ECONOMETRICS REPORT THE IMPACT OF GDP PER CAPITA AND OTHER FACTORS ON LIFE EXPECTANCY IN SOME COUNTRIES IN 2015

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS *** ECONOMETRICS REPORT THE IMPACT OF GDP PER CAPITA AND OTHER FACTORS ON LIFE EXPECTANCY IN SOME COUNTRIES IN 2015 Instructor PhD Đinh Thị[.]

FOREIGN TRADE UNIVERSITY

FACULTY OF INTERNATIONAL ECONOMICS

-*** -

ECONOMETRICS REPORT

THE IMPACT OF GDP PER CAPITA AND OTHER FACTORS

ON LIFE EXPECTANCY IN SOME COUNTRIES IN 2015

Instructor: PhD Đinh Thị Thanh Bình

Hanoi, October 2021

TABLE OF CONTENTS

INTRODUCTION 4

CHAPTER I LITERATURE REVIEW 5

CHAPTER II RESEARCH METHODOLOGY AND MODEL BUILDING 8

1 Research methodology 8

1.1 Collecting data method 8

1.2 Processing data method 8

2 Model building 8

2.1 Identifying type of model 8

2.2 Investigated variables and measurement of investigated variables 9

3 Data description 9

3.1 Data source 9

3.2 Data statistics description 10

3.3 Correlation among variables in model 10

CHAPTER III CHECK FOR THE PROBLEMS OF THE MODEL AND STATISTICAL INFERENCE 13

1 Model estimation 13

2 Testing and fix the defects of the model 14

2.1 Test the model's omitted variables (the correct form of the model) 14

2.2 Multicollinearity testing 15

2.3 Heteroskedasticity testing 15

2.4 Normality of u testing 17

3 Final regression model and estimation result 18

4 Testing for the overall significance of the model and testing for the significance of the independent variables 19

4.1 Testing for the overall significance of the model 19

4.2 Testing for the significance of the independent variables 19

5 Analyzing the estimated results and policy implication 21

5.1 The effect of GDP per capita on national life expectancy 21

5.2 The effect of the proportion of deaths caused by non-communicable diseases (% of total deaths) on national life expectancy 21

CONCLUSION 22

REFERENCES 23

APPENDIX 24

INTRODUCTION

Econometrics is the application of statistical methods to economic data in order

to give empirical content to economic relationships While econometric theory uses statistical theory and mathematical statistics to evaluate and develop econometric methods, applied econometrics uses theoretical econometrics and real-world data for assessing economic theories, developing econometric models, analysing economic history, and forecasting

Gross Domestic Product (GDP) is a monetary measure of the market value of all

the final goods and services produced in a specific time period As a measurement, it is often described as being a calculation of the total size of an economy Therefore, citizens' quality of life is closely linked with GDP per capita as it affects our living conditions,

understand the impact of GDP per capita on standard of living and how that in turn affects our longevity

As economics-based students, realizing the importance of applying econometric methods in research and problem analysis, our group decided to write a report using the

Ordinary Least Square Regression method (OLS) This paper explores the impact of GDP per capita and some other factors on life expectancy GDP per capita is the

primary independent variable, while life expectancy is the dependent variable

We would like to express our deepest appreciation towards PhD Đinh Thị Thanh Bình – lecturer of Econometrics class for providing us with knowledge, advice

as well as detailed instruction throughout our researching and conducting process for this report

Given the fact that this is our first time conducting research using an econometric method, due to the lack of practical knowledge, research methods and execution time, the inevitable shortcomings are for sure Our group is looking forward to getting the opinions of your precious Master with a view to fulfilling our knowledge in this field

