tiểu luận kinh tế lượng FACTORS AFFECTING HUMAN’S LIFE EXPECTANCY

In the process of searching for documents, we have pointed out some prominent factors that affected life expentacy which should be noted are: GDP per capita, GNI per capita, HE - Current

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS

GROUP REPORT

FACTORS AFFECTING HUMAN’S

LIFE EXPECTANCY

Hoàng Vân Anh 1815520150Nguyễn Hoàng Bách 1815520156

Vũ Minh Ngọc 1815520211

Class: KTEE218 (1-1920).1_LT

Course: Econometrics 1 Lecturer: Dr Tu Thuy Anh

Dr Chu Thi Mai Phuong Hanoi, October 2019

4.2 Correlation between dependent variable and each independent variables 14

4.4.1 Testing an individual regression coefficient 18

Life expectancy at birth reflects the overall mortality level of a population Itsummarizes the mortality pattern that prevails across all age groups in a given year –children and adolescents, adults and the elderly According to World HealthOrganization, global life expectancy at birth in 2016 was 72.0 years (74.2 years forfemales and 69.8 years for males), ranging from 61.2 years in the WHO African Region

to 77.5 years in the WHO European Region, giving a ratio of 1.3 between the tworegions.So what has affected life expectancy in the world? As economics studentsinterested in social matters, we decided to do research on the topic "Factors affectinghuman’s life expectancy ”

In the process of searching for documents, we have pointed out some prominent factors that affected life expentacy which should be noted are: GDP per capita, GNI per capita, HE - Current Health expenditure (% GDP) and air pollution In order to gain a better understanding of the 4 factors’ influence of human expectancy, the team has

gathered data from 207 countries around the world in 2016 and estimated the regression model using the OLS method Life expectancy is the dependent variable with GDP per capita, GNI per capita, HE - Current Health expenditure (% GDP) and air pollution as the

4 main determinants The results showed that all GDP per capita, GNI per capita and

HE - Current Health expenditure (% GDP) have positive relation with life expectancy, with the rise in GDP per capita, GNI per capita and HE - Current Health expenditure (% GDP) influence an increase in life’s predicted duration On the the other hand, air

pollution has a negative impact on average longevity

1 INTRODUCTION

Econometrics is the meaningful study of the social sciences in which the tools ofeconomic theory, mathematics and statistical speculation are applied to analyze economicproblems Econometrics uses the mathematical statistics methods to find out the essence

of statistics, make conclusions about the collected statistics that can make predictionsabout economic phenomenon

Since its inception, econometrics has provided economists with a sharpinstrument for measuring economic relations As economics students, we recognize theimportance of studying about Econometrics in logical and problem analysis To betterunderstand how to put the Econometrics into reality and to apply the Econometricseffectively and correctly, given the data set, our group, which includes three members:Nguyen Hoang Bach, Hoang Van Anh, and Vu Minh Ngoc, follows the methodology ofeconometrics to analyze the data Noted that because of the lack of information on thedata set, all inferences of abbreviations and others are based on assumptions and self-research As a result, we hope to have shown clearly our logic and reasoning of analysis

To the extent of purpose and resources, there are still deficiencies in this report,but we look forward to providing readers with a decent view of the overall of the data setgiven and the knowledge that we have gained through Dr Tu Thuy Anh’s Econometricscourse

2 LECTURE REVIEW

2.1 Related research

Understanding the methods to life expectancy is something that people have done since ancient times When science was not yet developed, people expected the mysterious medicine even the very colorful idealistic activities to dream of immortality and eternity Nowadays, as science and technology are growing, people are increasingly expanding their understanding of the world, explaining more natural and social

phenomena, and the issue of longevity is also analyzed and explained more and more realistically, gradually moving away from the spiritual and mystical elements

Through publications, scientific research, we also see many authors mention issues related to human life such as: the secret to improving longevity? What makes people quickly aging? Conclusions and recommendations of these publications mainly revolve around issues of genetics, diet, rest, work, entertainment of humans Such

explanations are far too simple, missing many important factors Some other studies havealso mentioned macro variables at a higher level such as education level, public service, average income, etc., but the data are not complete or no longer new to explain better for the problem of the current world

We have consulted a lot of life expectancy studies in history and here are some related research :

+ Bergh and Nilsson (2009) analyzed the relation between three dimensions of globalization (economic, social and political) and life expectancy using a panel of 92 countries over the period 1970-2005 They found a very robust positive effect from economic globalization on life expectancy, even when controlling for income, nutritional intake, literacy, number of physicians and several other factors

+ Mariani et al (2008) determined the relationship between life expectancy and environmental quality dynamics The results showed environmental conditions affected the life expectancy.

