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

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS -  - GROUP REPORT FACTORS AFFECTING HUMAN’S LIFE EXPECTANCY GROUP Group’s members Student ID Nguyễn Thu Trang 1814450068 Nguyễn Thị Diệu Ly 1814450052 Lường Thục Uyên 1810450007 Nữ Hoài Linh 1311140052 Class: KTEE218 (1-1920).1_LT Course: Econometrics Lecturer: MSc Quynh Thuy Nguyen Hanoi, September 2019 INDEX ABSTRACT INTRODUCTION SECTION I: OVERVIEW OF THE TOPIC Life expectancy Component Characteristics 10 Role to the economy 10 Theoretical framework 11 5.1 GDP 11 5.2 Pollution 12 5.3 GNI per capita PPP 15 Related research 16 6.1 Samuel H Preston 16 6.2 Erdal Demirhan, Mahmut Masca (2008) 17 6.3 Rodolphe Desbordes, Céline Azémar (2008) 18 Research question: 19 SECTION II: MODEL SPECIFICATION 20 Methodology of the study 20 1.1 Method used to collect secondary data 20 1.2 Method used to analyze the data 20 Theoretical model specification 20 2.1 Describe the data 22 Estimate model 1.1 25 Sample regression model 25 Hypothesis Testing 27 2.1 Hypothesis testing for coefficient of regression 27 2.2 Hypothesis testing for validation of model 28 Solution and Recommendation 29 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 CONCLUSION 34 APPENDIX 35 INDIVIDUALS ASSESSMENT 47 ABSTRACT In the context of fast paced globalization and industrialization, human’s life expectancy 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 better understanding of the three factors’ influence of human expectancy, the team has gathered data from 180 countries around the world in 2017 and estimated the regression model using the OLS method Life expectancy is the dependent variable with GDP per capita, GNI per capita, and air pollution as the main determinants The results showed that both GDP per capita and GNI per capita have positive relation with life expectancy, with the rise in GDP per capita and GNI per capita influence an increase in life’s predicted duration On the the other hand, air pollution has a negative impact on average longevity INTRODUCTION In front of the rapid development of the world regrading both globalization and industrialization , life expectancy has been increasingly of importance in measuring national development Researches have shown the relation between serveral factors and human longevity Among them, GDP per capita, GNI per capita, and air pollution have shown to have notable impact A byproduct of industrialisation is environmental pollution The most prominent is air pollution, which has adverse influence on human’s well being Recently, the air pollution index has experienced a drastic rise Consequently, the number of patients with diseases of the respiratory system increases and life longevity changes The phenomenon reveals the strong connection between air pollution and life expectancy Moreover, expectation of life is also dependent on the fluctuation of GDP per capita Healthcare is essential to everyone; however, the level of medical technology is different from country to country; more often than not those with better technology are on the wealthy side Therefore, a nation’s prosperity can be a significant factor in measuring life’s duration, making the relationship between GPD per capita and mortal expectancy worth looking into Lastly, in the fast paced development of globalization, it is worth taking into consideration the impact of GNI per capita has on life’s duration In spite of gorvenment’s effort in providing national healthcare system, the task proves to be at best challenging, and at worst, impossible Therefore, different individuals possess different capacities to afford healthcare based on personal income This creates different mortality rate in different social classes It is clear that GDP per capita plays a vital part in measuring human’s life expectancy With a view to giving a better understanding, scruntinizing a specific case, then looking for appropriate solutions, we would like to tackle the topic “ Factors affecting 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 nations around the world on life’s expectancy Based on the evaluation, we will suggest fitting 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 to understand the meaning of Human