CHAPTER I LITERATURE REVIEW

Several papers have acknowledged the relationship between GDP per capita and life expectancy, however, the correlation is still not completely understood (Dayanikli,

et al., 2016) examined an association between a country’s GDP and mortality rates across different nations in 2013 The study found a correlation between income and life expectancy, with higher income being associated with a longer life expectancy The most significant conclusion of this paper is that GDP per capita only affects life expectancy

up until a certain threshold After some GDP, the correlation between the variables weakens, which can be represented by their analysis: below-median GDP to life expectancy regression is stronger than the above-median GDP to life expectancy regression Additionally, this paper also points out that while education may very well lead to increases in life expectancy, there is a strong collinearity between increases in education and increases in per capita GDP, and thus the explanation cannot be labeled

as causal

(Taylor, 2021) also found that national income was positively correlated to life expectancy More specifically, for every tenfold increase in GDP per capita a country can expect the life expectancy of its citizens to increase by approximately 11 years Additionally, there is a statistically significant relationship between population growth and life expectancy, although it’s effect is much smaller than that of national income For every unit increase of 1% in population growth one can expect to see the average life expectancy of a country to fall by 0.81 years Furthermore, this paper believed that the relationship between national income and life expectancy is causal, since countries that are able to produce more with a smaller population (the result being a higher GDP per capita) are able to improve their healthcare infrastructure and its citizens are likely

to increase their standard of living overtime in comparison to citizens of poorer countries

In (Deshpande, et al., 2014), apart from recognizing the positive correlation between national income and life expectancy, they also examined the relationship between national health expenditure and life expectancy Based on the data of health expenditure, national income, government spending, literacy rate and physician density from 81 countries, including both developed, developing and underdeveloped ones, this paper shows that there is no significant correlation between healthcare spending and life expectancy in developing countries, but it does exist in developed countries Additionally, when the multiple regression for least developed countries was run, the only statistically significant variable is physician density, which was significant at the 1% level, specifically indicating that, in developing countries, access to healthcare is a large issue In these places, the fact that there is an available doctor nearby can have a significant impact, therefore the variable of physician density can have a statistically significant impact when it comes to developing countries

(Jetter, et al., 2016) leveraged a gigantic dataset of 197 countries over 213 years (1800 to 2012), leading to a systematic and economically sizable relationship between income levels and life expectancy This paper’s estimations produced firm evidence of

a consistently positive relationship until a value of approximately US$15,478 (using international price levels in 2005), corresponding to approximately 95 percent of the 4,325 sample observations GDP per capita alone is able to explain over 64 percent of the variation in life expectancy across countries and years Overall, this paper suggests that income levels are by far the strongest factor in raising life expectancy across the globe

(Maity, et al., 2017) analyzed the relationship between different variables and life expectancy across several countries This paper has studied the topic with a new approach by including uncommon independent variables such as GNI per capita (PPP), poverty headcount ratio at $1.00, among others However, the variables chosen are probably not the best measures of average life expectancy Although average life expectancy can be influenced by gender, genetics, lifestyle, etc and though these

variables might be correlated with some of the variables that were studied in this paper, since the studied variables do not necessarily have direct influence on average life expectancy The study concluded that further analysis should be done in order to study good health and well-being, including using a dependent variable such as infant mortality, which may be more easily affected by the dependent variables studied in this paper

This paper seeks to find the relationship between not only life expectancy and variables like GDP per capita, health expenditure and Gini Index but also with variables that have not been fully leveraged in research related to this topic such as poverty per headcount or cause of death by non-communicable diseases across different countries all around the world Based on the relationship found by previous studies, our hypothesis is that countries with higher levels of national income (as measured by GDP per capita) and spending on health expenditure will have higher life expectancies Citizens of countries that have a higher level of national income likely lead healthier lifestyles and have access to better healthcare infrastructure, both of which are contributing factors in life expectancy Additionally, countries with a booming population are more likely to have a higher life expectancy As examined in one study mentioned above, in countries with a large population, the nationals have higher life expectancy compared to other countries

CHAPTER II RESEARCH METHODOLOGY AND MODEL

BUILDING

1 Research methodology

1.1 Collecting data method

Cross-sectional data was collected across 200+ countries The data table contains 80

observations The data is synthesized from World Bank (2015)

1.2 Processing data method

Using Excel and Stata to process data and correlation matrix among variables

2 Model building

2.1 Identifying type of model

Model containing 7 variables:

- Dependent variable: Life expectancy at birth

· gini: Gini index (World Bank estimate)

· pov: Poverty headcount ratio at $1.90 a day (2011 PPP)

