tiểu luận kinh tế lượng FACTORS AFFECTING HUMAN LIFE EXPECTANCY

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS ECONOMETRICS REPORT FACTORS AFFECTING HUMAN LIFE EXPECTANCY GROUP 23 Group Member Cao Thu Trang Nguyễn Thị Hải Yến Nguyễn Thị Huyền Trang Student ID 1815520226 1815520240 1815520234 Lecturer : Mrs Tu Thuy Anh Ha Noi, October 2019 TABLE OF CONTENTS INTRODUCTION Chapter : LITERATURE REVIEW Pickett and Wilkinson (2005) 2 Erdal Demirhan, Mahmut Masca (2008) 3 Lillard, Burkhauser, Hahn, and Wilkins (2014) Chapter : METHODOLOGY Methodology used: 1.1 Methodology in collecting data: 1.2 Methodology in analyzing data Data Description 3.1 Data source 3.2 Statistics Description 3.3 Correlation Description Among Variables Chapter : ESTIMATED RESULT & STATISTICAL INFERENCE Estimated Model Testing Assumption 10 2.1 Testing Multicollinearity 10 2.2 Testing Autocorrelation 11 2.3 Testing Normality of residual 11 3.4 Testing Heteroskedasticity 17 Hypothesis Testing 17 4.1 Statistical Significance of Coefficients (in model 2) 17 4.2 Statistical Significance of Model 18 Recommendation 18 CONCLUSION 19 REFERENCES 20 APPENDIX 21 INTRODUCTION Human life expectancy is a statistical measure of the average time that a person is expected to live, based on the year of his birth, his current age and other demographic factors including gender Specially in the modern life with fast developing speed and increasingly international integration trend, life expectancy is perhaps the most important measure of health as well as the national development A great many factors have been researched before that affect the human longevity; however, it is only realistic to cover a comparatively small number of such factors for the sake of statistical analysis Thus the question arises: What exactly are the factors that have such an effect on human life expectancy? Having the awareness of how important the life expectancy is, we decided to find more about elements affecting the human lifespan whether it is a good or bad factor The variables we will be testing is : GDP per capita, GNI per capita, the level of air pollution as well Researching topic “Factors affecting human life expectancy”, we aim to understand and find out the solution contributing towards the enhancement of human life expectancy To accomplish the goal above, we need to estimate the regression model and testing the significance of variables Accompany with us in this project is an econometrics tool named “Gretl” In the end, we expect to achieve an objective look into the issue as well as apply appropriate measures to make progress in improving problems related to life expectancy Except for introduction and conclusion, this report includes main parts : - Chapter : Literature Review - Chapter : Methodology - Chapter : Estimated Result & Statistical Inference Chapter : LITERATURE REVIEW Prior to studying the relationship between average lifespan and poverty, it was crucial to examine other studies that dealt with a similar topic Life expectancy is an important index that reflects the standard of living and the social situation as well as the economic development level of a country Therefore, in recent decades, there are a lot of studies carried out that research about life expectancy and the potential factors affecting it Three papers were specifically chosen for this literature review to show that the project topic is significant Pickett and Wilkinson (2005) examined other research papers’ finds on the relationship; Erdal Demirhan, Mahmut Masca (2008) explored seven determinants of FDI of 38 developing countries from 2000 to 2004 and Lillard, Burkhauser, Hahn, and Wilkins (2014) looked at a self-reported health survey and income inequality Pickett and Wilkinson (2005) The research had conducted research on the relationship between income inequality and population health and suggested why the results might be “wholly supportive,” “unsupportive,” and “partially supportive” of the claim that these two variables were related “Wholly supportive” meant that the relationship between the two variables had only positive statistically significant associations “Unsupportive” implied that there were no statistically significant positive associations “Partially supportive” signified that only some of the relationships had statistically significant positive associations 70% of the studies implied that when there was larger income inequality, the health of the population suffered from poorer health The paper found that it was important to sample a large area to show the true nature of income inequality For example, studies that looked at large subnational regions were not as likely to prove the relationship between income inequality in health as international studies or studies examining sub-national regions Another issue in a few of the studies was identifying the proper control variables For example, the authors acknowledge that as countries are wealthier per capita, the relationship between life expectancy and GNI per capita becomes less prevalent Once