Factors affecting the average life expectancy of citizens in countries all over the world

Recognizing the importance of life expectancy to the development of countries, the research team decided to choose the topic "Factors affecting the average life expectancy of citizens in

FOREIGN TRADE UNIVERSITY INTERNATIONAL ECONOMICS FACULTY

Lê Vương Đức Hiếu 2013150018Nguyễn Phan Hương Ly 2011150831

HaNoi, 9 October, 2021

LIST OF TEAM MEMBERS

Nguyễn Phan Hương Ly 2011150831

Nguyễn Phan Hương

Ly

Lê Vương Đức Hiếu

Phạm Mai Anh

Hoàng Thùy Dương Hoàng Thùy

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 5

CHAPTER 2: LITERATURE REVIEW 7

1 Overview of average lifespan 7

2 Factors affect life expectancy of citizens in countries around the world in 2016 7

2.1 GDP per capita (USD) 7

2.2 Measles vaccination rate (%) 8

2.3 Percentage of government spending on health (%) 9

3 Research overview 9

3.1 Published relevant studies 9

3.2 Research gap 11

CHAPTER 3: RESEARCH METHOD AND ECONOMETRICS MODEL 12

1 Research hypothesis 12

2 Establish theorical model 12

2.1 Methods of data collection and processing 12

2.2 Model of theory 13

2.3 Data description 14

2.4 Describe the correlation between the variables 15

CHAPTER 4: QUANTITIES TESTS 17

1 Estimated model 17

1.1 OLS testing result 17

1.2 Sample Regression Function: 17

2 Diagnosing and solving problems of the model 17

2.1 Ramsey RESET Test 17

2.2 Multi-collinearity Test 18

2.3 Heteroskedasticity Test 18

2.4 Testing the normality of residual u 20

3 The results of estimators after solving problems 21

4 Testing hypothesis of postulated model 21

4.1 Testing statistical significance of regression coefficients 21

4.2 Testing overall significance of the model 22

4.3 Testing the significance of the model with economics theories 22

5 Result explanation 23

5.1 Meaning of estimators of regression coefficients 23

5.2 Determination coefficient (R-squared) 24

5.3 Result analysis 24

CHAPTER 5: RECOMMENDATIONS AND SOLUTIONS ON THE AGE OF COUNTRIES 26

CHAPTER 6: CONCLUDE 28

REFERENCES 29

CHAPTER 1: INTRODUCTION The average human life expectancy has always been evaluated as one of the most special topics of interest in many large research works Because the living expectation

of a country's citizens is one of the foundations for assessing the stability of economic, political, and social issues, especially the level of health of that country

Recognizing the importance of life expectancy to the development of countries, the research team decided to choose the topic "Factors affecting the average life expectancy of citizens in countries all over the world in 2016" with the aim is to study more about this topic, and at the same time, it is possible to propose some effective solutions to improve the average life expectancy of the population

This essay will use panel data analysis methods to study and analyze some of the

of the study is the degree of influence of 3 main influencing factors, including GDP per capita, measles vaccination rate, and percentage of government spending on health By applying knowledge from Econometrics along with socio-economic understanding to analyze and find the relationship between variables, the research team's essay will answer the following questions:

1 What are the main influencing factors on the average life expectancy?

2 What measures should be taken to increase life expectancy of people in those countries?

During the research, the data used was collected from reputable sources, mainly from the website of the World Bank and the World Health Organization, combined with the OLS method

The thesis of the research team consists of 4 chapters:

Chapter 1: Literature review and research hypothesis

Chapter 2: Research methods and econometric models

Chapter 3: Estimation, model testing, and statistical inference

Chapter 4: Solutions and recommendations

The research team has made great efforts in researching and finding information to complete this essay, but due to many limitations of expertise and experience, the essay cannot avoid errors The research team is looking forward to receiving comments from

Ms Đinh Thanh Bình to be able to complete the essay better

Sincere thanks to the research team!

CHAPTER 2: LITERATURE REVIEW

1 Overview of average lifespan

Average life expectancy (Life Expectancy, LE) is defined according to Decision No 291/QD-TTg on promulgating a system of human development evaluation criteria for monitoring, evaluating the implementation of the planning and the human resource development strategy for the period 2011-2020 that: “The number of years that a person

is expected to live until death assuming the age-related death rate is at the level of the time of calculation and the intact living conditions from birth to death” In other words,

it is the average longevity a person is expected to live based on year of birth, current age, and other demographic factors including gender The most commonly used method to measure life span is life expectancy at birth or life expectancy at birth (LE) It can be determined in two ways, the LE cohort is the mean lifetime of a real cohort (individuals born in a given year) and can only be estimated by cohorts born decades ago The LE cycle is the mean survival time of a hypothetical cohort presumed to be exposed, from birth to death, to the observed mortality in a given year

