tiểu luận kinh tế lượng the impact of air pollution indicators and GDP per capita on human’s life expectancy in 2015

TRƯỜNG ĐẠI HỌC NGOẠI THƯƠNG    ESSAY ECONOMETRICS The impact of air pollution indicators and GDP per capita on human’s life expectancy in 2015 Class: KTEE218 (1-1718).1_LT Lecturer: PhD Vũ Thị Phương Mai Group 10 members Ngô Minh Ngọc Student ID 1614450039 Đỗ Minh Ngọc 1614450038 Nguyễn Hồng Nhung 1614450041 Trần Lâm Oanh 1614450043 Hanoi , 2017 INDEX Abstract THEORETICAL FRAMEWORK 1.1 Life expectancy 1.1.1 Definition 1.1.2 Measure 1.1.3 Situation 1.2 GDP (Gross Domestic Product) and GDP per capita 1.2.1 Definition 1.2.2 Components 1.2.3 The effect of GDP per capita on life expectancy: 1.3 Air pollution 1.3.1 Defintion of pollution 1.3.2 Definition of air pollution 1.3.3 The situation of air pollution 1.3.4 The effect of air pollution on life expectancy 10 BUILD THE MODEL 12 2.1 Methodology of the study 12 2.2 Theoretical modeling 12 2.2.1 Determine the model type 12 2.2.2 Explain the variables 12 2.3 Describe the data 13 2.3.1 Data source 13 2.3.2 Statistical description 13 2.3.3 Correlation matrix between variables 14 3ESTABLISHMENT AND STATISTICAL DIMENSION 16 3.1 Estimate model 16 3.2 Hypothesis Testing 16 3.2.1 Are the results consistent with the theory? 16 3.2.2 Are the regression coefficients statistically significant? 17 3.2.3 Validation of model 17 3.3 Recommendations, Solutions 18 CONCLUSION 20 REFERENCES 21 GUIDELINES FOR USING MODEL 21 APPENDIX – DATA TABLE (Source: World Bank 2015) 22 Abstract Life expectancy is regarded as one of many noteworthy indicators in measuring a nation’s well-being Researches in the last decades have presented several factors that make great impacts on human’s life duration In the context of highly development in globalization, industrialization, and the like, GDP per capita and air pollution have proven to possess significant influence It is unavoidable that industrialization and modernization cause environmental pollution Air pollution, as a part of it, brings about negative effects on human’s health Recently, the air pollution index has witnessed a dramatic increase As a result, the number of people catching respiratory diseases rose and the expectation of life changed Never before has the need of profound insight about the relationship between air pollution and life duration been more essential In addition, another reason for the change in life expectancy is the fluctuation of GDP per capita Although constant efforts of government in health care service are beyond doubt, healthcare benefits have not yet reached every single individual On the basis of variation in personal income, different individuals possess different capabilities of affording medical care As a consequence, there exist gaps in people’s mortal ages, which make impacts on average longevity as a whole This means that GDP per capita plays a decisive part in affecting human life expectancy Due to justifications mentioned above, it is imperative for us to arm ourselves with more profound understanding about the influence of air pollution and GDP per capita to average life expectancy In hope of providing a deeper insight, scrutinizing a specific case as well as finding the most ultimate and radical solutions, the editorial group would like to take the topic “The impact of air pollution indicators and GDP per capita on human’s life expectancy in 2015” in thorough consideration This essay aims at evaluating the impact of GDP per capita and air pollution levels of 180 random nations all over the world In the end, we are bound to achieve an objective look into the issue as well as apply appropriate measures to make progress in practicing health care tasks This essay includes the following content:    Abstract Literature review Research results: Chapter 1: Theoretical framework Chapter 2: Building model Chapter 3: Estimation result and statistical inference Last but not least, due to the limited amount of time as well as some certain limits in understanding and data collecting, the essay may hardly avoid mistakes The editorial group is always willing to receive feedback from readers so as to complete the essay to the fullest Many thanks! THEORETICAL FRAMEWORK 1.1 Life expectancy 1.1.1 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 1.1.2 Measure 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 1.1.3 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 Graph 1: Human life expectancy at birth, measured by region, between 1950 and 2050 1.2 GDP (Gross Domestic Product) and GDP per capita 1.2.1 Definition Gross domestic product (GDP) is the monetary value of all the finished goods and services produced within a country's borders in a specific time period Per capita GDP is a measure of the total output of a country that takes gross domestic product (GDP) and divides it by the number of people in the country 1.2.2 Components GDP (Y) is the sum of consumption (C), investment (I), government spending (G) and net exports (X – M) Y=C+I+G+(X−M) 1.2.3 The effect of GDP per capita on life expectancy: Graph 2: Plot of life expectancy vs GDP per capita in 2009 The data for this graph is available from the Index Mundi website