factors affecting in human development index in 50 countries in the year 2019

The study uses a geometric approach to analyze and examine how the factors includinglife insurance volume, health-care expenditure, government expenditure on education,PM2.5 and unemploy

Reason for choosing the topic

In the concept of global economy witnessing ongoing remarkable changes, both developing and developed countries are considered to be stuck into a business cycle where they have at least experienced economic rise or recession, occasionally stability. This situation raised questions about national policy choices and required adequate empirical data to re-examine the accurate criteria for a country’s prosperity

For a half of century, Gross Domestic Product (GDP) served the only metric to track the progress of a nation, which means the essence of this evaluation method is not reliable enough to be applied to the practices GDP merely provided monetary measurement of income per capita, not well-being and socio-economic aspects Hence, an alternative geometric measure, Human Development Index (HDI), was needed to provide a more comprehensive picture of a country’s development that is predominantly based on individuals and their capabilities, not the economic growth alone.

Moreover, HDI is correlated with the five related to well-being and social problems including Life Insurance Volume to GDP (LIV), Health-care Expenditure to GDP (HE),PM2.5 (PMI), Government Expenditure on Education to GDP (GEE), andUnemployment rate (UNE) Acknowledging the importance of HDI, our team felt responsible for providing deeper insight through analysis and evaluation of this index and its relationship with the above indicators That is the reason why our team chose the topic “Factors affecting Human Development Index in 50 developing countries during the year 2021”.

Research objectives

In this topic, the study is aimed to analyze the Human Development Index (HDI) in 50 developing countries in the year 2021 and explain how the factors can affect in the variation of it The concept of development is multidimensional that entails various aspects of life such as knowledge and understanding, health, society, economy and security With more access to the resources and more choices people can make, a higher living standard and healthier lifestyle can be obtained in order to improve and develop a country.

Object and the scope of research

 Object of the research: the impacts of life insurance volume, health-care expenditure, government expenditure on education, PM2.5 and unemployment rate on Human Development Index in 50 developing countries.

 Scope of the research: 50 countries in the year 2021.

Research methodology

In order to achieve the examination and accurate measure of the effects of five factors in HDI, our team used the Ordinary Least Squares method (OLS) that has been taught in class and Microsoft Excel to sort data We also used STATA software to run the regression model and test the hypothesis.

Content and structure of the report

Our research report can be divided into 3 main parts:

Chapter 1: Overview of Human Development Index and its components

Chapter 2: Model Specification and Data analysis

Chapter 3: Estimated model, hypothesis testing and statistical inferences

During our preparation for investigating and collecting real-world data , due to the lack of experiences in running regression model and first-time practice in using STATA tool,our team could hardly avoid mistakes and deviation We are looking forward to receiving feedback from teacher in charge Nguyen Thuy Quynh in order to improve the quality of our report We would also send our best gratitude to Mrs Nguyen ThuyQuynh for her help and detailed guidance during Econometrics course.

OVERVIEW OF THE HUMAN DEVELOPMENT INDEX AND ITS COMPONENTS

Overview

1.1.1 Definition and Importance of HDI

The Human Development Index (HDI), according to the UNDP, is a concise indicator of average performance in significant human development dimensions:

 A long and healthy life – determined by life expectancy

 Education acquisition – determined by the median number of years spent in school for persons aged 25 and older and by the predicted years of schooling for youngsters starting school.

 A reasonable standard of living – determined by Gross National Income per capita.

The normalized indices for each of these three key dimensions' geometric means make up the HDI.

The term “human development” originally integrates the requirement for economic advancement Pay, however, does not encompass all of human existence The same can be said for well-being, education, real climate, and opportunity Pay development is vital Common rights and socio-eco-political chances should be embraced for human growth In view of the idea of human turn of events, the Human Development Index (HDI) is constructed It fills in as a more compassionate proportion of improvement than standard economic growth figures such as GNP or GDP.