The role of life expectancy in the development of a country: Life expectancy is one

of the indicators representing human development in a country and to some extent, it can reflect the living standard of that country As claimed by Ranis et al (1999), economic growth and development is a two-way relationship According to them, the first chain includes economic growth in favor of human development, since economic growth is likely to cause households and individuals to increase their spending, which means they will have better access to health and other services, resulting in longer longevity At the same time, the increase in spending on health and medical facilities is also an important contributing factor to economic growth Lower mortality can increase per capita income by increasing the productivity of available resources (typically human capital)

2 Factors affect life expectancy of citizens in countries around the world in

2016

2.1 GDP per capita (USD)

Gross Domestic Product (GDP) is the total market or monetary value of all final goods and services produced within a country's territory, over a given period of time

As a general measure of domestic production as a whole, it functions as a comprehensive scorecard of a country's economic health

Effect of GDP on life expectancy: When GDP is high, health expenditures will be given higher priority when the government allocates resources Medical expenses include all costs or expenses for medical care, prevention, promotion, rehabilitation, public health activities, health management and regulation, capital formation with the primary goal is to improve health in a country or region As medical costs increase, the average life expectancy of people living in that country improves markedly The amount

of health spending is one of the most important indicators of development If that country has a rapid increase in economic growth then life expectancy also tends to increase

2.2 Measles vaccination rate (%)

Measles (IMMU) is an infectious disease of particular pediatric importance, highly contagious and with a very high mortality rate According to a report by the World Health Organization (WHO), in the first three months of 2014 alone, nearly 56,000 cases

of measles were recorded, in 75 countries around the world Accordingly, every hour that passes, globally, 14 children die from measles However, now with the availability

of a safe and effective vaccine, this disease is no longer a tragedy Childhood measles vaccination measures the proportion of children between the ages of 12 and 23 months who received the measles vaccine before 12 months A child is said to have full immunity to measles after a single dose of the vaccine

Effects of measles vaccination on average life expectancy: A report from WHO states that today vaccines prevent 2.5 million deaths a year: Every 5 minutes, a single live is saved by vaccines all over the world Overall, vaccines have done and continue

to do an excellent job in eliminating or alleviating the impact of childhood diseases It

is obvious that vaccination has contributed significantly to the increase in life expectancy over the past centuries, thereby inferring the rate of measles vaccination is

2.3 Percentage of government spending on health (%)

Percentage Government Expenditure (GE) is the percentage of government spending

on total costs of solving problems related to health care and the national healthcare system According to the World Health Organization, medicare spending is made up of expenditures from the state budget, from direct payments from people's pockets and from other sources such as voluntary health insurance from employer-paid health plans and sponsored activities by NGOs According to World Health Organization Director-General Tedros Adhanom Ghebreyesus, increased domestic health spending is essential

to achieve universal health coverage and the sustainable development goals and related sustainability Spending on health is not an expense, it is an investment in poverty reduction, jobs, productivity, inclusive economic growth and a healthier, safer, more equitable society

health-The effect of the percentage of government spending on health: In middle-income countries, budgets for medical care spending per capita have doubled since 2000 On average, countries spend $60 per person in low-middle-income countries and nearly

$270 per person in upper-middle-income countries As countries' public spending on health increases, people will be less likely to fall into poverty when using health services However, government spending on healthcare only reduces inequalities in access when allocations are carefully planned to ensure that the entire population can get primary health care It is expected that the higher the level of public expenditure and per capita spending on health, the higher the level of health concern, the condition for solving health problems through investment in quality The better the quality of services and infrastructure, the longer the average lifespan

3.1 Published relevant studies

Research by Vlatka Bilas, Sanja Franc and Mile Bosnjak: Determinants of life expectancy in European Union countries

The main aim of this study was to understand the determinants of life expectancy among 28 European Union countries Selected variables namely gross domestic product

(GDP) growth rate, population growth rate, educational attainment, educational enrollment, GDP per capita and life expectancy were considered for the aforementioned countries for the period 2001 to 2011 on an annual basis using panel data analysis.Due to the differences between countries observed, heterogeneity of variance problem has emerged In the end, the authors decided to take logarithmic values of the observed variables and thus the heterogeneity problem disappeared They then analyzed the observed variables for each country (time series for each country) In the observed variables are integrated in the same order, the researchers use the Johansen integration method.

The results obtained after applying appropriate modeling techniques show that GDP per capita and educational attainment have an explanation level from 72.6% to 82.6%

of the difference in average lifespan (depending on year of observation) Therefore, these two independent variables are statistically significant In which, GDP per capita has a positive effect on life expectancy, and education level has a negative effect on life expectancy, opposite to what have been expected

Study by Wagstaff and Cleason (2004): A study evaluating the outcomes of health care and nutrition

Wagstaff and Cleason (2004) conducted a study evaluating the outcomes of health care and nutrition in the Millennium Development Goals for 180 countries around the world The study shows that the extent of public spending on health development is positive and depends on the effectiveness of policies and institutions in each country Other macro factors that also have an impact on development in the health sector include: GDP per capita, population growth rate However, the research model for regression analysis is still quite simple, not highly explanatory for the impact of public spending

on health

Research by Jacob Novignon, Solomon A Olakojo and Justice Nonvignon (2012): Impact of public and private sector spending on health and wellness status of people in Sub-Saharan Africa (SSA)

Studying the impact of public and private sector spending on the health and being of people in Sub-Saharan Africa (SSA), Jacob Novignon, Solomon A Olakojo and

well-Justice Nonvignon (2012) found out that , spending in the health sector has a significant effect on health status through improving life expectancy, reducing mortality and infant mortality Both public and private spending on the health sector is strongly linked to health status.

