tiểu luận kinh tế lượng tài chính factors affecting the human development index over the world in 2017

FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS  ECONOMETRICS REPORT Factors affecting the Human Development Index over the world in 2017 Group: Trinh Thi Hong Nhung – ID: 1813340049 Nguyen Hoang Ha – ID: 1813340023 Vu Phuong Linh – ID: 1813340036 Pham My Duyen – ID: 1813340019 Nguyen Binh An – ID: 1713340002 Dao Minh Hoa – ID: 1212340030 Class: KTEE301.1 (21/10-15/12/2019) Instructors: Dr Nguyen Thuy Quynh Hanoi, December 2019 FOREIGN TRADE UNIVERSITY FACULTY OF INTERNATIONAL ECONOMICS  ECONOMETRICS REPORT Factors affecting the Human Development Index over the world in 2017 Group Trinh Thi Hong Nhung – ID: 1813340049 Nguyen Hoang Ha – ID: 1813340023 Vu Phuong Linh – ID: 1813340036 Pham My Duyen – ID: 1813340019 Nguyen Binh An – ID: 1713340002 Dao Minh Hoa – ID: 1212340030 Class: KTEE301.1 (21/10-15/12/2019) Instructors: Dr Nguyen Thuy Quynh Hanoi, December 2019 TABLE OF CONTENT ABSTRACT We are living in a complex world People, nations and economies are more connected than ever, and so are the global development issues we are facing These issues span borders, straddle social, economic and environmental realms, and can be persisting or recurring From urbanization to the creation of jobs for millions of people, the world’s challenges will only be solved using approaches that take both complexity and local context into account For almost thirty years, UNDP’s human development approach—with its emphasis on enlarging people’s freedoms and opportunities rather than economic growth —has inspired and informed solutions and policies across the world In hope of providing a deeper insight, scrutinizing a specific case, our group would like to take the topic “Factors affecting the Human Development Index over the world in 2017” in thorough consideration This report investigates the determinants of Human Development Index in 120 countries, employing the methods of panel data analysis Due to the limited of data resources, we can only pick up a few prominent factors of those countries in 2017, which are Life expectancy at birth (LE), Expected years of schooling (EYS) - mean years of school, Gross national income (GNI) per capita, Inflation rate (INF) and Fertility rate (FER) Our research indicates that the relationship between human development index and three factors which are GNI per capita, inflation rate (INF), fertility rate (FER) is negative A reverse tendency could be observed in the relationship between HDI and life expectancy at birth (LE), expected years of school (EYS), as they are positive INTRODUCTION Econometrics is the quantitative application of statistical and mathematical models using data to develop theories or test existing hypothesis in economics and to forecast future trends from historical data It subjects real-world data to statistical trials and then compares and contrasts the results against the theory or theories being tested Depending on whether you are interested in testing an existing theory or in using existing data to develop a new hypothesis based on those observations, econometrics can be subdivided into two major categories: theoretical and applied Those who routinely engage in this practice are commonly known as econometricians In the report, we will use the econometric model to find out the relationship between by using collected data from World Bank, UNDP and others sources, whether they have positive or negative relationship And from the result, we may have some recommendations to countries with lower human development index We recognize the important of econometrics in social economics In order to understand how the econometrics works in real life and to apply econometrics effectively and correctly, our group would like to develop a report under the guidance of Dr Nguyen Thuy Quynh In this report, we used the econometrics analysis tool Stata to analyze the topic “Factors affecting the Human Development Index over the world in 2017” The report contains the following contents: • SECTION 1: OVERVIEW OF THE TOPIC • SECTION 2: MODEL SPECIFICATION • SECTION 3: ESTIMATED MODEL AND STATISTICAL INFERENCES • CONCLUSION • APPENDIX • INDIVIDUAL ASSESSMENT During the process of making this report, due to the limited amount of time as well as some certain limits in understanding and data collecting, despite all