+ Yavari and Mehrnoosh (2006) analyzed the effects of socio- economic factors

on life expectancy using multiple regression analysis This study showed that there is a positive, strong correlation between life expectancy as an independent variable and per capita income, health expenditures, literacy rate and daily calorie intake Also, it revealedthat there is a negative strong correlation between life expectancy and the number of people per doctor in African countries

+ Leung and Wang (2003) investigated the relationship between health care, life expectancy and output using a modified neoclassical growth model They showed incomeand economic development factors have positive impacts on lifetime

Summing up, the review of presented studies shows that the determinants of life expectancy can be divided into the economic, social and environmental factors

Accordingly, in this study, the impacts of these factors on life expectancy are estimated tofollow the existing literature

2.2 Research orientation

2.2.1 Dependent variable

Dependent variable is expected life expectancy at Birth (LEB) have a difference from the Life Average data If the average life expectancy calculation is calculated to estimate the average age of the deaths at a given time, the expected life expectancy at birth is the estimated life expectancy for a child at birth at a specific time provided that the factors that influence the life expectancy in the future do not change compared to the time of birth, so the expected life expectancy at birth is the result of the whole process from the past to the present from the factors that have relevant, the level of impact of

each factor can change over the period of time When analyzing life expectancy at birth,

we will be able to more accurately assess the impact of related factors at a given time

2.2.2 Independent variables

From the studies I also decided to choose variables to analyze their influence onhuman life expectancy The four factors are: Air pollution (µg/m3); GNIpc: Grossnational income per capita (USD); HE : Current health expenditure (USD); People using

at least basic drinking water services (% of population)

a/ Air pollution

A study published in the US National Library of Medicine National Institutes ofHealth has shown an influence from air quality on human life from 2000 to 2007 in theUnited States

Table 2 summarizes estimated regression coefficients for the association betweenchanges in PM2.5 and changes in life expectancy for 545 counties for 2000 to 2007 for

selected regression models When controlling for changes in all available socioeconomicand demographic variables as well as smoking prevalence proxy variables (Model 3), a

10 µg/m3 decrease in PM2.5 was associated with an estimated mean increase in lifeexpectancy of 0.35 years (SE= 0.16 years, p = 0.033) The estimated effect of PM2.5 onlife expectancy was consistent across models adjusting for various patterns of potentiallyconfounding variables (e.g Models 2 – 4) Models 5 – 9 of Table 2 show the results forselect stratified and weighted regressions In counties with a population density greaterthan 200 people per square mile, a 10 µg/m3 decrease in PM2.5 was associated with anincreased life expectancy of 0.72 (0.22 years, p< 0.01) (Model 6), compared with −0.31years (0.22 years, p = 0.165) in counties with less than 200 people per square mile (Pdifference <0.01) In counties whose proportion of urban residences was greater than 90percent, a 10 µg/m3 decrease in PM2.5 was associated with an increased life expectancy of0.95 (0.31, p< 0.01)(Model 7), compared with −0.16 (0.16 years, p = 0.299) in countieswith less than 90% urban residences (P difference < 0.01)

b/ Gross national income per capita

Higher income per capita (IPC) means better access to public and private healthservices those are provided by public or private sectors in a country Good health servicewhich lowers mortality rates in a country promotes to reach a long living population levelwith a higher life expectancy at birth (LEB) and healthy labour force enhancingproductivity People feel themselves more productive with a good health care henceincreasing productivity and working hours will cause an increase in IPC (economicgrowth) incessantly In traditional economic growth theory, labour force which is one ofthe factors of production function has got an important effect on the country’s economicgrowth This study aims to investigate the relationship between LEB and IPC data andvice versa for 56 developing countries in North Africa, Middle-East and South-East Asiawhere most of them are Islamic countries and members of The Organisation of IslamicCooperation (OIC)