two issues were identified, Wilkinson and Pickett reviewed all of the papers and found that only 8% of them were unsupportive of the claim that health and income inequality were related Therefore, the variables of health and income inequality ought to be associated Erdal Demirhan, Mahmut Masca (2008) In the document “Determinants of foreign direct investment flows to developing countries: a cross-sectional analysis”, Erdal Demirhan and Mahmut Masca explored seven determinants of FDI with a cross-country data of 38 developing countries in the five-year period from 2000 to 2004 One of those determinants that is directly affected to LEB is GDP per capita, the growth rate of which is used in the research as the proxy for market size Prior to building their own model, the authors mentioned the findings from a few 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 said that FDI tend to flow into economies with larger and expanding markets, translating into 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 thus increase profit, life expectancy Lillard, Burkhauser, Hahn, and Wilkins (2014) This research investigated the relationship between a US-born adult’s selfreported health and income inequality The dependent variable was in a range from 1-5 (1 being “poor” and being “excellent”) The independent variable was the share held by the top 1% from the age of 0-4 and also whether or not the child was considered as poor growing up The main find of this research paper was that if individuals suffered from income inequality early in their lives, they were more likely to have worse health and this association is statistically significant for both genders For example, if a male had grown up in a high income inequality society, they would be more likely to have worse health However, there are some issues with this paper that the researchers acknowledge Since the income inequality measure only changes over time and does not differ across groups that live in different regions of the US, there may be omitted variable bias Furthermore, the paper uses a linear model between inequality and health, when the true model may in fact be nonlinear Though the paper does not suggest why health and income inequality may be associated, it does encourage future studies to examine the mechanism of the relationship ➔ From the literature review above, we can see that GDP per capita, GNI per capita have the effect on the level of human life expectancy However, there is no current study including impact of all these factors, so we decided to conduct this research to find out how they affect on the life expectancy of 180 countries all over the world Chapter : METHODOLOGY Methodology used: During the project, we have used the knowledge of econometrics and macroeconomics with the main support of Gretl software, Microsoft Excel, Microsoft Word for completion this report 1.1 Methodology in collecting data: We collected this set of data which indicates the information of factors affecting the human life expectancy: GDP per capita, air pollution, GNI per capita This secondary data was gathered from reliable source of information- World Bank 1.2 Methodology in analyzing data We use Gretl to bring out the regression models by using Ordinary Least Squares method (OLS) to estimate the parameter of multi-variables regression models As a result, we can: - Use normality of residual command to test the normal distribution of the disturbance - Identify multicollinearity by calculating Variance Inflation Factor (VIF) thanks to Collinearity command - Use White test to test heteroskedasticity problem Model To demonstrate the relationship between human life expectancy and other factors, the regression function can be constructed as follows : (write in the stochastic form) ● Population Regression Function (PRF) : LE = + 1*GDP + 2*AP + 3*GNI + u ● Sample Regression Function (SRF) : LE= b0 + b1*GDP + b2*AP + b3*GNI + e In which : βo : the intercept of the regression model βi : the slope coefficient of the independent variable Xi u : the disturbance of the regression b0 : the estimation of βo bi : the estimation of βi e : the estimation of u Variables Explanation ➢ Dependent variable: LE : the human life expectancy (year) ➢ Independent variables : Exhibit 2.1 Variables Explanation Variable Meaning Unit Value expectation of regression coefficient GDP GDP per capita USD AP air pollution PM2.5 μg/m3 (annual exposure) (microgram per cubic meter) - GNI GNI per capita + USD + Data Description 3.1 Data source - We collected this set of data from data.worldbank.org - a reliable source LE: https://data.worldbank.org/indicator/SP.DYN.LE00.IN GDP: https://data.worldbank.org/indicator/SP.DYN.LE00.IN AP: https://data.worldbank.org/indicator/SP.DYN.LE00.IN GNI: https://data.worldbank.org/indicator/SP.DYN.LE00.IN - The structure of this data : cross-sectional data 3.2 Statistics Description - We use the “summary statistics” command in Gretl to get statistical indicators of the variables It shows the average value (Mean), the middle value (Median), the