(TIỂU LUẬN) BUSINESS STATISTICS 1 – ECON1193 TEAM ASSIGNMENT lecturer GREENI MAHESHWARI topic life expectancy at birth

BUSINESS STATISTICS – ECON1193 TEAM ASSIGNMENT Lecturer: GREENI MAHESHWARI Topic: Life Expectancy at Birth TEAM - SG-G04 Student’s Name Le Nguyen Hoang Yen Tu Tan Loc Bui Kim Long Nguyen Quy Ngoc Table of Contents S-number S3754943 S3743818 S3749132 S3757927 Page |2 PART 1: MULTIPLE REGRESSION 1.1 Dataset I - All countries (ALL) 1.2 Dataset II - Low-income countries (LI) .5 1.3 Dataset III - Middle-income countries (MI) 1.4 Dataset IV - High-income countries (HI) PART 2: TEAM REGRESSION CONCLUSION Comparison: Conclusion: .8 PART 3: TIME SERIES a Regression output b Formulas of trend models .13 c Predictions of Life Expectancy at Birth in 2015, 2016, and 2017 .14 d Predictions’ errors of Life Expectancy at Birth in 2015, 2016, and 2017 15 e Calculations of MADs and SSEs of countries 17 PART 4: TEAM TIME SERIES CONCLUSION 18 Comment: .19 Time Series Output comparison: .20 PART 5: OVERALL TEAM CONCLUSION .20 PART 6: REFERENCE LIST 21 PART 7: APPENDICES 22 HI’s backward elimination output updates .22 LI’s backward elimination output updates 23 MI’s backward elimination output updates 24 HI’s backward elimination output updates .24 ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY Page |3 PART 1: MULTIPLE REGRESSION The four data sets have the same dependent and independent variables, which are: - Dependent variable: Life Expectancy at birth (years) - Independent variables: + Domestic general government health expenditure per capita - PPP (current international $) (PPP) 1.1 + People using at least basic drinking water services (% of the population) (BDWS) + Smoking prevalence, total (ages 15+) (% of the population) (SP) + GNI per capita, Atlas method (current US$) (GNI) Dataset I - All countries (ALL) Hypothesis Testing: (applied onward for all other backward eliminations) - (None of the variables have relationships with life expectancy at birth) - (At least one of the variables (PPP, % of people using basic water services (BDWS), Smoking Prevalence (SP), GNI) has a relationship with life expectancy at birth) - P-value Test: + If p-value → not reject : There is no relationship between all variables and life expectancy at birth - Backward Elimination: given significant level (regression outputs can be found in the appendix) + In the multiple regression output above, Smoking Prevalence was the independent variable with the highest P-value (0,886) greater than the significant level indicating an insignificant relationship between this variable and life expectancy at birth Hence, we applied backward elimination and eliminated SP to proceed with a new multiple regression + In the second multiple regression output, GNI per capita had the highest p-value (0,857) much larger than meaning it was non-significant and should be deducted from the dataset following the rule of backward elimination to perform the next multiple regression for more accuracy - The FINAL model of multiple regression: ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY Page |4 + After eliminating the two insignificant variables, we are left with the final regression output in which PPP and the percentage of people using basic drinking water services are significant variables with p-values of < 0,05 Figure Final model of multiple regression of all countries + Regression Equation: Life expectancy at birth (years) : PPP per capita (current international $) : People using at least basic water services (% of the population) + Interpretations of regression coefficients: ● The slope shows that life expectancy at birth in all countries will increase by 0,002 years with every international $ increase in PPP per capita ● The slope shows that life expectancy at birth in all countries will increase by 0,31 years for every increase in the percentage of the population getting to use at least basic water services ● The intercept means without the general government health expenditure (PPP) and basic drinking water services, people in all countries can attain the life expectancy at birth of 43,49 years + The coefficient of determination implies that 82,39% of the variation in life expectancy at birth in all countries can be explained by the variations of PPP per capita and the percentage of people getting to use at least basic water services in