The data is from 2003 The graph shows that life expectancy at birth, increases at a decreasing rate with respect to GDP per capita (PPP) The main reason for this non-linear relationship is because people consume both needs and wants People consume needs in order to survive Once a person’s needs are satisfied, they could then spend the rest of their money on non-necessities If everyone’s needs are satisfied, then any increase in GDP per capita would barely affect life expectancy GDP per capita isn't the only thing that affects life expectancy Government intervention can also affect it A nation could be rich, but if its government ignores the plight of the poor, it could lower the life expectancy Another reason for the wide variation in the life expectancies for countries with low GDP per capita would be due to the level of non-market economic activity For example, if there is a lot of subsistence farming, people could be working and have enough food to eat, but wouldn’t be contributing much to the nation’s GDP because they wouldn’t be buying the food they eat, or selling the food they grow i.e no exchange of money The relationship between life expectancy and GDP per capita is strong enough to be the basis of a regression model Simple functions that increase at a decreasing rate include multiplicative (hyperbolas) and logarithmic functions 1.3 Air pollution 1.3.1 Defintion of pollution Pollution is the introduction of contaminants into the natural environment that causes adverse change Pollution can take the form of chemical substances or energy, such noise, heat or light Pollutants, the components of pollution, can be either foreign substances/energies or naturally occuring contaminants Pollution is often classed as point source or nonpoint source pollution A point source of pollution is a single indentifiable source of air, water, thermal, noise or light pollution A point source has negligible extent, distinguishing it from other pollution source geometries The sources are called point sources because in mathematical modeling, they can be approximated as a mathematical point to simplify analysis Pollution point sources are identical to other physics engineering, optics, and chemistry point sources and include:        Air pollution from an industrial source Water pollution from an oil refinery wastewater discharge outlet Noise pollution form a jet engine Disruptive seismic vibration from a localized seismic study Light pollution from an intrusive street light Thermal pollution from an industrial process outfall Radio emissions from an interference-producing electrical device Nonpoint source (NPS) pollution is a term used to describe pollution resulting from many diffuse sources, in direct contrast to point source pollution which results from a single source Nonpoint source pollution generally results from land runoff, precipitation, atmospheric deposition, drainage, seepage, or hydrological modification (rainfall or snowmelt) where tracing the pollution back to a single source is difficult 1.3.2 Definition of air pollution Air pollution occurs when harmful substances including particulates and biological molecules are introduced into Earth's atmosphere It may cause diseases, allergies or death of humans; it may also cause harm to other living organisms such as animals and food crops, and may damage the natural or built environment Human activity and natural processes can both generate air pollution 1.3.3 The situation of air pollution Air pollution is a significant risk factor for a number of pollution-related diseases and health conditions including respiratory infections, heart disease, stroke and lung cancer The health effects caused by air pollution may include difficulty in breathing, wheezing, coughing, asthma and worsening of existing respiratory and cardiac conditions These effects can result in increased medication use, increased doctor or emergency room visits, more hospital admissions and premature death The human health effects of poor air quality are far reaching, but principally affect the body's respiratory system and the cardiovascular system The most common sources of air pollution include particulates, ozone, nitrogen dioxide, and sulphur dioxide Children aged less than five years that live in developing countries are the most vulnerable population in terms of total deaths attributable to indoor and outdoor air pollution The World Health Organization estimated in 2014 that every year air pollution causes the premature death of some million people worldwide India has the highest death rate due to air pollution India also has more deaths from asthma than any other nation according to the World Health Organization In December 2013 air pollution was estimated to kill 500,000 people in China each year There is a positive correlation between pneumonia-related deaths and air pollution from motor vehicle emissions Annual premature European deaths caused by air pollution are estimated at 430,000 An important cause of these deaths is nitrogen dioxide and other nitrogen oxides emitted by road vehicles In a 2015 consultation document the UK government disclosed that nitrogen dioxide is responsible for 23,500 premature UK deaths per