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’s members Student ID Hoàng Vân Anh 1815520150 Nguyễn Hoàng Bách 1815520156 Vũ Minh Ngọc 1815520211 Class: KTEE218 (1-1920).1_LT Course: Econometrics Lecturer: Dr Tu Thuy Anh Dr Chu Thi Mai Phuong Hanoi, October 2019 INDEX ABSTRACT 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 According to World Health Organization, global life expectancy at birth in 2016 was 72.0 years (74.2 years for females 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 two regions.So what has affected life expectancy in the world? As economics students interested in social matters, we decided to research on the topic "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 Health expenditure (% GDP) and air pollution In order to gain a better understanding of the 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 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 INTRODUCTION Econometrics is the meaningful study of the social sciences in which the tools of economic theory, mathematics and statistical speculation are applied to analyze economic problems Econometrics uses the mathematical statistics methods to find out the essence of statistics, make conclusions about the collected statistics that can make predictions about economic phenomenon Since its inception, econometrics has provided economists with a sharp instrument for measuring economic relations As economics students, we recognize the importance of studying about Econometrics in logical and problem analysis To better understand how to put the Econometrics into reality and to apply the Econometrics effectively 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 of econometrics to analyze the data Noted that because of the lack of information on the data set, all inferences of abbreviations and others are based on assumptions and selfresearch 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 set given and the knowledge that we have gained through Dr Tu Thuy Anh’s Econometrics course 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 have also 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 revealed that 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 income and 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 to follow 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 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 on human life expectancy The four factors are: Air pollution (µg/m 3); GNIpc: Gross national 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 of Health has shown an influence from air quality on human life from 2000 to 2007 in the United States Table summarizes estimated regression coefficients for the association between changes 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 socioeconomic and 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 life expectancy of 0.35 years (SE= 0.16 years, p = 0.033) The estimated effect of PM 2.5 on life expectancy was consistent across models adjusting for various patterns of potentially confounding variables (e.g Models – 4) Models – of Table show the results for select stratified and weighted regressions In counties with a population density greater than 200 people per square mile, a 10 µg/m decrease in PM2.5 was associated with an increased life expectancy of 0.72 (0.22 years, p< 0.01) (Model 6), compared with −0.31 years (0.22 years, p = 0.165) in counties with less than 200 people per square mile (P difference = 30: Count of condition indices >= 10: Variance proportions >= 0.5 associated with cond >= 10: const PW 0,954 0,889 We see: VIF (AP) = 1.363 < 10 VIF (GNIpc) = 1.550 < 10 VIF (HE) = 1.350 < 10 VIF (PW) = 1,578 < 10 → The model does not contain perfect multicollinearity 4.6 Testing normality of residual • Given that the hypothesis is: { H0: The residuals have normality 1: The residuals don′ t have normality • Using normality of residual in Gretl: Frequency distribution for uhat4, obs 1-215 number of bins = 15, mean = 2,44559e-015, sd = 3,42874 interval midpt frequency rel cum < -12,207 -13,044 0,47% 0,47% -12,207 - -10,533 -11,370 0,47% 0,93% -10,533 - -8,8600 -9,6967 1,86% 2,79% -8,8600 - -7,1865 -8,0232 1,86% 4,65% -7,1865 - -5,5130 -6,3497 1,40% 6,05% -5,5130 - -3,8395 -4,6762 14 6,51% 12,56% ** -3,8395 - -2,1660 -3,0027 14 6,51% 19,07% ** -2,1660 - -0,49249 -1,3292 41 19,07% 38,14% ****** -0,49249 - 1,1810 0,34426 54 25,12% 63,26% ********* 1,1810 - 2,8545 2,0178 42 19,53% 82,79% ******* 2,8545 - 4,5280 3,6913 25 11,63% 94,42% **** 4,5280 - 6,2015 5,3648 3,72% 98,14% * 22 6,2015 - 7,8750 7,0383 7,8750 - 9,5485 8,7118 >= 9,5485 10,385 1,40% 99,53% 0,00% 99,53% 0,47% 100,00% Test for null hypothesis of normal distribution: Chi-square(2) = 17,183 with p-value 0,00019 • We see: Chi-square(2) = 17,183 with p-value 0.0002 < α = 0.05 At 5% level of significant, we have enough evidence to reject the null hypothesis H0 → The model does not have normality • Method: Increasing the number of observations until n ≥ 384 4.7 Testing Heteroskedasticity Heteroskedasticity indicates that the variance of the error term is not constant, which makes the least squares results no longer efficient and t tests and F tests results may be misleading The problem of Heteroskedasticity can be detected by plotting the residuals against each of the regressors, most popularly the White’s test It can be remedied by respecifying the model – look for other missing variables 23 In Gretl, the imtest white command is used, which stands for information matric test • Given that the hypothesis is: ������� ��: ��� ����� ���� ��� ���� ������������������ { ��: ��� ����� ��� ������������������ • White’s test: White's test for heteroskedasticity OLS, using observations 1-215 Dependent variable: uhat^2 coefficient std error t-ratio p-value -const 91,9429 51,9750 1,769 0,0784 * AP −1,06955 0,678559 −1,576 0,1166 GNIpc 0,00411162 0,00258476 1,591 0,1133 HE 2,66988 7,03961 0,3793 0,7049 PW −1,50364 1,37435 −1,094 0,2752 sq_AP 0,00171615 0,00357148 0,4805 0,6314 X2_X3 −2,73513e-06 4,09125e-06 −0,6685 0,5046 X2_X4 0,0228975 0,0517823 0,4422 0,6588 X2_X5 0,00810285 0,00611762 1,325 0,1868 sq_GNIpc 1,41019e-09 4,54122e-09 0,3105 0,7565 X3_X4 −4,17399e-05 4,56088e-05 −0,9152 0,3612 X3_X5 −3,87740e-05 2,85745e-05 −1,357 0,1763 sq_HE 0,312751 0,181513 1,723 0,0864 * X4_X5 −0,0689671 0,0641929 −1,074 0,2840 sq_PW 0,00789122 0,00988041 0,7987 0,4254 Unadjusted R-squared = 0,208014 Test statistic: TR^2 = 44,722940, with p-value = P(Chi-square(14) > 44,722940) = 0,000045 • We see: p-value = P(Chi-square(14) > 44,722940) = 0,000045 < α = 0.05 24  At the 5% significance level, there is enough statistical evidence to reject the null hypothesis H0 and conclude that this set of data meets the problem of Heteroskedasticity • Method: Using Robust to fix the problem: Model 5: OLS, using observations 1-215 Dependent variable: LEB Heteroskedasticity-robust standard errors, variant HC1 Const AP GNIpc HE PW Coefficient Std Error 43,9735 2,41354 −0,0437552 0,0140952 0,00011358 1,68835e-05 0,231884 0,292728 Mean dependent var Sum squared resid R-squared F(4, 210) Log-likelihood Schwarz criterion 0,148813 0,0242668 71,80154 2468,808 0,794530 170,3188 −567,4634 1161,780 t-ratio 18,22 −3,104 6,727 p-value