each country The remaining of 17,61% may be due to other ‘demographic and socioeconomic’ factors (Mondal and Shitan 2014, pa 1, p.118) ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY Page |5 1.2 Dataset II - Low-income countries (LI) - Backward Eliminations: (regression outputs can be found in the appendix ) + As seen from the regression output, the P-value of Smoking prevalence appear to be highest In comparison, the P-value of SP is 0.3 higher than the α (0.05) Hence, we eliminated this IV as it is insignificant and does not have a relationship with LEAB Hence we eliminate SP + From the second regression output, GNI per capita appears to be the least significant variable with the P-value of 0.09, higher than the α (0.05) Consequently, this IV does not have a relationship with LEAB Hence we eliminate GNI per capita + From the third regression output, the P-value of People using at least basic drinking water services (% of the population) appears to be greatest In comparison, the Pvalue of BDWS is 0.33 higher than the α (0.05) As a result, this IV is insignificant and does not have a relationship with LEAB Hence we eliminate BDWS + Finally, the P-value of the Domestic general government health expenditure per capita (0.21) still higher than the α (0.05) Then, this IV is insignificant and does not have a relationship with LEAB Hence we eliminate PPP ⇨ Conclusion: As we have eliminated all variables, there is no relationship between the four variables and Life Expectancy at Birth among Low-Income countries Hence, no final regression output is constructed for the LI dataset showing the life expectancy at birth in low-countries may depend on other factors 1.3 Dataset III - Middle-income countries (MI) - Backward Eliminations: (multiple regressions updates can be found in the appendix) + Based on the multiple regression output above, SP is the independent variable with the highest p-value (0.691) larger than α (0.691 > 0.05) Since it is insignificant implying no connection with LEAB, we apply the rule of backward elimination and eliminate this variable + Based on our first updated multiple regression output above, PPP is the independent variable with the highest p-value (0.556) and larger than α (0.556 > 0.05) Similar to SP, PPP in this output is insignificant and shows no connection with LEAB Again, we apply the rule of backward elimination and eliminate this variable + In our second updated multiple regression output above, GNI is the independent variable with the highest p-value (0.593) and larger than α (0.593 > 0.05) Hence, ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY Page |6 GNI in our second attempt is insignificant and has no relationship with LEAB Again, we apply the rule of backward elimination and eliminate this variable Figure Final multiple regression output of MI countries - After several attempts, we have the final regression of BDWS This significant variable has a p-value of 0, which is smaller than α (0 < 0.05) - - Regression Equation: y = 30,819 + 0,458X + y: Life Expectancy at birth (years) + X: People using at least basic drinking water services (% of the population) Regression coefficients: + b1= 0,458: LEAB will increase by 0,458 years for every increase in the percentage of the population using at least basic drinking water services + means the life expectancy at birth will only be 30,819 in middle income countries if there is no access to basic drinking water services - Coefficient of determination: R² = 0.7542: indicates 75.42% of the variation in LEAB can be explained by the variation in the percentage of people using at least basic drinking water services, the remaining 24.58% of the variation may be due to other factors 1.4.Dataset IV - High-income countries (HI) - Backward Elimination: (multiple regression updates can be found in the appendix) + As seen from the regression output, the p-value of PPP appears to be the highest In comparison with , p-value PPP is higher ( 0.939 > 0.05) Therefore; this IV is insignificant and does not have a relationship with LEAB, hence we eliminate PPP ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY Page |7 + As seen from the regression output, the p-value of BDWS appears to be the highest In comparison with , p-value BDWS is higher ( 0.314 > 0.05) Therefore; this IV is insignificant and does not have a relationship with LEAB, hence we