life expectancy as well as Human development index (HDI) and the suitable method of calculating this proxy of human life expectancy for the social condition of Vietnam and other countries Life expectancy * Definition Life expectancy is a statistical measure of the average time an organism is expected to live, based on the year of their birth, their current age and other demographic 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, and lifestyle, 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 first published 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 main methods to calculate Human life expectancy proposed by the UNDP The most commonly used measure of life expectancy 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 individuals born a given year) and can be computed only for cohorts born many decades ago, so that 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 It summarizes 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 from 60.0 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 all around 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 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 Eastern Europe following the collapse of the Soviet Union The 2000-2015 increase was greatest 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 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 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 recent years’ accumulating evidence: Rising incomes and personal well-being are linked in the opposite way It seems that economic growth actually kills people In a 2000 paper, Christopher Ruhm, an economics professor at the University of Virginia, showed that when the American economy is on an upswing, people suffer more medical problems and die faster; when the economy falters, people tend to live longer However, after 10 years, in the HDR 2010, a new list of indicators was presented 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 that particulate pollution reduces average life expectancy by 1.8 years, making it the greatest global threat to human health The counterpart for living standards was Gross National 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 and current situation of world as well as national and regional human development, Vietnam specifically, where globalization is occurring everyday with a tremendous amount of population working abroad and education has been a focus but actions are only being effectively carried out in mainly urban areas All of these components are calculated on the basis of the ratio between real value minuses minimum value over maximum value minuses minimum value The values run on the scale from to 1, with the minimum value being the required value for a country to merely not go into extinction Regarding the applicability of the different approaches on Human life expectancy, it is most accurate and suitable for this paper to base its output on GDP, Air Pollution and GNI, under the definition from UNDP HDI 1995 and 2010, of each year calculated by the method applied at the time Characteristics Due to its components and calculating methods, LEB holds some important characteristics, both arithmetically and socially The LEB index combines the achievement on various aspects into only one number, which makes it a very broad proxy, despite not including political participation rate or gender inequality The higher the LEB index of a country, the further it is from the point of dying out, the higher 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 the relative position of our country human life expectancy level comparing to economic growth level Moreover, according to the UNDP, LEB can be adapted at country level as long as there are qualified statistics, and Vietnam is no exception Finally, a characteristic of LEB that is in direct relation to our study in this paper is the link with income and income level, which GDP largely contributes to As income, in the form of GDP and later on, GNI, is a part of the LEB, there are supposedly a positive association among them Indeed, some countries with very similar GDP or GNI have entirely different LEB index, implying dissimilarity in human development level and rate, and in turn, in government policy Role to the