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

Life expectancy is the dependent variablewith GDP per capita, GNI per capita, and air pollution as the 3 main determinants.The results showed that both GDP per capita and GNI per capita

FOREIGN TRADE UNIVERSITY

FACULTY OF INTERNATIONAL ECONOMICS

-

-GROUP REPORT FACTORS AFFECTING HUMAN’S

LIFE EXPECTANCY

GROUP 2 Group’s members Student ID

Nguyễn Thu Trang 1814450068

Hanoi, September 2019

6.3 Rodolphe Desbordes, Céline Azémar (2008) 18

1.1 Method used to collect secondary data 20

2.1 Hypothesis testing for coefficient of regression 27

2.2 Hypothesis testing for validation of model 28

3.1 Open to trade and investment, enhance infrustructure 29

3.2 Promote Human capital and Physical capital 30

3.3 Air pollution enhancement strategy/orientation 30

3.4 More suggestions on raising GDP and GNI 31

In the context of fast paced globalization and industrialization, human’s lifeexpectancy is heavily affected by different factors Some which should be noted are:GDP per capita, GNI per capita, and air pollution In order to gain a betterunderstanding of the three factors’ influence of human expectancy, the team hasgathered data from 180 countries around the world in 2017 and estimated theregression model using the OLS method Life expectancy is the dependent variablewith GDP per capita, GNI per capita, and air pollution as the 3 main determinants.The results showed that both GDP per capita and GNI per capita have positiverelation with life expectancy, with the rise in GDP per capita and GNI per capitainfluence an increase in life’s predicted duration On the the other hand, airpollution has a negative impact on average longevity

In front of the rapid development of the world regrading both globalizationand industrialization , life expectancy has been increasingly of importance inmeasuring national development Researches have shown the relation betweenserveral factors and human longevity Among them, GDP per capita, GNI percapita, and air pollution have shown to have notable impact

A byproduct of industrialisation is environmental pollution The mostprominent is air pollution, which has adverse influence on human’s well being.Recently, the air pollution index has experienced a drastic rise Consequently, thenumber of patients with diseases of the respiratory system increases and lifelongevity changes The phenomenon reveals the strong connection between airpollution and life expectancy

Moreover, expectation of life is also dependent on the fluctuation of GDP percapita Healthcare is essential to everyone; however, the level of medicaltechnology is different from country to country; more often than not those withbetter technology are on the wealthy side Therefore, a nation’s prosperity can be asignificant factor in measuring life’s duration, making the relationship betweenGPD per capita and mortal expectancy worth looking into

Lastly, in the fast paced development of globalization, it is worth taking intoconsideration the impact of GNI per capita has on life’s duration In spite ofgorvenment’s effort in providing national healthcare system, the task proves to be atbest challenging, and at worst, impossible Therefore, different individuals possessdifferent capacities to afford healthcare based on personal income This createsdifferent mortality rate in different social classes It is clear that GDP per capitaplays a vital part in measuring human’s life expectancy

With a view to giving a better understanding, scruntinizing a specific case, thenlooking for appropriate solutions, we would like to tackle the topic “ Factorsaffecting human’s life expectancy in 2017” This report will evaluate the influence

of GDP per capita, air pollution levels, and GNI per capita of 179 random nationsaround the world on life’s expectancy Based on the evaluation, we will suggestfitting measures to make progress in practicing health care tasks

This essay includes the following content:

I Abstract

II Introduction

III Section I: overview of the topic

IV Section II: model specification

V Section III: estimated model and statistical inferences:VI.Conclusion:

VII Appendix:

VIII References:

IX.Individual assessment:

SECTION I: OVERVIEW OF THE TOPIC

Before getting into the affecting factors themselves, it is essential tounderstand the meaning of Human life expectancy as well as Human developmentindex (HDI) and the suitable method of calculating this proxy of human lifeexpectancy for the social condition of Vietnam and other countries

1 Life expectancy

* Definition

Life expectancy is a statistical measure of the average time an organism isexpected to live, based on the year of their birth, their current age and otherdemographic factors including sex

Classification – The difference and linkage between Lifespan, Life Expectancy and HDI

While the term “lifespan” refers to the maximum number of years an individual can live, life expectancy refers to an estimate or an average number of years a person can expect to live Most simply put, life expectancy can be attributed

to and impacted by an individual and their personal health history, genetics, andlifestyle, whereas lifespan holds for all living humans