standard deviation (S.D) as well as the minimum value (Min) and the maximum value (Max) of all the given variables Exhibit 2.2 Statistics Description (Source : Gretl) Summary Statistics, using the observations – 40 Variable Mean Median S.D Min Max GDP 12804 5105.6 17555 303.70 1.019e+005 AP 28.344 21.000 19.78 3.000 1107.00 GNI 18850 11300 20637 750.0 1.211e+005 LE 71.533 73.000 8.036 51.00 84.00 From the result on Exhibit 2.2 above : o 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 o The standard deviation of variable GDP 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 o 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) o Variable GNI has the highest value of standard deviation among given variables: 20637.23 This result is reasonable because people in high income countries may enjoy much convenience than people in low income countries 3.3 Correlation Description Among Variables - Before running the regression model, we consider the correlation among variables by using the “correlation matrix” command in Gretl - Correlation Coefficients, using the observations - 180 5% critical value (two-tailed) = 0.1463 for n = 180 We obtained the correlation table among variables as Exhibit 2.3 follows: Exhibit 2.3 Correlation Matrix (Source : Gretl) LE GDP AP GNI LE 1.000 GDP 0.6291 1.000 AP -0.3224 -0.2420 1.000 GNI 0.6413 0.8181 -0.0672 1.000 ● The correlation coefficient between LE and GDP is 0,6291, which is positive and quite high and in accordance with the theory Therefore, GDP has a positive effect on LE, any change in GDP per capital will lead to a largely similar change in human life expectancy ● The correlation coefficient between LE and AP is -0,3224, which is in accordance with the theory The air pollution has a negative effect on LE, any change in level of air pollution will lead to a slightly inverse change in human life expectancy ● The correlation coefficient between LE and GNI is 0,6413, which is positive and relatively high and in accordance with the theory Therefore, GNI has a largely positive effect on LE, any change in density of the city will lead to a largely similar change in human life expectancy ➔ According to the figures from the table, all the correlation coefficient between dependent variable and independent variables is less than 0,8 => The multicollinearity is not like to occur in this model ➔ To fix the problem, we need to establish another model in which we use the logarithm of all the variables - Model Testing Assumption for Model Exhibit 3.6 OLS - Model ➢ Based on the result collected from the Exhibit 3.6, we have new Sample Regression Function as follow: l_LE = 3,636 + 0,0303667*l_GDP - 0,0168294*l_AP +0,0453417*l_GNI + e ● The coefficient of determination : + R = 0.709 means that the independent variables in the model account for 70.9% of the variation in the value of the dependent variable and the remaining depends on other factors ● Meaning of estimation coefficients: + b0 = 3.636 : In case that all factors equal 0, then the average human life expectancy is 3.636 years + b1 = 0.0303667 > => In accordance with the theory In case other factors doesn’t change, when the GDP per capita increases 1$, then the average human life expectancy will increase 0.0303667 year + b2 = -0.0168< => In accordance with the theory In case other factors doesn’t change, when the air pollution level increases microgram per cubic meter, then the average human life expectancy decrease 0.0168 year + b3 = 0.045 > => In accordance with the theory 14 In case other factors doesn’t change, when the GNI per capita increases 1$, then the average human life expectancy will increase 0.045 year 3.1 Testing Multicollinearity - Using the Collinearity command in Gretl to know the VIF (variance inflation factor) If VIF is equal or bigger than 10, there will be a perfect multicollinearity Exhibit 3.7 Collinearity - Model - The result shows: ➢ Since the VIF is lower than 10, indicating that there is no multicollinearity in model 3.2 Testing Autocorrelation This set of data is cross-sectional so we can skip this step 3.3 Testing Normality of Residual - Using the command Normality of Residual in Gretl, we get the result as follow: Exhibit 3.8 Histogram - Model 15 Exhibit 3.9 Normality of residual - Model - - Hypotheses: + H0: The disturbance follow normal distribution + H1: The disturbance doesn’t follow normal distribution As the result shown on the Exhibit 3.9, we see the p- value = 0,00001 < 0,05 ➔ We have enough evidence to reject H0, the disturbance still doesn’t follow normal distribution However, as we said above (in 2.3 part), the number of observations is 180, it is still able to conduct a significant test as usual 16 3.4 Testing Heteroskedasticity - Using the White test on Gretl, we got the result shown on Exhibit 3.10 Exhibit 3.10 White test - Model - Hypothesis: + H0: var (ui) = constant for all i - From the result on Exhibit 3.10, we get the p-value = 0.154577 > 0.05 We have enough evidence to accept H0 ➔ Heteroskedasticity isn’t the problem with model ➔ We have