the efforts, the report may hardly avoid mistakes We are always willing to receive your comments so that our group can improve and complete this report Many thanks! SECTION 1 OVERVIEW OF THE TOPIC The Human Development Index (HDI) The Human Development Index (HDI) is a composite statistic of life expectancy, education, and per capita income indicators, which are used to rank countries into four tiers of human development A country scores higher HDI when the lifespan is higher, the education level is higher, the GDP per capita is higher, the fertility rate is lower, and the inflation rate is lower 1.1 HDI stands for Human Development Index It was developed and launched by Pakistani economist Mahbub-ul-Haq, followed by Amartya Sen, an Indian economist, in 1990 Human Development Index, HDI, is a comprehensive tool devised by the United Nations for measuring the levels of social and economic developments of the different countries and ranking them accordingly It is a comparative measure of life expectancy, education, literacy, and standard of living Essentially, Human Development Index, HDI, makes use of four parameters for measuring and ranking countries according to their social and economic development which includes the Life Expectancy at Birth, Expected Years of Schooling, Mean Years of Schooling and Gross National Income per Capita There are two steps to calculating the HDI: 1.1.1 Forming indices for each of the four metrics The values of each of the four metrics are first normalized to an index value of to To this, “goalposts” of the maximum and minimum limits on each metrics are set by the UNDP, as shown in the table With the actual value for a given country, and the global maximum and minimum, the dimension (indices) value for each metric is calculated as: The dimension index is therefore in a country that achieves the maximum value and it is for a country that is at the minimum value 1.1.2 Aggregating the four metrics to produce the HDI Once each of the individual indices has been calculated, they are aggregated to calculate the HDI The HDI is calculated as the geometric mean (equally-weighted) of life expectancy, education, and GNI per capita, as follows: The education dimension is the arithmetic mean of the two education indices (mean years of schooling and expected years of schooling) 1.2 Economic theories The main purpose of our group’s research is to determine the factors which affect the fluctuation of The Human Development Index (HDI) However, we will mainly focus on the long-term relationship between those factors and HDI According to previously published researches, some long-term factors substantially affecting HDI are Life expectancy at birth, expected years of schooling, Gross national income (GNI) per capita, Inflation, and Fertility 1.2.1 The effect of Life expectancy at birth on HDI Life expectancy at birth (years) is the average number of years a newborn child would live if current mortality patterns were to stay the same According to Max Roser in an article on “Our World in Data” website, the first component of the HDI – a long and healthy life – is measured by life expectancy Long-run estimates of life expectancy across the world are shown in the visualization For countries where historical records are available, such as the UK, estimates can extend as far back as 1543 – click on the UK to see this long-run perspective Global and regional estimates extend back to the year 1770 This dataset is based on a combination of data from the Clio Infra project, the UN Population Division, and global and estimates for world regions from James Riley (2005) 1.2.2 The effect of Expected years of schooling on HDI The second component – access to education – is measured by expected years of schooling of children at school-entry age and mean years of schooling of the adult population Education has been one of the most integral drivers and outcomes of global development The provision of education is now viewed in most parts of the world as a basic right – with pressure on governments to ensure a high-quality education for all Education should have a positive effect on HDI because as education increases so does the knowledge of how to lead a healthier life This knowledge might, for example, take the form of improved nutrition or reduced exposure to various health risks, such as indoor pollution exposures Education is measured