According to the random and fixed effects estimation models with panel dataanalysis and cross-section data analysis in the study, LEB is found as one of thedeterminants of IPC and IPC as a main determinant of LEB in 56 developing countries.Granger causality test is also applied to test the direction of causality between LEB andnational IPC for 56 developing countries and it is seen that IPC Granger causes LEBincrease and vice versa for panel data For cross-section data analysis there is no provedcorrelation between two variables.

LEB and IPC relationship in 56 developing countries (2015)The IMF Data Available: https://www.imf.org/en/data Accessed: 12 October 2018

c/ Current health expenditure

The graph below shows the relationship between what a country spends on healthper person and life expectancy in that country between 1970 and 2015 for a number of rich countries

The US stands out as an outlier: it spends far more on health than any other country, yet the life expectancy of the American population is not longer, but actually shorter than in other countries that spend far less

If we look at the time trend for each country, we first notice that all countries have followed an upward trajectory—the population lives increasingly long lives as health expenditure increases But again, the US stands out by following a much flatter trajectory: gains in life expectancy from additional health spending in the U.S are much smaller than in the other high-income countries, particularly since the mid-1980s.

This development has led to a large inequality between the US and other rich countries In the US health spending per capita is often more than three times higher than in other rich countries, yet the populations of countries with much lower health spending than the US enjoy considerably longer lives In the most extreme case, we see that Americans spend more than 5-times what Chileans spend, yet the population of Chileactually lives longer than Americans

d/ People using at least basic drinking water services

Infants and young children are the innocent victims of the worldwide failure tomake safe drinking water and basic sanitation services available to impoverished people(see Figure 4).Their families’ poverty, lack of basic services and the resulting filthyliving environment mean that children under 5 years of age in particular are exposed to amultitude of health threats, without the physical or economic means to combat them

Malnutrition – particularly protein-energy malnutrition – stunts growth, impairs cognitivedevelopment and, crucially, lowers the children’s resistance to a wide range of infections,including the water-related diarrhoeal diseases and malaria (see Figure 5) In developingcountries, over 90% of all diarrhoeal deaths occur in children under 5 years of age (seeFigure 3) In sub-Saharan Africa alone, some 769 000 children under 5 years of age diedannually from diarrhoeal diseases in 2000–2003.That is more than 2000 children’s liveslost every day, in a region where just 36% of the population have access to hygienicmeans of sanitation South Asia has a similarly low sanitation coverage.There too childmortality is very high Some 683 000 children under 5 years of age die each year fromdiarrhoeal disease Compare that with the developed regions, where most mothers andbabies benefit from safe drinking water in quantities that make hygiene behaviour easy,have access to safe, private sanitation, adequate nutrition, and many other prerequisites tohealth Of the 57 million children under 5 years old in the developed regions, about 700succumbed annually to diarrhoeal disease (according to statistics for 2000–2003).Thatmeans that the sub-Saharan baby has almost 520 times the chance of dying fromdiarrhoea compared with a baby born in Europe or the United States of America.

3 Methodology of the study

3.1 Method used to collect secondary data

The team collected sample and estimated values based on data from 215

observations in 2016 from 215 countries worldwide For quantitative results, the number

of outputs should be equal to the number of inputs, which is the data collected by the statistical method

3.2 Method used to analyze the data

By using OLS method, data is selected and checked the statistical significance ofthe regression coefficients and the suitability of the model based on the observedobservations comparing with the previous research and similar studies, to find the bestresults to use for analysis

During the course of the project, the team used the knowledge of econometricsand macroeconomics, quantitative methods with the main support of GRETL software,Microsoft Excel, Microsoft Word for synthesis and completion of this report

3.3 Econometrics model

Based on developed economic theory and practical experience, we have

identified the expectation of independent variables affecting the expected life expectancy

at birth of the following:

LEB = f(AP; GNI; HE; PW)