eliminate BDWS + As seen from the regression output, the p-value of SP appears to be the highest In comparison with , p-value SP is higher ( 0.084 > 0.05) Therefore; this IV is insignificant and doesn’t have a relationship with LEAB, hence we eliminate SP Figure Final multiple regression model of HI countries - - Equation: y = b0 + b1x = 77.151+ 0.000072x + y: Life Expectancy at birth (years) + x: Gross National Incomes per capita (US$) Regression Coefficient: + b1= 0.000072 shows that LEAB will increase by 0.000072 years for every US$ increase in GNI per capita + means people living in HI countries can attain the average 77,151 in life expectancy even without any earnings to afford essential needs, which is impossible in reality Hence does not make sense in this case - Regression Coefficient of Determination: + R square = 0.3645 means 36.45% of the variation in LEAB can be explained by variation in GNI per capita, and the remaining 63.55% of the variation in LEAB may be due to other factors ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY Page |8 PART 2: TEAM REGRESSION CONCLUSION Comparison: - Significant independent variables vary among all multiple regression models from different datasets Mainly, all countries have two significant variables which are PPP and BWDS Furthermore, both high-income and middle-income countries have only one significant variable which are GNI and BWDS respectively In contrast, life expectancy in low-income countries may be due to other factors since it does not have any relationship with all four variables as proved through the backward elimination steps - The regression model of all countries will provide the best life expectancy at birth estimation because it has the highest R square (82,39%) index indicating a strong relationship between life expectancy and the variables while the other two models of MI and HI countries own lower values (75,4% and 50,4% respectively) implying weaker correlations among the metrics Particularly, in all countries, 82.39%, of the variation in life expectancy at birth can be explained by the variations of PPP per capita and the percentage of people getting to use at least basic water services (BWDS) in each country, the remaining 17.61% may be due to other factors Conclusion: Water and healthcare services are essential factors reflecting a person’s living standards and own significant correlations to a person’s life expectancy at birth Internationally, 82,39% of the general expected lifespan of a child in every country has a positive relationship with such indicators meaning if the government invests and provides more quality drinking water and medical services, the overall lifespan will be prolonged noticeably Furthermore, such correlations can be seen through Queensland University of Technology’s research showing that people living in the countrysides of Australia will live 10 years lesser than those residing in the urban areas with fully equipped quality water sources (Queensland University of Technology 2015) Therefore, the more people have basic drinking water to use; the longer life expectancy they can attain Additionally, it is proved that ‘health care expenditure significantly influences health status through improving life expectancy at birth, reducing death and infant mortality rates’ (Novignon, Olakojo & Nonvignon 2012, pa 3) ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY Page |9 PART 3: TIME SERIES a Regression output - Uganda and El Salvador + Linear trend models Figure Uganda’s linear output + Quadratic trend models Figure Uganda’s Quandratic Output + Figure El Salvador’s Linear Output Figure El Salvador’s Quadratic Output Exponential trend models ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 10 Figure Uganda’s Exponential Output - Figure El Salvador’s Exponential Output United Arab Emirates and Burundi + Linear trend models Figure 10 ARE’s linear output + Quadratic trend models Figure 12 ARE’s quadratic output + Figure 11 Burundi’s linear output Figure 13 Burundi’s quadratic output Exponential trend models Figure 14 ARE’s exponential output ECON1193 – TEAM ASSIGNMENT Figure 15 Burundi’s exponential output TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 11 - Qatar and Nigeria + Linear trend models Figure 16 Qatar’s Linear Output + Quadratic trend models Figure 18 Qatar’s quadratic output + Figure 17 Nigeria’s linear