by the education index In this analysis, the Total literacy rate has been taken as a proxy of Education index There are many metrics we can use to assess education access, quality, and attainment – we cover many of them throughout our work on education The visualizations present the two metrics that the HDI captures: - Mean years of schooling estimates the average number of years of total schooling adults aged 25 years and older have received This data extends back to the year 1870 and is based on the combination of data from Lee and Lee (2016); Barro-Lee (2018); and the UN Development Programme - Expected years of schooling measures the number of years of schooling that a child of school entrance age can expect to receive if the current age-specific enrollment rates persist throughout the child’s life by country 1.2.3 The effect of Gross national income (GNI) per capita on HDI The architects of the HDI have decided to add a third dimension – a decent standard of living – and to measure it by Gross National Income per capita GNI is expected to be positively related to HDI for diverse reasons GNI displays disposable income As disposable income increases, people have more resources for better shelter, food, and medical care Again, countrywide data might offer some advantages over individual data: a wealthy person living in a poor country is unlikely to have the same access to quality food and medicine as a wealthy person living in a wealthy country Since income is highly correlated with many other categories that would affect HDI (e.g education, life expectancy), it is also held constant to estimate, without bias, the specific effects of those variables For most of human history, our ancestors were stuck in a world of poor health, hunger and little access to formal education Economic growth – particularly over the past few centuries – has allowed some part of the world population to break out of these conditions The map shows the GNI per capita - this is the metric that the HDI relies on: 1.2.4 The effect of Fertility on HDI Total Fertility rate (Children per woman): The number of children that would be born to each woman with prevailing age-specific fertility rates where ASBR is each five-year age-specific birth rate defined as where Bx is the number of live births to mothers age x and Px is the number of resident women age x The higher the fertility is, the higher the population expected can get It might result in a shortage of sources of food and drinks, occupations, education services, accommodations, etc, which decreases HDI 1.2.5 The effect of Inflation on HDI Inflation (annual %): Inflation, as measured by the annual growth rate of the GDP implicit deflator, shows the rate of price change in the economy as a whole The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency Therefore, the results of the regression model excepted Inflation rate are consistent with the economic theories In fact, an increase in Life expectancy does have an impact on Human Development index (HDI) As the number of years a newborn infant could expect to live if prevailing patterns of age-specific mortality rates at the time of birth stay the same throughout will result in an increase in the final calculation of the human development index Furthermore, an increase in the average number of years of education received by people ages 25 and older, converted from education attainment levels using official durations - Expected years of schooling, will also lead to an increase in the human development index On the other hand, there is always a trade-off between the sum of value added by all resident producers plus any product taxes (GNI per capita) and the human development index When the GNI per capita increase, there will be a decline in the human development index Also, when a country experienced higher Inflation rate - a sustained increase in the general price level of goods and services in an economy over a period of time, it will lead to a fall in a country’s potential for individual human development Last but not least, Fertility rate also has a negative impact on the human development index as Specifically, when the number of offspring born per mating pair, individual or population increase, it will lead to a decline in people and their capabilities while assessing the development of a country Recommendations 6.1 Recommendations to increase the