· healthexp: Current health expenditure

· ncd: Cause of death, by non-communicable diseases

Estimated means of variables:

● 𝛽1>0: GDP per capita increases, life expectancy at birth increases

● 𝛽2<0: Population decreases, life expectancy at birth increases

● 𝛽3<0: Gini index (World Bank estimate) decreases, life expectancy at birth increases

● 𝛽4<0: Poverty headcount ratio at $1.90 a day (2011 PPP) decreases, life expectancy at birth increases

● 𝛽5>0: Current health expenditure increases, life expectancy at birth increases

● 𝛽6>0: Cause of death, by non-communicable diseases increases, life expectancy

3.2 Data statistics description

Variable Observation Mean Standard deviation Min Max

Source: Run the sum command in STATA

Though most of the variables have a large number of observations, those that do not will reduce the sample size for the later regression models This is due to data being unavailable or unreported for a specific country at the time of the collection or during the period of observation (2015)

3.3 Correlation among variables in model

Correlation coefficient between variables is depicted in the table below:

life loggdpcap logpop heathexp ncd gini pov

life 1.0000

loggdpcap 0.8665 1.0000

Source: Run the corr command in STATA

a Correlation between dependent variable and independent variables

Analyzing the correlation, we can have the overall viewpoint of the dependent variable and independent variables based on database statistics, other than the mean assumption about sign based on the database is economic theory verified ahead

Positive correlation means that the increase in independent variables leads to the increase in dependent variable and vice versa When negative correlation happens, the increase in independent variables leads to the decrease in dependent variable and vice versa

In specification:

● r(life, loggdpcap) = 0.8665 The degree of correlation between these two

variables is relatively high Positive coefficient shows that life expectancy and GDP per capita have positive effect on each other, as expected in the initial estimated means

● r(life, logpop) = -0.0828 The degree of correlation between these two variables

is very low Negative coefficient shows that life expectancy and population have negative effect on each other, as expected in the initial estimated means

● r(life, healthexp) = 0.5906 The degree of correlation between these two

variables is quite high Positive coefficient shows that life expectancy and current health expenditure have positive effect on each other, as expected in the initial estimated means

● r(life, ncd) = 0.8267 The degree of correlation between these two variables is

relatively high Positive coefficient shows that life expectancy and cause of death, by non-communicable diseases, have positive effect on each other, as expected in the initial estimated means

● r(life, gini) = -0.4398 The degree of correlation between these two variables is

relatively low Negative coefficient shows that life expectancy and Gini index (World Bank estimate) have negative effect on each other, as expected in the initial estimated means

● r(life, pov) = -0.7093 The degree of correlation between these two variables is

relatively high Negative coefficient shows that life expectancy and poverty headcount ratio at $1.90 a day (2011 PPP) have negative effect on each other, as expected in the initial estimated means

b Correlation among independent variables

Moreover, from correlation coefficient matrix, we can see that the degree of correlation among independent variables is quite high with the highest being the correlation between cause of death, by non-communicable diseases, and poverty headcount ratio at $1.90 a day (2011 PPP) (r(ncd, pov) = -0.8009) and the lowest being the correlation between population and current health expenditure (r(logpop,healthexp)= -0.0199)

CHAPTER III CHECK FOR THE PROBLEMS OF THE

MODEL AND STATISTICAL INFERENCE

1 Model estimation

Model estimation result

Table 1 Initial estimation result

2 Testing and fix the defects of the model

2.1 Test the model's omitted variables (the correct form of the model)

- In the selection of variables to include in the model, many times the relevant variables will be omitted, which will lead to inaccurate estimation

- Carrying out Ramsey's RESET test using Stata, we get the following results:

Table 2 Ramsey's RESET Testing Result

Ramsey RESET test using powers of the fitted values of life

Ho: model has no omitted variables

F(3, 70) = 0.71

Prob > F = 0.5491

Source: Run the ovtest command in STATA

Hypothesis testing:

H0: Model has no omitted variables

H1: Model has omitted variables

With significance level α=5%

From the result we get, p-value = 0.5491 > α=5%

⇨ Accept H0

Conclusion: Model has no omitted variables