output Figure 19 Nigeria’s quadratic output Exponential trend models Figure 20 Qatar’s exponential output ECON1193 – TEAM ASSIGNMENT Figure 21 Nigeria’s exponential output TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 12 - Slovania and Rwanda + Linear trend models Figure 22 Slovenia’s linear output + Figure 24 Slovenia’s quadratic output + Figure 23 Rwanda’s linear output Quadratic trend models Figure 25 Rwanda’s quadratic output Exponential trend models Figure 26 Slovenia’s exponential output ECON1193 – TEAM ASSIGNMENT Figure 27 Rwanda’s exponential output TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 13 b Formulas of trend models Countries Linear Formula Quadratic Formula = 41,95 + 0,509 Uganda = 47,702 - 0,57+ 0,035 Exponential Formula Linear: log=1,629 + 0,004 Non-linear: = () (UGA) El Salvador = 61,215 + 0,431 = 59,027 + 0,841- 0,013 Linear: log= 1,788 + 0,003 Non-linear: = 61,376 (SLV) United Arab * = 70.096 + 0.245T * = 69.536 + 0.35T - 0.0034T2 Linear: log* =1.846 + 0,0014T Non-linear: * = (101.846)(100.0014T) Emirates (ARE) Burundi * = 46.615 + 0.306T * = 48.149+0.019T+0.0093T2 Non-linear: * (BDI) Qatar * = 74,21 + 0,123T * = 74,009 + 0,16T – 0,001 * = 39,804 + 0,643T * = 40,411 +0,53T + 0,004 Linear: log * = 1,607 + 0,006T * = 70.713 + 0,328T * = 71,426 + 0,194T+ 0,004 Linear: log * = 1,85 + 0,002T Non-linear: * = ( * = 31.6 + 1,082T * = 43.654 - 1.179T+ 0,073 Linear: log= 1,517 + 0,01T Non-linear: * = ( (SVN) Rwanda Linear: log * =1,871 + 0,0007T Non-linear: * = (40,46)( (NGA) Slovenia = (101.67)(100.0026T) Non-linear: * = (74.3)() (QAT) Nigeria Linear: log * = 1.67 + 0.0026T (RWA) Figure 28 Table of trend model formulas from eight countries c Predictions of Life Expectancy at Birth in 2015, 2016, and 2017 Countries MODEL 2015 () 2016 () 2017 () Uganda (UGA) Linear 57,729 58,238 58,747 Quadratic 63,667 65,302 67,007 Exponential 56,624 57,148 57,677 Linear 74,576 75,007 75,438 Quadratic 72,605 72,627 72,623 Exponential 76,192 76,726 77,263 Linear 77.691 77.936 78.181 El Salvado (SLV) United Arab ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 14 Emirates Quadratic 77.119 77.254 77.383 (ARE) Exponential 77.518 77.768 78.019 Burundi (BDI) Linear 56.101 56.407 56.713 Quadratic 57.675 58.28 58.904 Exponential 56.312 56.65 56.99 Linear 78,023 78,146 78,269 Quandratic 78,008 78,105 78,2 Exponential 79,048 79,21 79,364 Nigeria Linear 59,733 60,376 61,019 (NGA) Quadratic 60,685 61,467 62,257 Exponential 62,259 63,131 64,015 Slovenia Linear 80,881 81,209 81,537 (SVN) Quadratic 81,284 81,73 82,184 Exponential 81,658 82,035 82,414 Linear 65,142 66,224 67,306 Quadratic 77,258 80,678 84,244 Exponential 67,143 68,707 70,307 Qatar (QAT) Rwanda (RWA) Figure 29 Table of life expectancy predictions from 2015 to 2017 d Predictions’ errors of Life Expectancy at Birth in 2015, 2016, and 2017 Countries Model Uganda (UGA) Linear 1,846 1,651 1,435 Quadratic -4,092 -5,413 -6,825 Exponential 3,558 3,034 2,505 Linear -1,309 -1,495 -1,688 Quadratic 0,662 0,885 1,127 Exponential -2,925 -3,214 -3,513 Linear -0.59 -0.68 -0.769 Quadratic -0.018 0.002 0.029 El Salvado (SLV) United Arab ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 15 Emirates (ARE) Exponential -0.417 -0.512 -0.607 Burundi (BDI) Linear 0.993 1.074 1.144 Quadratic -0.194 -0.799 -1.047 Exponential 1.545 0.831 0.867 Linear 0,013 0,038 -0,062 Quadratic 0,028 0,079 0,131 Exponential -1,012 -1,026 -1,033 Nigeria Linear -0,066 -0,318 -0,597 (NGA) Quadratic -1,018 -1,409 -1,835 Exponential -2,592 -3,073 -3,593 Slovenia Linear -0,105 -0,033 -0,361 (SVN) Quadratic -0,508 -0,554 -1,008 Exponential -0,883 -0,860 -1,238 Linear 1,554 0,905 0,190 Quadratic -10,562 -13,549 -16,748 Exponential -0,447 -1,578 -2,811 Qatar (QAT) Rwanda (RWA) Figure 30 Table of prediction errors in eight countries e Calculations of MADs and SSEs of countries Countries Uganda (UGA) El Salvador (SLV) United Arab Emirates (ARE) Burundi (BDI) Qatar (QAT) Model Linear Quadratic Exponential Linear Quadratic Exponential Linear Quadratic Exponential Linear Quadratic Exponential Linear Quadratic Exponential ECON1193 – TEAM ASSIGNMENT MAD= 4,932 16,33 9.097 4,492 2,674 9,652 2,039 0,013 1,536 4,492 3,211 3,243 0,113 0,238 3,071 1,644 5,443 3,032 1,497 0,891 3,217 0,68 0,0043 0,512 1,5 1,07 1,081 0,038 0,079 1,024 SSE= 8,193 92,626 28,14 6,798 2,491 31,227 1,402 0,0012 0,805 3,448 1,772 