HDI is the statistics of life expectancy, education, and income per capita

indicators It was developed by the Pakistani economist Mahbub ul Haq and firstpublished by the United Nations Development Programme (UNDP) in 1990 A

country’s HDI is supposedly higher when there are longer life expectancy, longer

education period and higher income per capita

* Measure

Over 25 years, there has been 2 main methods to calculate Human lifeexpectancy proposed by the UNDP The most commonly used measure of lifeexpectancy is at birth (LEB), which can be defined in two ways:

- Cohort LEB is the mean length of life of an actual birth cohort (all individualsborn a given year) and can be computed only for cohorts born many decades ago, sothat all their members have died

- Period LEB is the mean length of life of a hypothetical cohort assumed to be exposed, from birth through death, to the mortality rates observed at a given year.

* Situation

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 Global life expectancy at birth in

2015 was 71.4 years (73.8 years for females and 69.1 years for males), ranging from60.0 years in the WHO African Region to 76.8 years in the WHO European Region,giving a ratio of 1.3 between the two regions Women live longer than men allaround the world The gap in life expectancy between the sexes was 4.5 years in

1990 and had remained almost the same by 2015

Global average life expectancy increased by 5 years between 2000 and 2015,the fastest increase since the 1960s Those gains reverse declines during the 1990s,when life expectancy fell in Africa because of the AIDS epidemic, and in EasternEurope following the collapse of the Soviet Union The 2000-2015 increase wasgreatest in the WHO African Region, where life expectancy increased by 9.4 years

to 60 years, driven mainly by improvements in child survival, and expanded access

to antiretrovirals for treatment of HIV

2 Component

Life expectancy is affected by many factors such as:

- Socioeconomic status, including employment, income, education and economic wellbeing;

- The quality of the health system and the ability of people to access it

- Health behaviours such as tobacco and excessive alcohol consumption, poor nutrition and lack of exercise;

- Social factors; genetic factors; and environmental factors including overcrowded housing, lack of clean drinking water and adequate sanitation

This thesis narrows down the factors into the following 3 significants: GDP, GNI

and Air Pollutions.

Respectively were the three components of the human life expectancy: GDP,Air Pollution and Per capital income Argument for these selections was recentyears’ accumulating evidence: Rising incomes and personal well-being are linked inthe opposite way It seems that economic growth actually kills people

In a 2000 paper, Christopher Ruhm, an economics professor at the University ofVirginia, showed that when the American economy is on an upswing, people suffermore medical problems and die faster; when the economy falters, people tend to livelonger However, after 10 years, in the HDR 2010, a new list of indicators waspresented Those living in the country's most polluted counties could expect to live up

to one year longer if pollution met the WHO guideline Globally, the AQLI reveals thatparticulate pollution reduces average life expectancy by 1.8 years, making it thegreatest global threat to human health The counterpart for living standards was GrossNational Income (GNI) per capita instead of Gross Domestic Product (GDP) per capita

to diminish the effect of abroad income, international remittances and/or aid flow.Overall, the new indicators were much more refined and applicable to the then andcurrent situation of world as well as national and regional human development,Vietnam specifically, where globalization is occurring everyday with a tremendousamount of population working abroad and education has been a focus but actions areonly being effectively carried out in mainly urban areas

All of these components are calculated on the basis of the ratio between realvalue minuses minimum value over maximum value minuses minimum value Thevalues run on the scale from 0 to 1, with the minimum value being the required valuefor a country to merely not go into extinction Regarding the applicability of thedifferent approaches on Human life expectancy, it is most accurate and suitable for thispaper to base its output on GDP, Air Pollution and GNI, under the definition fromUNDP HDI 1995 and 2010, of each year calculated by the method applied at the time.

3 Characteristics

Due to its components and calculating methods, LEB holds some importantcharacteristics, both arithmetically and socially The LEB index combines theachievement on various aspects into only one number, which makes it a very broadproxy, despite not including political participation rate or gender inequality Thehigher the LEB index of a country, the further it is from the point of dying out, thehigher its chance and level of human well-being and development, and vice versa

By analyzing the Vietnam LEB index over the years, we are able to address therelative position of our country human life expectancy level comparing to economicgrowth level Moreover, according to the UNDP, LEB can be adapted at countrylevel as long as there are qualified statistics, and Vietnam is no exception Finally, acharacteristic of LEB that is in direct relation to our study in this paper is the linkwith income and income level, which GDP largely contributes to As income, in theform of GDP and later on, GNI, is a part of the LEB, there are supposedly a positiveassociation among them Indeed, some countries with very similar GDP or GNIhave entirely different LEB index, implying dissimilarity in human developmentlevel and rate, and in turn, in government policy

4 Role to the economy

Human life expectancy plays a significant role to one country economyscenario With LEB, we are offered an alternative to measure well-being besides andbeyond wealth, which are more easily estimated by income From LEB indicators, thecore issues of a country such as healthcare, education and living standard are

raised and conveniently compared between countries, providing great insights to thereasons, effects and consequences of government policy A developing country likeVietnam can utilize the results and use it as guidance for the authority on the path ofmaking suitable policy to assist social well-being improvement as a whole.