The HDI is a measuring tool that assists in regularly assessing the financial situations of nations and additionally monitors something quite similar since it takes into account various limits to determine the progress of those places Yearly, each nation is granted a position by the UNDP A higher position is dispensed to the one that has performed well on the whole or the vast majority of the boundaries, meaning possessing a better standard of living, higher educational quality, and longer life expectancy Similarly, countries where poor people fare well on the whole or the greater part of the boundaries achieve a lower rank, which, in turn, call for immediate action

The HDI is used to alter the technique from generic financial insights to human outcomes and to demonstrate the consideration of strategy designers, the media, and non-legislative associations It was sent off to re-domain that individuals and their capability ought to be a definitive rule for deciding the nation’s turn of events, not financial development.

The Human Development Index helps with the analysis of public policy options and explains how two countries with comparable pay per person can have comparable salaries per person but possess different future and proficiency levels, to the point where one of the countries has a significantly higher HDI than the other These differences encourage discussion of government policies relating to health and education to determine what is possible in one country against what is out of reach for the other.

The HDI is additionally utilized to address inequality within countries, between sexes, between states or territories, across identities, and among other economic categories Promoting contradictions in this way has sparked public debate in a number of countries.

The yearly Human Development Reports released by the Human DevelopmentReport Office of the United Nations Development Programme include the genesis of the HDI (UNDP) In 1990, Pakistani economist Mahbub ul Haq created and introduced these with the express goal of "moving the focus of development economics from national income accounting to people-centered strategies." To persuade the general public, scholars, and politicians that they can and should evaluate development not only by economic accomplishments but also by increases in human well-being, Haq argued that a straightforward composite measure of human development was necessary.

Despite being an advancement compared to previous development measurements, the HDI still shows off its limitations, some of which can be listed as follow:

 There is a wide divergence of HDI within one country For example, North China is poorer than South-east

 HDI represents long-term changes such as life expectancy and thus may not react to recent short-term changes.

 Depending on how it is used, increased national wealth may or may not result in improved economic welfare For instance, if a nation spends more on its military, its GNI will increase, but wellbeing may actually decrease.

 Additionally, larger GNI per capita may mask severe inequality within a nation High degrees of inequality exist in certain nations with greater real GNI per capita, such as Saudi Arabia and Russia HDI, however, can identify nations with comparable GNI per capita but varying degrees of economic growth.

 Economic welfare depends on a number of other factors, including threat of war, levels of pollution, access to clean drinking water, etc.

It can be concluded that although the HDI is an improvement in comparison to past indices, it is still not a perfect indicator that can properly reflect the idea that theHuman Development concept brings in itself.

HDi is calculated based on the three key dimensions of human development: Health, Education, and Standard of living, Indicators used to characterize these basic dimensions include:

 Health is measured by the life expectancy at birth (LE) Life expectancy at birth is the number of years a newborn infant could anticipate to live provided current trends in age-specific death rates at the moment of birth hold true for the duration of their lives A country’s life expectancy index at age LE can be calculated using the following formula:

 Education is determined by two factors: the expected years of schooling (EYS) - the number of years of schooling a child starting school can expect to receive if current patterns of age-specific enrolment rates persist throughout the child’s life

- and the mean years of schooling (the average number of years of education received by people aged 25 and older, converted from education attainment levels using official durations of each level, denoted as MYS) The education index (EI) is calculated in the following formula:

MYSI = MYS15 and EYSI = EYS18

 Standard of living is characterized by gross national income per capita (GNI).When converted to international dollars using PPP rates and divided by the midyear population, GNI is the total income that an economy generates from its production and ownership of factors of production, minus the incomes paid for the use of factors of production owned by the rest of the world Thus, the following formula produces the income index (II):

 Having determined the above indices, the HDI is calculated as the geometric mean (equally-weighted) of life expectancy, education, and income index, as follows:

Related Published Reseaches

The idea of a human development index was conceived in 1990, during a period when people's lives in the world especially in developing/poor countries were also more focused on education and health factors This index was developed by Pakistani economist Mahbub ul Haq and Indian economist Amartya Sen and is featured in the