3,825 0,005 0,024 3,144 TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 16 Nigeria (NGA) Slovenia (SVN) Rwanda Linear Quadratic Exponential Linear Quadratic Exponential Linear Quadratic Exponential 0,981 4,262 9,258 0,500 2,071 2,980 2,649 40,859 4,836 0,327 1,421 3,086 0,167 0,690 0,993 0,883 13,620 1,612 0,462 6,389 29,071 0,142 1,581 3,052 3,72 575,627 10,592 Figure 31 Table of MAD and SSE values of three trend models in eight countries from 2015 to 2017 3.3 Recommendation: - Uganda: linear model is recommended for its lowest MAD and SSE (1,644 and 8,193) also shows a relatively strong linear relationship - El Salvador: quadratic model is recommended for its loweset MAD and SSE (0,891 and 2,491) indicating low errors also implies a strong quadratic relationship - United Arab Emirates and Burundi: Quadratic trend model is recommended for its smallest MAD and SSE in each nation (0.0043 and 0.0012 in [ARE]; 0.0043 and 0.0012 in [BDI]) High also show strong quadratic relationships ( 99.98% in ARE and 96.24% in BDI) - Qatar and Nigeria: Linear model is recommended for its lowest MAD and SSE in each nation (0,038 and 0,005 in QAT; 0,327 and 0,462 in NGA) High R2 (98,96% in QAT and 99,75% in NGA) also show a strong linear relationship - Slovenia and Rwanda: linear model is recommended for its lowest MAD and SSE (0,167 and 0,142 in SVN; 0,883 and 3,72 in RWA).The high (98,33%in Slovenia and 60,76% in Rwanda) also show a relatively strong linear relationship ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 17 PART 4: TEAM TIME SERIES CONCLUSION Figure 32 Life expectancy at birth of countries from 1985 to 2017 Burundi Uganda Rwanda Nigeria El [BDI] [UGA] [RWA] [NGA] Salvador [SLV] GNI per capita $ 260 $670 $710 $2,880 $3,430 Slovenia [SVN] United Arab Emirates [ARE] Qatar [QAT] $22,230 $43,380 $75,150 Figure 33 Table of GNI per Capita in eight countries Comment: The line chart above illustrates the Life expectancy at birth measured in countries namely Burundi, Uganda, Rwanda, Nigeria, El Salvador, Slovenia, United Arab Emirates, and Qatar Overall, it is obvious that countries shared the same upward pattern in the period of 32 years Before 1999, the trend LEAB of countries showed several differences, especially in Rwanda with a drop and bottomed out in 1993 However, in the next period of 18 years, except for a sharp rise of 15 years in Rwanda, the other nations witnessed a gradual increase in LEAB ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 18 It can clearly be seen that the chart is divided into groups: Rwanda, Nigeria, Burundi and Uganda together scattering at the lower range while Slovenia, El Salvador, Qatar, the United Arab Emirates experienced higher ratio range The first group is indeed developing countries with the GNI per capita from $ 260 to $2,880 while those in the second one are developed countries with income per capita ranging from $3,430 to $75,150 Time Series Output comparison: Linear model (5) Quadratic model (3) Uganda, Qatar, Nigeria, Slovenia, Rwanda United Arab Emirates, Burundi, El Salvador Figure 34 Table of trend model pursued by eight countries From time series prediction, we can see that there are five countries following the Linear model: Uganda, Qatar, Nigeria, Slovenia, Rwanda The rest pursue the Quadratic model Additionally, based on the analysis of time series, the Linear model is the one having the smallest MAD and SSE values indicating its highest probability in giving the closest value out of the three models Furthermore, in some countries, R-squared values are very high showing that the results scattering extremely close to the trendlines, hence giving more accurate predictions Specifically, Qatar’s linear model has the R Square of 98.96% while also owning the smallest MAD and SSE of 0.038 and 0.005 respectively Therefore, we choose its Linear trend model as the best Life expectancy at birth predictor PART 5: OVERALL TEAM CONCLUSION After conducting various studies on LEAB, we figure that its connection with income (or GNI per capita), though positive, is not substantially strong In fact, PPP and BWDS are two more prominent determinations for life expectancy Additionally, by using a linear model we predict that life expectancy at birth in 2025 will be 79.253 years Optimistic signs shown in our previous findings also make us believe the United Nations can attain its goal