economy Human life expectancy plays a significant role to one country economy scenario With LEB, we are offered an alternative to measure well-being besides and beyond wealth, which are more easily estimated by income From LEB indicators, the core issues of a country such as healthcare, education and living standard are 10 orders fighter planes for use in the military, defense and aeronautical contractors receive money for their work, increasing GDP Every plane built by a contractor is a product added to national economic output All in all, if the capital inflows are sustainably matched with human development as well as human life expectancy and those domestic conditions are improved, it is expected that Vietnam economy will sooner join regional developed economies in the next few years 33 CONCLUSION Under the contribution of all group members, this report is completely using the knowledge we earned from the study and research into (Basic) Econometrics I Working on this topic, especially practicing with STATA, helps us understand better about the course, such as the related economic theories and the process of analyzing, verifying, testing the econometric model and the relationship between variables By apply what we learned and through our own research and analysis, we can reach some practical conclusion about socio-economic phenomena The team has completed the model “Factors affecting human’s life expectancy in 2017” The research is concerned with the effect of 03 features including Gross domestic product per capita (GDP), air pollution (AP) and Gross national income per capita (GIC) on longevity of people in various countries in 2017 Based on the estimation of the model, we can aware the significance of each independence variables and set up the correlation between variables, then, draw some conclusion about the dependence of human’s longevity on three former regressors In addition, other categories which influence life expectancy though aren’t included in the report such as demographic, genetics, ethnicity status, health care resource, … should be needed to consider in order to conclude a more exact result Last but not least, we would like to thank MSc Thuy Quynh Nguyen for giving useful lessons about the econometrics and enthusiastic guidance for our report However, since we are still lack of specialistic knowledge as well as skills, this analysis could encounter some mistakes We would like to receive your comments to master our group work 34 APPENDIX Data table No Countries GPC AP GIC LE Afghanistan 1084 48 1900 63 Albania 5,954.00 18 11800 78 Algeria 4,132.80 36 14320 76 Angola 3,695.80 36 5740 61 Antigua and Barbuda 13,566.90 14 21660 76 Argentina 13,467.10 13 20170 76 Armenia 3,609.70 26 9090 74 Australia 56,554.00 45320 82 Austria 43,665.00 17 49390 82 10 Azerbaijan 5,500.30 30 17290 72 11 Bahamas, The 22,888.10 14 30750 75 12 Bahrain 22,688.90 55 44300 77 13 Bangladesh 1,210.20 89 3680 72 14 Barbados 15,557.80 15 16700 76 15 Belarus 5,949.10 20 17590 74 16 Belgium 40,356.90 16 45330 81 17 Belize 4,850.00 27 8060 70 18 Benin 783.9 35 2110 61 19 Bhutan 2,613.60 56 8330 70 20 Bolivia 3,077.00 28 6650 69 21 Bosnia and Herzegovina 4,574.10 47 12100 77 22 Botswana 6,532.10 18 16470 66 23 Brazil 8,757.20 11 15470 75 24 Brunei Darussalam 30,967.90 84210 77 25 Bulgaria 6,993.50 28 17820 74 26 Burkina Faso 615.6 40 1650 60 27 Burundi 303.7 46 760 57 35 28 Cabo Verde 2,954.10 40 6180 72 29 Cambodia 1,163.20 29 3300 68 30 Cameroon 1,244.40 66 3390 58 31 Canada 43,315.70 43990 82 32 Central African Republic 348.4 46 750 51 33 Chad 777.2 46 2130 53 34 Chile 13,653.20 21 22010 79 35 China 8,069.20 58 14400 76 36 Colombia 6,044.50 18 13810 74 37 Comoros 727.6 17 2670 63 38 Congo, Dem Rep 474.9 46 800 59 39 Congo, Rep 1,712.10 53 6030 64 40 Costa Rica 11,406.40 20 14920 80 41 Cote d'Ivoire 1,420.60 24 3340 53 42 Croatia 11,579.70 22 22860 77 43 Cuba 7,602.30 18 5678 80 44 Cyprus 23,075.10 18 31980 80 45 Czech Republic 11,556.90 21 31420 79 46 Denmark 53,014.60 11 50560 81 47 Djibouti 1,862.20 52 2456 62 48 Dominican Republic 6,468.50 20 14020 74 49 Ecuador 5,547.70 13 11230 76 50 Egypt, Arab Rep 4,127.10 105 10750 71 51 El Salvador 10,347.30 37 7110 73 52 Equatorial Guinea 7,074.90 47 5980 58 53 