"Human Development Report" (Human Development Studies - HDR) was first published by UNDP (1990) Later, HDI was also used by UNDP and other countries around the world to assess human development over each period

One of the first and very famous studies on HDI was "Human Development Index: Methodology and Measurement" by Amatyr K Sen and Sudhir Anand themselves which was published in 1994 This study mainly analyzes aspects affecting the HDI such as: Disaggreration of HDI by population subgroups, gender, and some other supplementary components Notably, the HDI of this period was determined by three main factors: life expectancy, education and adjusted income; and conspicuously, the formula at this point is very complex Although still analyzing life exdpectancy, the focus and calculation of this factor is different from the current average age calculation. This old calculation is based on three main aspects to evaluate:

- A long and healthy life as measured by the average life expectancy at birth.

- The population's knowledge is measured by adult literacy rate and school enrollment rate.

- People's standard of living is measured by Gross Domestic Product (GDP) per capita and adjusted by purchasing power parity (Purchasing) method.

Power Parity - PPP), in US dollars - USD.

To calculate the HDI value, it is first necessary to calculate three component indices: life expectancy, knowledge and income The general rule for calculating these component indices is to use the minimum and maximum values for each indicator. From 1990 to 2010, the content and method of calculating the HDI of the basic Human Development Reports (HDRs) did not change much However, the content and methods of calculating HDI also have limitations The most important limitation is that it does not more closely reflect human development in the context of not wanting to expand the HDI components, because of the complexity in collecting information for countries, even with 192 member states of the United Nations Because of this, the

2010 Human Development Report used a new methodology that is still in use today

In terms of studies with application or analysis of aspects related to HDI components, in 2017 we have a study: "Quality of Life among General Populations of Different Countries in the Past 10 Years, with a Focus on Human Development Index: A Systematic Review and Meta-analysis" This study mainly uses relevant factors to determine HDI such as: Physical domain, Psychological domain, Social domain and Environmental domain to calculate the QoL - Quality of Life index in countries around the world The extremely high HDI subgroup had the greatest overall QOL mean at 74.26 (CIr.40-76.12) The lowest mean score was observed in the psychological domain (Mg.37; CIf.23–68.52) And the study came to the conclusion, the highest means of various QOL domains were observed in the very high HDI subgroup.

In addition, there are two other studies: "The dynamic association between healthcare spending, CO2 emissions, and human development index in OECD countries" published in 2020 and "Environmental Sustainability and Human Development: A

Greening of Human Development Index" published in 2014 as this study will show, also very clearly explores the connections between Health and Environmental concerns and the Human Development Index (HDI) More specifically, research in 2014 shows that the close relationship between the development index of environmental sustainability and the HDI constitutes a U-shaped relationship between the HDI and the EPI In addition, this study also considers and calculates a new index, which is EHDI, between both environmental factors and witnessing a huge change in order, which is said to be objective and ensure the development of the environment more sustainable. And the 2020 study shows that: all three key variables, health care costs, CO2 emissions and HDI all show a cause-and-effect relationship; A two-way causal relationship exists between health care costs and CO2 emissions, which suggests that CO2 emissions significantly increase health care costs in OECD countries, similarly, head investment in health care also increases emissions by using more energy; positive relationship of investment in health facilities with HDI and finally, CO2 reduction has a positive effect on HDI.

Develop Research Hypotheses (Research Questions)

Therefore, based on the theories and formulas given above about HDI and related factors, there are points that have not been resolved in previous studies This study was born to answer the following questions: What is the effect of Life Insurance Volume toGDP on the Human Development Index - HDI? What are the effects of environmental factors, specifically air pollution, on HDI? What is the impact of health spending onHDI? What is the impact of general health problems on the HDI?