5 Theoretical framework

5.1 GDP

* Definition of GDP and GDP per capita

GDP is the monetary or market value of the goods and services producedwithin the geographic boundaries of a country during a specified period of time,normally a year

GDP per capita is a measure of a country's economic output that accounts for itsnational population It divides the country's GDP by its total population

The graph shows that life expectancy increases in accordance with the nationalGDP per capita, however this is not the case for many countries For example, Cubahad the life expectancy of 80 years with a GDP of approximately 9,500$, while theexpectancy of China was only 76 years with a GDP much higher than 10000$.

This could be contributed to the consumption of both needs and wants ofpeople They consume needs to survive Once their needs are satisfied, they willspend the rest of their life on luxuries and other wants This makes the increase ofGDP uninfluential to the life expectancy

Different mortality gaps between social classes can also be a reason If thegaps are too wide, it will not matter if the GDP is high, national life expectancy canstill be low due to high mortality rates of the lower class

Finally, the GDP per capita does not reflect non-market economic activity.For example, if there is a lot of subsistence farming, people could be working andhave enough food to eat, but wouldn’t be contributing much to the nation’s GDPbecause they wouldn’t be buying the food they eat, or selling the food they grow i.e

no exchange of money It also does not reflect the income received from foreignmarket, therefore the data could be different across countries

The correlation between GDP per capita and life expectancy can be illustrated bythe Preston curve

5.2 Pollution

* Definition of pollution

Pollution, also called environmental pollution, is the addition of anysubstance (solid, liquid, or gas) or any form of energy (such as heat, sound, orradioactivity) to the environment at a rate faster than it can be dispersed, diluted,decomposed, recycled, or stored in some harmless form Pollution can be classifiedinto different categories based on its cause:

- Pollution caused by solid wastes

- Pollution caused by liquid wastes

- Pollution caused by gaseous wastes

- Pollution caused by wastes without weights

* Definition of air pollution:

Air pollution is pollution caused by gaseous wastes, such as Carbonmonoxide (CO), Sulphur dioxide (SO2), Nitrogen dioxide (NO2), Ozone (O3) andsmog gases It may cause respiratory diseases in various degrees of severity inhumans, with the highest level is death; it can also adversely impact other livingorganism Due to the different contribution of density of gaseous wastes, airpollution is often more severe in the urban areas

* The situation of air pollution

Air pollution is one of the reasons for a number of health conditions likerespiratory infections, heart disease, stroke and lung cancer In many cases, airpollution exacerbates pre-existing cardiorespiratory illnesses—individuals sufferingfrom asthma, for example, are particularly vulnerable While it is hard to pinpointexactly the number premature deaths or diseases caused by air pollution, it is clearthat the detriments brought about by contaminated air is worsening through time

By 2017, the estimated pollution caused deaths were estimated to be 2,9 millions,doubled the number in 1990, which was 1,68 millions

(source: https://ourworldindata.org/air-pollution#death-rates-from-air-pollution)

The cause of such phenomenon could be attributed to the rapid economic growth indeveloping countries, like China and India, which resulted in the boom of industrialwastes.

* Effects of air pollution on life expectancy

Research has shown air pollution reduces life expectancy by approximately ayear Contaminated air has particulate matter (PM) smaller than 2.5 microns Thesefine particles can enter deep into the lungs, and breathing PM2.5 is associated withincreased risk of heart attacks, strokes, respiratory diseases and cancer PM2.5pollution comes from power plants, cars and trucks, fires, agriculture and industrialemissions

Upper panel A: How air pollution shortens human life expectancy around theworld Lower panel B: Gains in life expectancy that could be reached by meetingworld health organization guidelines for air quality around the world.

