This paper uses basic educational and health-related factors as antecedents for life expectancy at birth on country level. Life expectancy is a highly complicated construct and varies strongly with respect to national borders. Aiming to provide a prediction model, prognoses are based on health expenditure per capita, HIV prevalence, immunization rates and literacy rates and justify cross-national dissimilarities in expected lifetime in a simple manner. The model helps to estimate the importance of the used antecedents among each other and gives a basic understanding on how life expectancy could potentially be influenced. Due to the usage of basic antecedents, the highlighted potential will be of special interest for developing countries.
Table of Contents
1 Introduction
2 Population and Sample
3 Variables
4 Methodology
5 Analysis of Results
6 Discussion
7 Conclusion
Research Objectives and Topics
This paper aims to develop a linear statistical model to identify and predict the influence of fundamental health-related and educational factors on human life expectancy at birth across different nations. By utilizing accessible and easily manipulatable variables, the research seeks to provide decision-makers with insights into how moderate efforts in specific societal areas can effectively impact population health outcomes.
- Linear regression modelling of life expectancy
- Impact of health expenditure on longevity
- Correlation between literacy rates and life expectancy
- Influence of HIV prevalence on national life expectancy
- Analysis of cross-national health and education disparities
Excerpt from the Book
4 Methodology
All data has been received from The World Bank’s website. The World Bank gathers data by self-conducted primary research or it uses data that has been directly collected in the specific country by the local government or statistical institutions. Due to the standing of the collection institutions, a decent data quality and accuracy is ensured. This linear model will be built on the most recent available data which dates back to 2013. As already mentioned, data availability reduced the population of 249 nations to a sample of the size of 89 nations.
According to (Mazumdar 2001) there is “no clear and quantitative determination of factors affecting the variations in life expectancy.” This study assumes that literacy rate, immunization level, HIV prevalence as well as health expenditure are indicators for basic education level respective health care quality. They determine significantly the overall life expectancy within a country. These indicators are easy to understand and can also be influenced in a relative simple manner. Hence, the model will show options on how to influence life expectancy and furthermore determine the quantitative influence of each factor.
When building a linear model, it has to be acknowledged that life expectancy cannot be improved unlimitedly. Consequently, health expenditure cannot be included in a linear form. A linear approach would suggest that an additional dollar for health expenditure always increases life expectancy by the same value, independent on the current level of health expenditure. Therefore, the model seeks to capture the decreasing impact of spending behavior with the logarithmic function, supporting the hypothesis that the impact of spending on health related subjects has a decreasing impact. This approach assumes that an additional dollar adds more value to life expectancy, if the current level of expenditure is low, and less value, if the level of expenditure is high. The concavity of the logarithmic function minds an upper bound for life expectancy.
Summary of Chapters
1 Introduction: Provides an overview of the research interest regarding life expectancy, its determinants, and existing scholarly findings on educational and social factors.
2 Population and Sample: Details the scope of the study, covering 249 nations, and explains the criteria for the selection of the 89-country sample based on data availability.
3 Variables: Defines the dependent variable (life expectancy) and the five independent variables, including health expenditure, immunization rates, HIV prevalence, and literacy rate.
4 Methodology: Describes the data source and the rationale behind using a linear regression model with logarithmic transformations to account for diminishing returns in health spending.
5 Analysis of Results: Presents the statistical evaluation using SPSS, detailing correlations, model fit, and the decision to exclude immunization rates from the final model.
6 Discussion: Interprets the model's coefficients, evaluates the significance of each indicator, and addresses the study's limitations regarding data selection and model exhaustiveness.
7 Conclusion: Summarizes the key findings, highlighting the potential for decision-makers to improve life expectancy through targeted investments and HIV prevention.
Keywords
Life Expectancy, Linear Regression, Health Expenditure, HIV Prevalence, Literacy Rate, Immunization, Public Health, Socio-economic Factors, Statistical Modelling, Global Development, Education, Predictive Analysis, Population Health, Healthcare Quality, Data Analysis
Frequently Asked Questions
What is the primary focus of this research paper?
The paper focuses on identifying and modeling the basic antecedents of human life expectancy at birth on a country-by-country level using linear regression.
Which indicators are used as independent variables?
The study examines health expenditure per capita, adult literacy rates, child immunization rates for DPT and measles, and the prevalence rate of HIV.
What is the main research objective?
The objective is to provide a predictive model that helps politicians and decision-makers understand how basic, manipulatable factors can be used to improve national life expectancy.
What scientific method is employed?
The author uses linear regression analysis, specifically utilizing logarithmic functions for health expenditure to account for decreasing marginal returns.
What does the main body of the paper cover?
It covers the selection of the population and sample, the definition of variables, the methodology of the linear model, statistical result analysis, and a discussion of the practical implications and model limitations.
Which keywords best describe this study?
Key terms include Life Expectancy, Linear Regression, Health Expenditure, HIV Prevalence, and Literacy Rate.
Why were immunization rates excluded from the final model?
They were excluded because, while they correlate with life expectancy on their own, their impact becomes statistically insignificant when combined with health expenditure and literacy rates due to multi-collinearity and the herd immunity effect.
How is health expenditure modeled?
It is modeled using a natural logarithmic function because the study assumes that the impact of additional health spending decreases as a country's current level of expenditure increases.
What does the constant value of 42.688 in the final model represent?
It represents the baseline life expectancy in a theoretical scenario with no HIV cases, no health expenditure, and an illiterate population.
- Quote paper
- Patrick Glasen (Author), 2015, Basic Antecedents of Life Expectancy at Birth. Linear Regression Modelling, Munich, GRIN Verlag, https://www.grin.com/document/311417