enough evidence to conclude that Model is the perfect estimation for this set of data Hypothesis Testing 4.1 Statistical Significance of Coefficients (in model 2) ➢ Hypotheses : + H0 : = (i = 1,2,3) + H1: ≠0 ➢ Level of significant : 5% ➢ As a result on the Exhibit 3.6 : ● p-value for l_GDP = 0.0029 < α = 0.05 ➔ We have enough evidence to reject H0 ➔ is statistically significant 17 ● p-value for l_AP = 0.0351 < α = 0.05 ➔ We have enough evidence to reject H0 ➔ is statistically significant ● p-value for l_GNI = 8.05e-05 < α = 0.05 ➔ We have enough evidence to reject H0 ➔ is statistically significant CONCLUSION: All given variables are statistically significant 4.2 Statistical Significance of Model ➢ Hypotheses ➢ Level of significance : 5% ➢ From the result on Exhibit 3.6, we see the p - value F - test = 6.23e-47 < α = 0.05 ➔ We have enough evidence to reject H0 + + H0: 1= 2= 3=0 H1: 12+ 22+ 32≠0 ➔ Model is statistically significant at 5% level of significance Recommendation According to the findings of this study, we can conclude that enhancing human life expectancy through improving GDP per capita, GNI per capita; and decreasing the level of air pollution Governments of countries should establish national programs that improve long run GDP per capita, as well as GNI per capita such as open to trade and investment, enhance infrastructure, promote human & physical capital, Moreover, countries should pursue policies to control more effectively air pollution and particulate matter, from stationary and mobile sources in their countries in order to achieve environmentally acceptable levels of ambient air quality and deposition of pollutants 18 CONCLUSION This essay have been completed under the contribution of members with knowledge gained from the researches and studies of Econometrics, together with the external knowledge By doing this essay, we can better understand the process of running the econometric model, analyzing, verifying the fit of the model and the relationship between variables in the model In addition, we can apply the knowledge learned and through the econometric model analysis to draw useful conclusions for a socio-economic problem We would like to thank Mrs Tu Thuy Anh and Mrs Mai Phuong for the guidance and suggestions to help us understand the problem and analyze in the right direction In the process, due to our limited understanding and collecting data, it is inevitable that there are some mistakes in the assignment In addition, the selected variables may not be necessarily the best ones that affect the life expectancy because it can be influenced by many other factors such as gender, genetics, lifestyle and so on This is the biggest drawback that we could not avoid Therefore, we hope this essay will contribute as a review and an analysis of some factors that affect life expectancy for future researchers and policy makers to consult and learn more about the model as well as the issue 19 REFERENCES 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 Andrew W Correia, C Arden Pope, III, Douglas W Dockery, Yun Wang, Majid Ezzati, and Francesca Dominici, 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, NCBI, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3521092/ Audre Biciunaite, Economic Growth and Life Expectancy – Do Wealthier Countries Live Longer?, Euromonitor International, https://blog.euromonitor.com/economic-growth-and-life-expectancy-dowealthier-countries-live-longer/ Max Ehrenfreund, How income affect life expectancy, World Economic Forum, https://www.weforum.org/agenda/2015/09/how-income-affects-life-expectancy/ Max Roser, Life expectancy, Our World In Data, https://ourworldindata.org/life-expectancy Pickett, Kate E, and Richard G Wilkinson, Income Inequality and Health: A Causal Review.” Social Science & Medicine, Pergamon, 2014, Science Direct, https://www.sciencedirect.com/science/article/abs/pii/S0277953614008399 James H.Stock & Mark W Watson, Introduction to Econometrics, Brief Edition 20 APPENDIX No Countries Data table 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 21 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 22 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 23 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 24 116 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 25 145 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 26 173 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 27 ... are the factors that have such an effect on human life expectancy? Having the awareness of how important the life expectancy is, we decided to find more about elements affecting the human lifespan... as well Researching topic ? ?Factors affecting human life expectancy? ??, we aim to understand and find out the solution contributing towards the enhancement of human life expectancy To accomplish the... affect life expectancy, World Economic Forum, https://www.weforum.org/agenda/2015/09/how-income-affects -life- expectancy/ Max Roser, Life expectancy, Our World In Data, https://ourworldindata.org /life- expectancy