GNI per capita based on purchasing power parity (PPP) GNI per capita is ameasurement of income divided by the number of people in the country Itcompares the GNI of countries with different population sizes and standards ofliving PPP GNI is converted to international dollars using purchasing power parityrates An international dollar has the same purchasing power over GNI as a U.S.dollar has in the United States GNI is the sum of value added by all residentproducers plus any product taxes (less subsidies) not included in the valuation ofoutput plus net receipts of primary income (compensation of employees andproperty income) from abroad It also does not include the shadow or blackeconomy Data are in current international dollars based on the 2011 ICP round.

* The effect of GNP per capita, PPP on life expectancy:

As of GDP, the correlation between GNI per capita and life expectancy can

be demonstrated by the Preston Curve, with similarities in overall trends

However, as the GNI takes into account the money flow from foreignmarkets, as well as using the income rather than the output value a market, itsrelationship with life expectancy can differ from that of the GDP It should be notedthat, as the GNI also includes the income from foreign sources, it reveals a moreinclusive picture of the income inequality and other factors that could come to play

in assessing human life expectancy

The relationship between life expectancy and GNI per capita is strongenough to be the basis of a regression model Simple functions that increase at adecreasing rate include multiplicative (hyperbolas) and logarithmic functions

6 Related research

6.1 Samuel H Preston

The most common theory related to the correlation between economic growthand life expectancy is the study of Samuel H Preston, in which he introduced thePreston Curve The Preston curve indicates that individuals born in richer countries, onaverage, can expect to live longer than those born in poor countries However, the linkbetween income and life expectancy flattens out This means that at low levels

of per capita income, further increases in income are associated with large gains inlife expectancy, but at high levels of income, increased income has little associatedchange in life expectancy In other words, if the relationship is interpreted as beingcausal, then there are diminishing returns to income in terms of life expectancy.

In the article “Economic Growth and Life Expectancy – Do Wealthier CountriesLive Longer?”, author Audre Biciunaite also points out that the income-life expectancyrelationship can be inverted This is due to the definition of developed countries whichmakes assumptions that do not allow objective measurement

Regarding the effect of air pollution on life expectancy, Joshua S Apte with

“Ambient PM2.5 Reduces Global and Regional Life Expectancy” has pointed outthe adverse impact of air pollution on health, more specifically the reduction ofsurvival The researchers looked at outdoor air pollution from particulate matter(PM) smaller than 2.5 microns The data was collected from the Global Burden ofDisease Study to measure PM2.5 air pollution exposure and its consequences in 185countries Researchers then quantified the national impact on life expectancy foreach individual country as well as on a global scale

6.2 Erdal Demirhan, Mahmut Masca (2008)

In the paper “Determinants of foreign direct investment flows to developingcountries: a cross-sectional analysis”, Erdal Demirhan and Mahmut Masca exploredseven determinants of FDI with a cross-country data of 38 developing countries inthe five-year period from 2000 to 2004 One of those determinants that is directlyrelated to LEB is GDP per capita, the growth rate of which is used in the research asthe proxy for market size

Prior to building their own model, the authors mentioned the findings from afew existing studies on the topic Market size, measured by GDP per capita appears

to be the most robust FDI determinant in econometrics studies (Artige and Nicolini,2005) The idea is also supported and further explained by Jordaan (2004), who saidthat FDI tend to flow into economies with larger and expanding markets, translatinginto greater purchasing power or higher GDP per capita, the markets from which

firms have a higher chance to earn better returns from invested capital and thusincrease profit, life expectation.

6.3 Rodolphe Desbordes, Céline Azémar (2008)

In research “Public Governance, Health and Foreign Direct Investment inSub-Sahara Africa”, the two authors Desbordes and Azémar examined the effect ofhuman health on GDP by two ways First, by accounting for the Foreigndevelopment index (FDI) deficit in Sub-Sahara Africa regions, they found out thatdue to low human accumulation, especially health, 110-140% of FDI gap betweenSSA’s regions was explained Second, they also developed a relationship betweeninfectious diseases on the reduction of GDP The result was that 1% increase in HIVprevalence in the adult population is responsible for the decrease of GDP due to3.5% down in net FDI inflow

After comparing percentage change in GDP and FDI between differentregions and continentals in the world, they saw that FDI inflow in median countries

in SSA was less than 1% of their GDP, thus claiming that poor public governancewas responsible for this trend With the lack in domestic-oriented and external-oriented policies, SSA’s governments could not regulate the economic growthwhich then resulted in low GDP and FDI inflow However, this paper also showedthat SSA’s GDP deficit was mostly explained by market size and insufficient publicgoods provisions, especially health care services Additionally, a remarkable pointthat the authors offered was that once HIV and malaria did not exist, the net inflow

of FDI every year in the median SSA country would have been predicted to be third higher in the period 2000-2004

one-At the end of the research, they explained the results by several reasons First, aspoor health conditions raised the production labor cost and labor compensation as well

as reduced productivity, it might trigger a drop in staff morale which caused lowproductivity Furthermore, high cost of recruiting and training new skilled workerswould only create more burden to the weak economy Finally, it is possible that

foreign investors, on the purpose of seeking for efficiency or market, tended to avoid countries in which people died because of infectious and dangerous diseases.