of increasing the longevity in low-income countries by 2030 From various regression models of countries across different income ranges, GNI per capita appears to have no prominent correlation with LEAB Although it is a significant variable in HI countries’ multiple regression model, the R-squared of 0.3647 indicates a weak relationship as only 36.47% of the change in LEAB is related to the variation in income USA, one of the wealthiest countries, is an example attaining GNI ranked at the th place but its LEAB is only at 53th globally (Geoba.se 2018 & World Data 2018) ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 19 Meanwhile, PPP and BWDS are two significant variables of the regression model in all countries owning a high R-squared (0.8239) This means almost 83% of changes in LEAB depends on the variations of these two factors Additionally, in MI nations, BWDS is considered as a key independent variable with 75.42% in relation According to WHO (2018), having access to standardized water source will partly contribute to smaller chances of getting chronic diseases Moreover, subsidies from the authorities for the improvement of healthcare services will result in longer citizens’ lifespan (Rahman, Khanam & Rahman 2018) In part 4, we have found that Qatar’s linear model would be the best predictor for the global life expectancy at birth since it owns the lowest degree of errors with MAD and SSE of 0,038 and 0,005 respectively Furthermore, this model has a significant r-squared (98,96%) meaning the value scatters very close to the linear trendline Hence, the result of this equation will be more approximate to real life expectancy in 2025 and 2030 particularly:  Life expectancy at birth in 2025 = = 74,21 + 0,12341= 79,253 (years)  Life expectancy at birth in 2030 = = 74,21 + 0,12346=79,868 (years) In addition to the increasing general longevity in 2030 up to 80 years as calculated above, the graph in part also implies that despite living in poor countries namely Uganda, Rwanda, and Burundi, average life expectancy tends to increase over the years These two optimistic signs promise a positive future of people living longer in low-income regions Furthermore, Dubinsky (2017) also praised the rapid improvements of life expectancy in low-income nations, which was essentially thanks to the global effort and sponsors that helped enhancing healthcare services and living conditions like providing qualified water source or decreasing infectious diseases and starvation for people in such countries In conclusion, by using multiple regression and R-squared values, we can illustrate a positive relationship between life expectancy and two variables including drinking water and public healthcare services in which improvements of such factors will help to prolong the population’s lifespan Furthermore, the United Nations and countries globally are striving to better the overall living standards and embrace the importance of helping people living in low-income regions Such commitment in attaining the Good Health and Well-being goals of ensuring the overall health of the population (United Nations 2018) make the goal of increasing life expectancy in poor countries by 2030 become attainable ECON1193 – TEAM ASSIGNMENT TEAM – SG-G04 – LIFE EXPECTANCY P a g e | 20 PART 6: REFERENCE LIST Dubinsky, K 2017, Life expectancy in low-income countries on the rise, The Borgen Project, viewed 11 May 2019, Geoba.se 2018, The World: Life Expectancy (2018) – Top 100+, Geoba.se, viewed May 2019, Gordon, L & Biciunaite, A 2014, ‘Economic Growth and Life Expectancy – Do Wealthier Countries Live Longer?’, Euromonitor, blog post, 14 March, viewed May 2019, Queensland University of Technology 2015, 'Unclean water supply may contribute to lower life expectancy in remote Australia’, PhysORG, 14th December, viewed 10th May 2019, Rahman, M, Khanam, R & Rahman, M 2018, ‘Health care expenditure and health outcome nexus: new evidence from the SAARC-ASEAN region’, NCBI, Global Health, 22 November, viewed May 2019, United Nations 2018, Impressive African health gains at risk from changing trends: WHO report, United Nations, viewed 11 May 2019, US National Library of Medicine National Institutes of Health 2012, The effects of public and private health care expenditure on health status in sub-Saharan Africa: new evidence from panel data analysis, view 10th May 2019,