expected years of schooling and improve the education system The first and foremost step is to be clear on what education needs to achieve Much of education in developing countries is designed to test students and act as a flawed classification mechanism of students into categories which decide their future Whereas, it should be a means of development The second is to improve the quality of teachers Teachers are the backbone of the education system No policy, no curriculum and no money can achieve anything without capable and enthusiastic teachers Teacher training needs to be improved and special emphasis needs to be given to effective teacher-student interaction, apart from subject knowledge The third would be to assess public school infrastructure, and invest in improving it The amount of money allocated for education must be spent wisely to build schools The fourth step is to encourage people from local communities to become teachers A big problem in developing countries is that educated people seek opportunity only in the cities or in places more advanced than their hometowns If people who have an incentive to stay back in their home towns are encouraged to teach, we can solve the problem of lack of quality teachers The government also needs to ensure basic needs for the school and for the safety and hygiene of the students and staff are fulfilled The fifth is to revise curricula and teaching practices such that they inspire independent thought and help students dream Students must also get exposed to many different things in school, so that they can choose the right career path based on interest and not parental pressured education system 6.2 Recommendations to increase the life expectancy The low life expectancy of developing populations does not mean that elderly people are absent from developing populations Since poor countries tend to have much higher infant death rates, this situation lowers the average life expectancy In order to increase the life expectancy, government and policy-makers should pay attention to increase the healthcare system The fact that the patient is the most important person in a medical care system must be recognised by all those who work in the system This single factor makes a significant difference to the patient care in any hospital In developing countries, financial constraints often lead to compromised quality of care This can be corrected by the introduction of management systems that emphasise cost recovery Some of the issues that need to be addressed to improve patient care are: • Trained Personnel: A well-trained team is critical to providing high quality care with desirable outcomes Lack of adequate personnel and lack of adequate training facilities for the available personnel are major problems The temptation to recruit untrained or poorly trained people should be resisted The number of training programmes must be increased, and the existing programmes must be improved Making a uniform basic curriculum available for all training institutions/programmes should help bring about standardisation • Equipment: All the necessary equipment must be in place and properly maintained This is vital to the performance of the medical system and contributes significantly to better results Eye-care equipment of acceptable standards is now available at reasonable prices, and this must be accompanied by appropriate maintenance systems • Use of Proper Instruments: Good quality instruments are now available at lower costs With the development of proper inventory control systems for a given operation, the costs can be lowered • Use of Appropriate Medications: Access to low cost medicines is an absolute necessity for appropriate care • Use of Newer Technologies: It is important to continually employ newer technologies that improve the quality of care Of course, this must be done with reference to cost-efficiencies Recommendations to decrease inflation rate Regarding the inflation rate, the primary policy for reducing inflation is monetary policy – in particular, raising interest rates reduces demand and helps to bring inflation under control Other policies to reduce inflation can include tight fiscal policy (higher tax), supply-side policies, wage control, appreciation in the exchange rate and control of the money supply CONCLUSION To sum up, our research has examined the statistically dependent relationship