Estonia 13645.5 28570 77 54 Ethiopia 4,921.90 36 1620 65 55 Fiji 42,405.40 8960 70 56 Finland 36,526.80 42640 81 36 57 France 27,389.00 12 41720 83 58 Gabon 9474.7 40 16340 66 59 Gambia, The 3,764.60 61 1540 61 60 Georgia 3,764.60 20 9350 73 61 Germany 41,176.90 14 49010 81 62 Ghana 1,361.10 23 3990 62 63 Greece 18,007.80 13 26940 82 64 Grenada 9,212.20 15 11760 73 65 Guam 35,439.50 30987 79 66 Guatemala 3,923.60 35 7620 73 67 Guinea 554 23 1930 59 68 Guinea-Bissau 596.9 33 1610 57 69 Guyana 4,136.70 17 7510 67 70 Haiti 814.5 26 2770 63 71 Honduras 2,326.20 38 4230 73 72 Hungary 12,365.60 23 25190 76 73 Iceland 50,734.40 46480 83 74 India 1,613.20 74 6060 68 75 Indonesia 3,336.10 15 10700 69 76 Iran, Islamic Rep 4,957.60 43 17860 76 77 Iraq 4,974.00 52 15860 70 78 Ireland 60,664.10 10 52990 82 79 Israel 35,729.40 21 35210 82 80 Italy 30,049.10 20 36580 83 81 Jamaica 4,966.00 17 8280 76 82 Japan 34,474.10 13 41950 84 83 Jordan 4,096.10 39 8880 74 84 Kazakhstan 10,510.00 20 23620 72 85 Kenya 1,350.00 16 2960 67 37 86 Kiribati 1,424.50 4330 66 87 Korea, Rep 27,105.10 29 35860 82 88 Kuwait 28,975.40 67 83360 75 89 Kyrgyz Republic 1,121.10 17 3320 71 90 Lao PDR 2,159.40 33 5810 66 91 Latvia 13,666.60 20 24580 74 92 Lebanon 8,046.60 33 12570 79 93 Lesotho 1,073.80 25 3400 54 94 Liberia 852 1190 62 95 Lithuania 14,252.40 19 27730 75 96 Luxembourg 101,909.80 17 69470 82 97 Macedonia, FYR 4,834.10 40 98420 76 98 Madagascar 1401.9 20 1410 66 99 Malawi 1362.7 26 1590 63 100 Malaysia 9,643.60 16 26360 75 101 Maldives 8,395.80 29 12450 77 102 Mali 729.7 44 2010 57 103 Malta 23,819.50 16 34250 82 104 Mauritania 1,158.30 85 3830 63 105 Mauritius 9,252.10 15 21870 74 106 Mexico 9,143.10 20 17830 77 107 Micronesia, Fed Sts 3,016.00 3980 69 108 Moldova 1,832.50 21 6440 71 109 Mongolia 3,944.20 24 11110 69 110 Montenegro 6,461.20 23 16700 77 111 Morocco 2,847.30 23 7670 76 112 Mozambique 528.3 20 1210 58 113 Myanmar 1,194.60 54 5190 66 114 Namibia 4,737.70 21 11110 64 115 Nepal 1743.8 75 2660 70 38 116 Netherlands 44,292.90 15 50340 82 117 New Zealand 38,201.90 36210 81 118 Nicaragua 2,096.00 27 5030 75 119 Niger 359 63 940 60 120 Nigeria 2,655.20 38 5910 53 121 Norway 74,505.20 63030 82 122 Oman 16,627.40 53 41060 77 123 Pakistan 1,431.20 65 5050 66 124 Panama 13,134.00 13 20040 78 125 Paraguay 4,109.40 15 11360 73 126 Peru 6,030.30 28 12500 75 127 Philippines 2,878.30 23 8850 69 128 Poland 12,566.00 24 25880 78 129 Portugal 19,220.00 10 28870 82 130 Qatar 66,346.50 107 121090 78 131 Romania 8,958.80 20 21130 75 132 Russian Federation 9,329.30 17 23400 71 133 Rwanda 710.3 50 1850 67 134 Samoa 4,149.40 5830 75 135 Sao Tome and Principe 1,624.60 14 3080 66 136 Saudi Arabia 20,732.90 106 55320 75 137 Senegal 908.7 38 3140 67 138 Serbia 5,237.30 21 14230 75 139 Seychelles 15,390.00 13 24940 73 140 Sierra Leone 587.5 19 1400 51 141 Singapore 53,629.70 19 82930 83 142 Slovak Republic 16,089.70 21 28950 77 143 Slovenia 20,729.90 20 30660 81 144 Solomon Islands 1,922.00 2170 70 39 145 Somalia 426 20 789 56 146 South Africa 5,769.80 30 10860 62 147 South Sudan 758.7 32 1870 56 148 Spain 25,683.80 10 34930 83 149 Sri Lanka 3,844.90 28 11530 75 150 St Lucia 8,076.10 14 11190 75 the 6,739.60 14 12280 73 152 Sudan 2,513.90 50 4140 64 153 Suriname 8,819.00 18 15430 71 154 Swaziland 3,136.90 22 8520 57 155 Sweden 50,585.30 48930 83 156 Switzerland 80,989.80 13 65450 83 157 Tajikistan 2918.7 50 3410 71 158 Tanzania 872.3 23 2740 65 159 Thailand 5,814.90 26 15450 75 160 Timore-Leste 1,161.80 19 7390 69 161 Togo 551.1 33 1620 60 162 Tonga 4,093.80 5910 73 163 Trinidad and Tobago 17,321.80 14 33280 71 164 Tunisia 3,828.10 45 11240 75 165 Turkey 10,979.50 36 25340 75 166 Turkmenistan 6,432.70 31 15070 68 167 Uganda 693.9 60 1830 60 168 Ukraine 2,124.70 19 7880 71 169 United Arab Emirates 39,101.70 64 70600 77 170 United Kingdom 43,929.70 12 41090 82 171 United States 56,207.00 58300 79 172 Uruguay 15,524.80 11 20350 77 151 St Vincent and Grenadines 40 173 Uzbekistan 2,137.60 40 6130 71 174 Vanuatu 2,805.80 2860 72 175 Vietnam 2,107.00 28 5680 76 176 Vigin Islands (U.S.) 36,350.80 16 30987 80 177 West Bank and Gaza 2,865.80 21 5450 73 178 Yemen, Rep 1,401.90 53 3220 65 179 Zambia 1,313.90 27 3860 61 180 Zimbabwe 1,018.70 23 