MODEL SPECIFICATION

Methodology

2.1.1 Method used to collect data

Secondary data from developing countries in 2021: by using quantitative method, 50 observations are analyzed, all of which have been sourced from the websites: PMI2.5 Index https://databank.worldbank.org/

2.1.2 Method used to analyze data

Microsoft Excel is utilized to sort data while STATA software to process statistics and calculate the correlation matrix among variables

2.1.3 Method used to derive the model

Based on previous studies, we can see a linear relationship between life expectancy at birth, expected years of schooling and gross national income per capita As a result, we use a multiple regression model and the Ordinary Least Squares (OLS) method to create a linear function to test our hypotheses.

The OLS method estimates the relationship by minimizing the total square error in the difference between the observed and predicted values of the dependent variables figured by a straight line.

When using this method, we relied on basic assumptions of the OLS (Ordinary Least Squares) Consider the 2 variable regression model:

1 The regression model is linear in the parameters.

2 X values are fixed in repeated sampling This also means 𝑙𝑙 and 𝑙𝑙 are uncorrelated.

3 Zero mean value of disturbance 𝑙𝑙(E( | )=0)𝑙𝑙𝑙𝑙

4 Homoscedasticity or equal variance of 𝑙𝑙

5 No correlation between the disturbances (cov [ui uj|𝑙 𝑙, 𝑙 ] = E [ui uj |𝑙 , 𝑙 𝑙 ] =0).

6 The model is correctly specified.

7 The number of observations must be greater than the number of parameters to be estimated.

8 The X values in a given sample must not all be the same

9 There is no perfect multicollinearity.

Theoretical model specification

Based on the above-mentioned sections, our group has developed a function analyzing the relationships between Human Development Index and other independent variables, as well as their effect on Human Development Index

HDI = f(LIV, PM, HE, GEE, UNE)

Type of variable Variable Meaning Unit

Dependent HDI Human Development Index

LIV Life Insurance Volume to GDP %

HE Healthcare Expenditure to GDP %

Government Expenditure on Education to

HDI depends on many factors which are life expectancy at birth, expected years of schooling, mean years of schooling, gross national income per capita and various factors To indicate those other factors, we build the population regression model and set u to represent the disturbances: i lnY= + X1+X +X +X +X +u2 3 4 5 i

PRM: lnHDI = + lnLIV+lnPMI+ lnHE+lnGEE+lnUNE+ u i i

SRM: lnHDI= + lnLV+lnPMI+ lnHE+lnGEE+lnUNE + i

 HDI (0 HDI 1) is the dependent variable

 LIV, PMI, HE are the independent variable which explain for HDI

 : the intercept of the regression line

 : the estimate of the intercept of the regression line

 (i=1,2,3,4,5): the slope of the regression

 (i=1,2,3,4,5): the estimator of the slope of the regression line

 ui: the error term of the regression line

 : the estimator of the error term u i

 : is the intercept of the regression line and equals to the expected value of lnHDI when all explanatory variables equal to 0.

 : holding other factors constant, when LIV increases by 1%, the average of corresponding HDI will increase by %

 : holding other factors constant, when PMI increases by 1%, the average of corresponding HDI will increase by %

 : holding other factors constant, when HE increases by 1%, the average of corresponding HDI will increase by %

 : holding other factors constant, when GEE increases by 1%, the average of corresponding HDI will increase by %

 : holding other factors constant, when UEN increases by 1%, the average of corresponding HDI will increase by %

Variable Meaning Unit Expected sign of regression

PM PM2.5 àm/m 3 - According to UNDARK: “the bottom scale shows the U.S.Environmental ProtectionAgency’s current benchmarks specifically for PM2.5, which is measured in micrograms per cubic meter of air — sometimes rendered as àg/m³ The higher the mass of fine particulates in the air, the more dangerous it is to breathe”

Data analysis

The data used in this article is obtained mainly from Data Bank, “2019 World Air Quality” report by IQAir, and The Global Economy This is the type of cross-sectional data and includes 50 observations from 50 with very high and high HDI in the year