annum Across the European Union, air pollution is estimated to reduce life expectancy by almost nine months Causes of deaths include strokes, heart disease, COPD, lung cancer, and lung infections Urban outdoor air pollution is estimated to cause 1.3 million deaths worldwide per year Children are particularly at risk due to the immaturity of their respiratory organ systems Air pollution costs the world economy $5 trillion per year as a result of productivity losses and degraded quality of life, according to a joint study by the World Bank and the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.These productivity losses are caused by deaths due to diseases caused by air pollution One out of ten deaths in 2013 was caused by diseases associated with air pollution and the problem is getting worse The problem is even more acute in the developing world "Children under age in lower-income countries are more than 60 times as likely to die from exposure to air pollution as children in high-income countries." The report states that additional economic losses caused by air pollution, including health costs and the adverse effect on agricultural and other productivity were not calculated in the report, and thus the actual costs to the world economy are far higher than $5 trillion The air pollution effects have been becoming more and more alarming year by year Governments and individuals should take drastic measures to mitigate this global challenge 1.3.4 The effect of air pollution on life expectancy 10 BUILD THE MODEL 2.1 Methodology of the study  First of all, our group sets the hypothesis for the research question, "The impact of air pollution indicators and GDP per capita on human’s life expectancy in 2015." Why should this stage require a full review of the scope, nature, substance, environment and condition of the subject, and relationships in the process of mobilization?  Build econometric models: From theoretical basis to the mathematical model and statistical models to find out the most suitable one  The team collected sample and estimated values based on data from 180 observations in 2015 from 180 countries For quantitative results, the number of outputs should be equal to the number of inputs, which is the data collected by the statistical method As a result, the team selected information and checked the statistical significance of the regression coefficients and the suitability of the model based on the observed observations comparing with the previous research and similar studies, to find the best results to use for analysis  During the course of the project, the team used the knowledge of econometrics and macroeconomics, quantitative methods with the main support of STATA software, Microsoft Excel, Microsoft Word for synthesis and completion of this essay 2.2 Theoretical modeling In order to construct an econometric model, it is first necessary to identify the factors that are involved in the interaction and description of economic variables In order to obtain the results of the computation and analysis of the output, the statistical method 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 a function with specific numerical, computational, and differential values such as derivatives, differential and direct meaning analysis 2.2.1 Determine the model type Dependent variable: LE Independent variable includes variables: GPC, AP Regression model: LE = + 1.GPC + 2.AP + 2.2.2 Explain the variables 12 Variables Meaning Unit LE Life expectancy year GPC GDP per capita USD AP Air pollution PM2.5, mean annual exposure microgram per cubic meter Based on the sample regression function, the slope indicated in the sample, when the independent variables GPC and AP were simultaneously 0, the mean dependent variable LE was ̂̂ β ̂̂ term shows in the sample, where the independent The slope β i variables change by one unit, the mean dependent variable varies βi unit ̂̂ 2.3 Describe the data 2.3.1 Data source Variables LE GPC AP Link https://data.worldbank.org/indicator/SP.DYN.LE00.IN https://data.worldbank.org/indicator/NY.GDP.PCAP.CD?end=2015&start=1960 https://data.worldbank.org/indicator/EN.ATM.PM25.MC.M3 2.3.2 Statistical description Before analyzing the data, the team will describe the data to give the reader the most general view of the collected data sets This explains some of the errors encountered when running the model due to a data error As stated in the theoretical part, the data set consists of three variables The group will then provide a description of each variable in the model Describing data using des, we obtain the following results: des Contains data from C:\Users\Laptop Hp\Desktop\KTL1 GK\Data World Bank 2015.dta obs: vars: size: 180 Dec 2017 21:18 1,260 storage display value variable name type format label gpc float %8.0g GPC ap le int byte %8.0g %8.0g AP LE variable label Sorted by: 13 Continue using the sum statement to describe the data, the “sum” command shows the number of observations (Obs), the average value (Mean), standard deviation (Std.dev.) as well as the maximum value (Max) and the minimum value (Min) of the variables sum le gpc ap Variable Obs Mean le 180 71.53333 gpc ap 180 180 12571.48 28.34444 Std Dev Min Max 