2410 60 41 Sceenshot the result from STATA: 42 Run command: des LE GPC AP GIC Run command: sum LE GPC AP GIC Run command: corr LE GPC AP GIC Run command: reg LE GPC AP GIC 43 REFERENCES Air pollution statistics http://ec.europa.eu/eurostat/statisticsexplained/index.php/Air_pollution_statistics Brunekreef B Air pollution and life expectancy: is there a relation? Occupational and Environmental Medicine 1997 Damodar N Gujarati and Dawn C.Porter, Basic Econometrics, 5th edition Daniel A Vallero, 2007, Fundamentals of air pollution Kenneth Wark, Cecil Francis Warner, 1976, Air pollution, its origin and control Kulkarni S, Lavin-Rector A, Ezzati M, Murray CJ Falling behind: life expectancy in U.S counties from 2000 to 2007 in an international context Population Health Metrics 2011 Life expectancy at birth versus GDP per capita (PPP) http://www.statisticalconsultants.co.nz/blog/life-expectancy-at-birth-versusgdp-per-capita-ppp.html Peng RD, Dominici F Statistical methods for environmental epidemiology in R: a case study in air pollution and health Springer; 2008 Pope CA, III, Ezzati M, Dockery D Fine-particulate air pollution and life expectancy in the United States New England Journal of Medicine 2009 10 Pr Ph.D Nguyen Quang Dong and vPr Ph.D Nguyen Thi Minh, 2015, Econometrics textbook, Publisher of National Economics University 11 Shrestha LB Life expectancy in the United States CRS Report for Congress 2005 12 The Effect of Air Pollution Control on Life Expectancy in the United States: An Analysis of 545 US counties for the period 2000 to 2007 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521092/ 13 The Growing Gap in Life Expectancy by Income: Implications for Federal Programs and Policy Responses (2015) https://www.nap.edu/read/19015/chapter/2 44 14 Life expectancy and national income in Europe, 1900-2008: an update of Preston’s analysis https://doi.org/10.1093/ije/dyt122 15 Geography, income play roles in life expectancy, new Stanford research shows https://news.stanford.edu/2016/04/11/geography-income-play-roles-in-lifeexpectancy-new-stanford-research-shows/ 16 Akansha Maity, Emelie Rhenman, and Elijah Sanders, Factors Explaining Average Life Expectancy:An Examination Across Nations https://smartech.gatech.edu/bitstream/handle/1853/59089/final_paper_0.pdf 17 Pickett, Kate E, and Richard G Wilkinson “Income Inequality and Health: A Causal Review.” Social Science & Medicine, Pergamon, 2014 https://www.sciencedirect.com/science/article/abs/pii/S0277953614008399 18 How income affects life expectancy 19 https://www.weforum.org/agenda/2015/09/how-income-affects-life- expectancy/ 20 Sara Hertog , The Association between Two Measures of Inequality in Human Development: Income and Life Expectancy https://www.un.org/en/development/desa/population/publications/pdf/technic al/TP2013-7.pdf 21For life expectancy, money matters https://news.harvard.edu/gazette/story/2016/04/for-life-expectancy-moneymatters/ 22 Has the relation between income inequality and life expectancy disappeared? Evidence from Italy and top industrialised countries https://jech.bmj.com/content/59/2/158 23 Life expectancy at birth, total (years) https://data.worldbank.org/indicator/SP.DYN.LE00.IN 24 Economic Growth and Life Expectancy – Do Wealthier Countries Live Longer? 45 https://blog.euromonitor.com/economic-growth-and-life-expectancy-dowealthier-countries-live-longer/ 25 Ambient PM2.5 Reduces Global and Regional Life Expectancy https://pubs.acs.org/doi/10.1021/acs.estlett.8b00360/ 46 INDIVIDUALS ASSESSMENT Evaluator Trang Uyên Ly Linh Trang - 10 10 10 Uyên 9.75 - 9.75 9.5 Linh 10 9.50 9.50 - Ly 10 9.75 - 9.75 Average score 9.92 9.75 9.75 9.75 47 ... method of calculating this proxy of human life expectancy for the social condition of Vietnam and other countries Life expectancy * Definition Life expectancy is a statistical measure of the... build this following model to analyze the effect of some factors to Human’s Life Expectancy LE = f(GPC, AP, GIC) In which:  LE : Life Expectancy at Birth (year)  GPC : Gross domestic product... 1$ USD, the human’s life expectancy will increase 0.0000805 year, ceteris paribus = - 0.1005909 < 0: When air pollution level increases to microgram per cubic meter, the human’s life expectancy