T is Yit =β0 +x’itβ +€it. Here, xit is a K-dimensional vector of explanatory variables without a constant term β0 is the intercept which is not dependent on i and t. β a (K×1) vector, the slopes are independent of I and t. €it. , the error varies over I and t. Results The panel data regression model results are shown in the below table 2.Based on the regression result, the significant predictor variables include; GDP, Schooling, alcohol, percentage expenditure, and polio. The other variables were found to be non-significant. These variables fall under the theme of socioeconomic and health resources. The demographic factor was found to be non-significant. The value of the time and country coefficient is shown in Appendix B. Table 2.Panel data regression model output Term Intercept Estimate 64.318315 Std Error DFDen 0.7993396 422.5 t Ratio 80.46 Prob>|t| 95% Lower 95% Upper |t| 95% Lower 95% Upper