8.035814 51 84 17599.25 19.77875 303.7 101909.8 107  The standard deviation of variable LE is 8.035814 It can be seen that data with relatively high standard deviation, high level of dispersion, show that 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 60-70 years  The standard deviation of variable GPC is 17599.25 We can see that the data has a very high standard deviation, which shows that the gap in average income between countries is very large This is understandable because there is a marked difference in the level of economic development among nations GDP per capita income of the Americas and Europe is often much higher than that of Asian and 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, Africa (eg, Qatar: 107, Buhtan: 56, India: 74, Nigeria: 38, Ethiopia: 36), whereas in developed countries in Europe and America, pollution levels are very low (USA: 8, Australia: 6, Sweden: 13, New Zealand: 6) 2.3.3 Correlation matrix between variables Before running the regression model, we consider the correlation between variables using the “corr” command We obtained the correlation table between the variables as follows: corr le gpc ap (obs=180) le le 1.0000 gpc ap 0.6124 -0.3224 gpc ap 1.0000 -0.2312 1.0000 14 Conclusion: The variable GPC correlates with LE relatively high The GPC variable has a negative correlation coefficient, indicating the opposite effect on the dependent variable The AP variable has a positive correlation coefficient, which shows the same effect on the dependent variable The correlation coefficient between the LE and the GPC is 0.6124, showing that each positive relationship, ie, as the average income increases, the average life expectancy is also increasing, because when income increases, People will pay more for health care and health care than before, and as gross national income increases, government will spend more on public services, especially health services and environmental protection, thereby limiting the cancer and other health problems to people, improving life expectancy The correlation coefficient between LE and GPC was high (0.6124) indicating that each correlation was quite close between average income and life expectancy The correlation coefficient between LE and AP is -0.3224 indicating the inverse relationship, ie when the level of pollution increases, the average life expectancy decreases This is true because the greater the pollution, the higher the cancer rate, leading to higher mortality, lower life expectancy In the developing world, the level of pollution is always high, plus the low level of health care services is the main reason for the low life expectancy in these countries The correlation coefficient between GPC and AP is -0.2312, indicating the opposite relationship, as the average income increases, the level of pollution increases as well, which represents the tradeoff between industrial development and environmental pollution The relatively small correlation coefficient (-0.1878) indicates that the correlation between the two variables is relatively small (loosely), because there are many other causes of environmental pollution From the above analysis, it can be seen that independent variables correlate with the dependent variable, and between the independent variables also correlate Furthermore, no correlation coefficients greater than 0.8 were observed, so this model was not affected by multi-collinearity ESTABLISHMENT AND STATISTICAL DIMENSION 3.1 Estimate model To run the regression model, we execute the “regress” command as follows: regress le gpc ap Source Model Residual Total le gpc ap _cons SS 4733.59357 6825.20643 11558.8 Coef df MS Number of obs = 180 F(2, 177) = 61.38 Prob > F = 0.0000 R-squared = 0.4095 Adj R-squared = 0.4029 179 64.5743017 Root MSE = 6.2097 Std Err t P>|t| [95% Conf Interval] 2366.79679 177 38.5604883 0002594 0000271 9.57 0.000 0002059 0003129 -.0776155 70.47188 0241202 9515858 -3.22 74.06 0.002 0.000 -.1252156 68.59397 -.0300154 72.34979 From the above table, we have the sample regression equation: LE = 70.47188 + 0.0002594 GPC – 0.0776155 AP The coefficient of determination: R = 0.4095 means that the independent variables in the model account for 40.95% of the variation in the value of the dependent variable and the remaining depends on other factors Meaning of estimation coefficients: = 0.0002594 > means that when the GPC increases to 1$ USD, the human’s life expectancy will increase to 0.02594% holding other factors constant = -0.0776155 < means that when air pollution level increases to microgram per cubic meter, the human’s life expectancy will decrease 7.76155% holding other factors constant 3.2 Hypothesis Testing 3.2.1 Are the results consistent with the theory?  We can see that following the theory, we have: - If the GDP per capita increases, the human’s life expectancy will increase - If the air pollution increases, the human’s life expectancy will decrease  Follow the analysis, β1 > 0, β2 < Therefore, all the results are suitable with the theory Problem 1: With a significance level of α = 5%, is the regression coefficient of the GPC variable actually greater than 0? Hypothetical pair: H0: βGPC ≤ 16 H1: βGPC> p-value (βGPC) = 5.54 x 10-7