of Human Development Index on Life Expectancy, Inflation,Fertility, Expected years schooling and the Gross National Income per capita The results obtained after this research are consistent with the economic theories and some previous published researches Specificially: - There’s a positive impact of Life Expectancy, Expected years of schooling and GNI on Human development index As those factors increase, unemployment rate would increase followingly - In contrast, Inflation and Fertility have negative relationship with Human development index If population increases, unemployment rate would decrease followingly The report was completed by the whole group’s effort and the knowledge that we have studied at class Despite of our lack of knowledge and collecting data, we have tried our best and gained more knowledge along with understanding about the process of running the econometrics model, analyzing the model and learning the relationship between variables in the model, even though there are still lots of variables that needed to be analyzed to have a more overall look We would like to thank Dr Nguyen Thuy Quynh once again for your guidance and suggestions to help us finish the report in the right direction We still have many omissions and mistakes so that we would like to receive your comments to improve our report to the fullest REFERENCES Documents: Joko Sạngaji, 2016 “The Determinants of Human Development Index in Several Buddist Countries” Indonesia Publications Damodar N Gujarati, and Dawn C Porter, “Basic Econometrics”, 5th Edition El-Agrody, N M., Othman, A Z., & Hassan, M B.-D., 2010, “Economic Study of HDI in Egypt and Impacts on GDP” Smit Shah, National Institute of Bank Management, Pune India “Determinants of Human Development Index: A Cross-Country Empirical Analysis”, MPRA Marjetka Troha, 2015, “Impact of Life expectancy on Human development: The case of Slovenia” Gherardo Girardi, Senior Lecturer in economics, 2017, “The Human Development Index and life expectancy prior to birth” N Gregory Mankiw, “Principles of Macroeconomics”, 6th Edition Websites: Max Roser in an article on “Our World in Data”: https://ourworldindata.org/ World Bank Data: https://data.worldbank.org/indicator?tab=all UN Data: https://data.un.org/DocumentData.aspx? q=hdi&id=392&fbclid=IwAR1AkXXmV7sJ5iNWm3MWfRcZE4LFj9k1W4T7kpEF1LWwNVYuLTXr_S45s0 APPENDIX Do-file: STATA COMMANDS USED IN THE REPORT: gen lnGNI=ln(GNI) gen lnEYS=ln(EYS) Statistical description: sum HDI LE lnEYS lnGNI INF FER Correlation matrix: corr HDI LE lnEYS lnGNI INF FER Regression model: reg HDI LE lnEYS lnGNI INF FER Test for misspecification: vif Test for Heteroskedasticity: imtest, white Test for normal distribution of the disturbance: predict res, residual sktest res Outputs: 2.1 Statistical description 2.2 Correlation matrix 2.3 Regression model 2.4 Test for misspecification 2.5 Test for multicollinearity 2.6 Test for heteroskedasticity 2.7 Test for normal distribution of disturbance The dataset of 120 countries over the world in 2017: Obs 10 11 12 13 14 15 16 17 18 19 20 21 22 Country Name Afghanistan Albania Algeria Antigua and Barbuda Armenia Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Bhutan Bolivia (Plurinational State of) Bosnia and Herzegovina Botswana Brazil Bulgaria Cambodia Canada LE 64 78 76 76 74 83 81 72 75 77 72 76 73 81 70 69 77 67 75 74 69 82 EYS 10.4 GNI 1824 INF 4.98 FER 4.63 HDI 0.5 14.8 11886 1.99 1.64 0.79 14.4 13802 5.59 3.05 0.75 13.2 20764 2.43 0.78 9144 0.97 1.74 0.76 22.9 43560 1.95 1.75 0.94 16.1 45415 2.08 1.53 0.91 12.7 15600 12.9 1.9 0.76 12.8 26681 16 41580 1.52 1.39 1.77 2.03 0.81 0.85 3677 5.7 2.09 0.61 15.3 15843 4.66 1.61 0.8 15.5 16323 6.03 1.73 0.81 19.8 42156 2.13 1.68 0.92 12.3 8065 3.86 2.01 0.61 14 6714 2.82 2.83 0.69 14.2 11716 1.17 1.29 0.77 12.6 15534 3.31 2.94 0.72 15.4 13755 3.45 1.75 0.76 14.8 18740 2.06 1.54 0.81 11.7 3413 16.4 43433 2.89 1.6 2.56 1.5 0.58 0.93 13 11.4 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 Chile China Colombia Costa Rica Croatia Czechia Denmark Dominican Republic El Salvador Estonia Ethiopia Fiji France Gabon Gambia Germany Ghana Greece Grenada Guatemala Guinea Guinea-Bissau 79 76 74 80 77 78 80 74 73 77 65 70 82 66 61 81 63 81 73 73 60 57 16.4 21910 2.18 1.68 0.84 13.8 15270 1.59 1.68 0.75 14.4 12938 15.4 14636 4.31 1.63 1.83 1.77 0.75 0.79 15 22162 1.13 1.42 0.83 16.9 30588 1.15 1.63 0.89 