7 Research question:

Accordingly, this research aims to fulfil the objectives in the followings:

 First, to determine the deciding factors that affect on human life expectancy

 Second, to explain the influence of GDP per capita, GNI per capita, and airpollution on human life expectancy

 Third, based on the above analysis, to propose the combat methods to

the negative impact that air pollution can possibly have on human life

expectancy, while enhancing the advantages in the relationship between GDP per capita, and GNI per capita with life expectancy

In order to achieve these objectives, the study will answer the following questions:

 First: What is human life expectancy and how is it affected, measured on ageneral basis?

(Overview, Part 1 – 4)

 Second: What are the two-way linkage between human life expectancy and 3factors: GDP per capita, GNI per capita, and air pollution?

(Theoretical Framework and Public Research)

 Third: How effective are estimated models and statistical inferences to thehypothesis

(Hypothesis Testing)

 Fourth: What are the specific recommendation for improving human

life expectancy? (by altering the GDP per capita, GNI per capita, and airpollution)

(Recommendation)

19

SECTION II: MODEL SPECIFICATION

1 Methodology of the study

1.1 Method used to collect secondary data

The team collected sample and estimated values based on data from 180observations in 2015 from 180 countries worldwide For quantitative results, thenumber of outputs should be equal to the number of inputs, which is the datacollected by the statistical method

1.2 Method used to analyze the data

By using OLS method, data is selected and checked the statistical significance

of the regression coefficients and the suitability of the model based on the observedobservations comparing with the previous research and similar studies, to find thebest results 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 STATAsoftware, Microsoft Excel, Microsoft Word for synthesis and completion of thisreport

2 Theoretical model specification

In order to construct an econometric model, it is first necessary to identify thefactors that are involved in the interaction and description of economic variables Inorder to obtain the results of the computation and analysis of the output, the statisticalmethod used in the two fields is the estimation and verification of the hypothesis.Thus, in order to analyze the factors influencing the human’s life expectancy,the group used the regression analysis model to show the trend of variable in terms

of the average of the sample With the specimen, the regression function is afunction with specific numerical, computational, and differential values such asderivatives, differential and direct meaning analysis

Base on given theoretical framework as well as previous research, the team didbuild this following model to analyze the effect of some factors to Human’s LifeExpectancy.

LE = f(GPC, AP, GIC)

In which:

 LE : Life Expectancy at Birth (year)

 GPC : Gross domestic product per capita (USD)

 AP : Air polution (µg/m3)

 GIC: Gross national income per capita (USD)

To determine the influence of 3 given factors on human’s life expectancy, from the theory presented above, the team proposed the following research model:

Population Regression model:

Explain the variables

regression coefficient

µg/m3

-(annual exposure) per cubic

meter)

In which:

- Dependent variable: LE

- Independent variables: GPC, AP and GIC

2.1 Describe the data

(Std.dev.) as well as the maximum value (Max) and the minimum value (Min) of all

the given variables

From the result above:

 The standard deviation of variable LE is 8.035814, a high standard

deviation, which means the difference in life expectancy across countries

is relatively high Rich countries, developed countries often have a high

average life expectancy (over 80 years), mainly in the Americas and

Europe, while those in Asia and Africa are developing countries, with the

average longevity of usually around 60 to 70 years

 The standard deviation of variable GPC is 17554.63 This is also a high

standard deviation, which shows that the gap in average income between

various countries worldwide is quite large It is totally understandable

because there is a marked difference in the level of economic

development among nations GDP per capita income of the Americas or

Europe is often much higher than that of Asian or African countries

 The mean value of 28.34444 indicates that the level of pollution is mild

(the safe level is 25) and the standard deviation is 19.77875 Countries

with severe levels of pollution are often poor, developing countries in

Asia and Africa (For example: Qatar: 107, Saudi Arabia: 106, India: 74,

Kuwait: 67), whereas in developed countries in Europe and America,

pollution levels are very low (For example: Australia: 6, USA:

8,Denmark: 11, UK: 12, Sweden: 13)