The Rino Rappuolli Study (August 2014): The impact of vaccines on longevity, well-being and science

Results from the study indicate that children born in several developing countries in Asia between 1994 and 2013, the vaccine prevented 322 million illnesses, 21 million hospitalizations and 732,000 premature deaths, saving up to $295 billion in direct medical costs These results show the positive impact of using vaccines to protect health and increase life expectancy

3.2 Research gap

The scope of research here is still relatively limited, Vlatka Bilas, Sanja Franc and Mile Bosnjak only focus on European Union countries with many similarities between countries, the question is, whether the study results hold for the case of less developed countries?

In addition, Rino Rappuoli's study only studied the impact of vaccines in general on human life expectancy, not a specific study investigating the effect of measles vaccination on average lifespan Furthermore, among the previous studies presented, none of these studies used the OLS method to estimate the multidimensional influence

of all three factors on the life expectancy dependent variable

CHAPTER 3: RESEARCH METHOD AND ECONOMETRICS MODEL

1 Research hypothesis

From understanding the theories and previous studies, the research team found that there are still gaps that exist as mentioned above, therefore, the team would like to present an essay on the factors affecting the average life expectancy of countries around the world in 2016 includes the following independent variables: GDP per capita, measles vaccination rate, and percentage of government spending on health to get a more accurate and comprehensive overview of previous studies New in the study is the inclusion of a representative factor of health input into the model, while previous studies have barely mentioned this issue

The research hypothesis was developed by our team:

GDP per capita, measles vaccination rate, and percentage of government spending on health have a significant impact on life expectancy

GDP per capita is positively correlated with life expectancy

Measles vaccination rates are positively correlated with life expectancy The percentage of government spending on health is positively correlated with life expectancy

2 Establish theorical model

The regression analysis method involves finding the dependency of one variable, called the dependent variable, on one or more other variables, called independent variables, for the purpose of estimating or predicting the expected value of the dependent variable dependent on knowing the values of the independent variable in advance, specifically in this study, analyzing the relationship between the independent variable (GDP per capita, measles vaccination rate, percentage of government spending on health) and the dependent variable (average life expectancy)

2.1 Methods of data collection and processing

To research and run the model, the team collects samples and their estimates are based on data from 266 observations in 2016 from 266 countries worldwide The model's data is cross-sectional data, collected by statistical methods with reliable data sources from the World Bank

The data were selected by estimating the coefficients of the OLS ordinary least mean model, and the statistical significance of the regression coefficients and model fit based on the observations was checked, as well as compared with previous and similar studies to find the best results to use for analysis During the work, the group used the knowledge of econometrics and macroeconomics, quantitative methods with the main support of STATA software, Microsoft Excel, Microsoft Word to summarize and complete this essay.

2.2 Model of theory

After researching and referring to the studies that have been done before, our group decided to use multiple regression analysis to find out the dependence of the dependent variable on average life expectancy for 3 independent variables, which is the rate of life Measles vaccination rates, percentage of government spending on health and GDP per capita during 2016

Based on economic theory, to analyze the influence of factors on average life expectancy, the group chose to study a linear regression model and take the logarithm

of the GDP variable:

𝐿𝐸 = 𝛽0+𝛽1ln𝐺𝐷𝑃 + 𝛽2𝐼𝑀𝑀𝑈 + 𝛽3𝐺𝐸 + 𝑢𝑖

In which:

𝛽0: Constant parameter

𝐿𝐸 = 𝛽0+𝛽1ln𝐺𝐷𝑃 + 𝛽2𝐼𝑀𝑀𝑈 + 𝛽3𝐺𝐸 + 𝑢𝑖

In which:

𝛽0: The estimate of intercept factor

𝑢𝑖: residual, estimate of random errors

2.3 Data description

a Data source

The data used in this essay has been collected from the official websites of the World Bank and the World Health Organization (WHO)

The survey was carried out in 266 countries around the world, with different levels

of development and histories Therefore, we find that this sample space is large enough, objective enough and reliable enough to build a regression model

b Description of statistics

In order to help readers have the most overview as well as be able to make some initial judgments, our team will describe the data before proceeding to delve into the data analysis Through this description, our team can predict some errors that may occur when running the model due to the lack of data

Based on the model, our team will use the Gen command to get the logarithm of the GDP variable and name the variable lnGDP Then, our team will use the Sum statement

in Stata to describe the independent and dependent variables, obtaining the results:

Table 1 Statistical description of variables

Variable | Obs Mean Std Dev Min Max