19.1 47918 13.7 13921 1.15 3.28 1.79 2.37 0.93 0.74 12.6 6868 1.01 2.06 0.67 16.1 28993 3.42 1.6 0.87 8.5 1719 9.85 4.35 0.46 15.3 8324 3.35 2.79 0.74 16.4 39254 1.03 1.92 0.9 12.8 16431 2.65 4.01 0.7 1516 8.03 5.28 0.46 17 46136 11.6 4096 1.51 12.37 1.57 3.93 0.94 0.59 17.3 24648 1.12 1.38 0.87 16.9 12864 0.91 2.08 0.77 10.8 7278 4.42 2.92 0.65 9.1 2067 8.92 4.78 0.46 10.5 1552 1.36 4.55 0.46 9.2 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 Guyana Haiti Honduras Hong Kong, China (SAR) Hungary Iceland India Indonesia Italy Jamaica Jordan Kazakhstan Kenya Korea (Republic of) Kuwait Latvia Lebanon Lesotho Liberia Lithuania Luxembourg Madagascar Malawi 66 63 73 84 76 82 68 69 83 76 74 70 67 82 74 74 79 54 63 74 82 66 63 11.4 7447 1.9 2.49 0.65 9.3 1665 10.68 2.99 0.5 10.2 4215 3.93 2.5 0.62 16.3 58420 1.48 1.13 0.93 15.1 25393 2.35 1.53 0.84 19.3 45810 1.76 2.24 0.94 12.3 6353 2.49 2.24 0.64 12.8 10846 3.81 2.34 0.69 16.3 35299 1.23 1.34 0.88 13.1 7846 4.38 1.99 0.73 13.1 8288 15.1 22626 3.32 7.44 2.85 2.73 0.74 0.8 12.1 2961 8.01 3.57 0.59 16.5 35945 1.94 1.05 0.9 13.6 70524 2.17 2.08 0.8 15.8 25002 2.93 1.74 0.85 12.5 13378 4.32 2.1 0.76 3255 667 5.32 12.42 3.17 4.39 0.52 0.44 16.1 28314 14 65016 3.72 1.73 1.75 1.69 0.86 0.9 8.28 11.54 1.26 4.3 0.52 0.48 10.6 10 10.6 10.8 1358 1064 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 Malaysia Maldives Mali Malta Mauritania Mauritius Mexico Mongolia Montenegro Myanmar Namibia Nepal Netherlands New Zealand Nicaragua Niger Norway Oman Pakistan Panama Papua New Guinea Paraguay 75 77 58 81 63 74 77 69 77 66 64 70 82 82 75 60 82 77 66 78 65 73 13.7 26107 3.87 2.02 0.8 12.6 13567 2.82 1.91 0.72 7.7 1953 15.9 34396 1.76 1.36 5.97 1.37 0.43 0.88 3592 2.25 4.62 0.52 15.1 20189 3.67 1.44 0.79 14.1 16944 6.04 2.16 0.77 15.5 10103 4.05 2.91 0.74 14.9 16779 2.38 1.74 0.81 8.6 10 5567 4.57 2.17 0.58 12.3 9387 6.14 3.45 0.65 12.2 2471 18 47900 18.9 33970 3.63 1.38 1.85 1.97 1.66 1.81 0.57 0.93 0.92 12.1 5157 3.85 2.43 0.66 5.4 906 2.8 0.35 17.9 68012 1.88 1.71 0.95 13.9 36290 1.6 2.92 0.82 5311 4.09 3.56 0.56 12.7 19178 0.88 2.49 0.79 8.6 10 3403 5.42 3.61 0.54 12.7 8380 3.6 2.45 0.7 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 Peru Philippines Poland Portugal Romania Russian Federation Rwanda Samoa Sao Tome and Principe Senegal Serbia Seychelles Slovenia South Africa Spain Sri Lanka Sweden Tanzania (United Republic of) Tonga Trinidad and Tobago Tunisia 75 69 77 81 75 71 67 75 66 67 75 73 81 63 83 75 82 66 73 70 75 13.8 11789 2.8 2.28 0.75 9154 2.85 2.64 0.7 16.4 26150 2.08 1.39 0.87 16.3 27315 1.37 1.36 0.85 14.3 22646 1.34 1.64 0.81 15.5 24233 3.68 1.76 0.82 11.2 1811 8.28 4.09 0.52 12.5 5909 1.75 2.43 0.71 12.5 2941 5.69 4.37 0.59 9.7 2384 1.32 4.7 0.51 14.6 13019 3.13 1.46 0.79 14.8 26077 2.86 3.63 0.8 17.2 30594 1.43 1.58 0.9 13.3 11923 5.18 2.43 0.7 17.9 34258 1.96 1.34 0.89 13.9 11326 7.7 2.21 0.77 17.6 47766 1.79 1.85 0.93 12.6 8.9 2655 5.32 4.95 0.54 14.3 5547 7.44 3.6 0.73 12.9 28622 1.88 1.74 0.78 15.1 10275 5.31 2.22 0.74 111 112 113 114 115 116 117 118 119 120 Turkey 76 60 72 77 81 79 77 72 76 61 Uganda Ukraine United Arab Emirates United Kingdom United States Uruguay Vanuatu Viet Nam Zambia 15.2 24804 11.14 2.08 0.79 11.6 1658 5.21 5.1 0.52 15 8130 14.44 1.37 0.75 13.6 67805 1.97 1.45 0.86 17.4 39116 2.56 1.79 0.92 16.5 54941 2.13 1.77 0.92 15.9 19930 6.22 1.98 0.8 10.9 2995 3.08 3.82 0.6 12.7 5859 3.52 2.04 0.69 12.5 3557 6.58 4.72 0.59 INDIVIDUAL ASSESSMENT The individual assessment is based on each member’s attitude towards the group work 10 Hoang Ha 10 My Duyen 10 Phuong Linh 10 Minh Hoa Evaluator Hong Nhung Hong Nhung - Binh An 10 - 10 10 10 Hoang Ha 10 10 - 10 10 My Duyen 10 10 10 - 10 Phuong Linh 10 10 10 10 - Minh Hoa Average score 10 10 10 10 10 - 10 10 10 10 10 Binh An ... tool Stata to analyze the topic Factors affecting the Human Development Index over the world in 2017 The report contains the following contents: • SECTION 1: OVERVIEW OF THE TOPIC • SECTION 2:... hope of providing a deeper insight, scrutinizing a specific case, our group would like to take the topic Factors affecting the Human Development Index over the world in 2017 in thorough consideration... schooling increases, the Human Development index is expected to increase, and vice versa, holding other variables remain unchanged • When the GNI per capita increases, the Human Development index