2019 Five indicators are listed in this data: Human Development Index (HDI), LifeInsurance Volume (LIV), PM2.5 (PM), Healthcare Expenditure to GDP (HEE), Government Expenditure on Education to GDP (GEE), Unemployment rate (UEN)

To create the summary of variables as below, we run the summarize command (sum lnHDI lnLIV lnPMI lnHE lnGEE lnUNE)

Variable Obs Mean Std Dev Min Max lnHDI 50 -0.138231 0.0781679 -0.3467246 -0.0439519 lnLIV 50 0.1147721 1.315275 -3.218876 2.076938 lnPMI 50 2.650961 0.4997828 1.722767 3.688879 lnHE 50 2.042107 3232366 1.332366 2.819592 lnGEE 50 1.546593 2816094 0.9555115 2.047693 lnUNE 50 1.678786 0.5834206 -0.3285041 2.850707 from which can be pointed out:

 - Human Development Index (HDI): lnHDI has a mean value of -0.138231, the minimum value ( -0.3467246) in Egypt and the maximum value ( -0.0439519) in

Norway with the standard deviation of 0.0781679.

 - Life insurance volume to GDP (LIV): lnLIV has a mean value of 0.1147721, the minimum value (-3.218876 ) in United Arab Emirates and the maximum value (2.076938) in Denmark with a standard deviation of 1.315275.

 - PM2.5 (PMI): The mean value of lnPMI in 50 observed countries is 2.650961, with the highest value belonging to Slovenia (3.688879) and the lowest value (1.722767) in Iceland and the standard deviation being 0.4997828.

 - Healthcare expenditure to GDP (HE): the mean value of lnHE of 50 observed nations is 1.546593 , with the highest value (2.819592) in the USA and the minimum value (1.332366) in Thailand with a standard deviation of 0.3232366.

 - Government Expenditure on Education to GDP (GEE): lnGEE has a mean value of 0.1147721, the minimum value (0.9555115) in Egypt and the maximum value (2.047693) in Denmark with a standard deviation of 0.2816094.

 - Unemployment rate (UEN): the mean value of lnUEN of 50 observed nations is 1.678786 , with the highest value (2.850707) in Greece and the minimum value (-.3285041) in Thailand with a standard deviation of 0.5834206.

Our group used STATA to run the command (corr HDI ) to analyze the correlation between the variables we obtained the results as follows:

HDI LIV PMI HE GEE UEN

According to the Australian Bureau of Statistics, correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable.

Correlation coefficient Strength of the correlation

- The correlation between the dependent variable and independent variables:

 + lnLIV and lnHDI are positively correlated at a rate of 0.4242 The positive correlation coefficient indicates a positive relation between Human Development Index and Life Insurance Volume to GDP; this is in accordance with the initial expectation.

 + lnPMI and lnHDI are negatively correlated at a rate of -0.5618 The negative correlation coefficient indicates a negative relation between Human

Development Index and PM2.5; this is in accordance with the initial expectation.

 + lnHE and lnHDI are positively correlated at a rate of 0.5357 The positive correlation coefficient indicates a correlation relation between Human Development Index and Healthcare Expenditure to GDP; this is in accordance with the initial expectation.

 + lnGEE and lnHDI are positively correlated at a rate of 0.4437 The positive correlation coefficient indicates a correlation relation between Government Expenditure on Education to GDP and Human Development Index ; this is in accordance with the initial expectation.

 + lnUEN and lnHDI are negatively correlated at a rate of 0.5357 The negative correlation coefficient indicates a correlation relation between Human Development Index and Unemployment rate; this is in accordance with the initial expectation.

- The correlation between independent variables:

 + lnPMI and lnLIV are negatively correlated with a coefficient of -0.2904

 + lnHE and lnLIV are positively correlated with a coefficient of 0.3315.

 + lnHE and lnPMI are negatively correlated with a coefficient of -0.5.

 + lnGEE and lnLIV are positively correlated with a coefficient of 0.1192.

 + lnGEE and lnPMI are negatively correlated with a coefficient of -0.5459.

 + lnGEE and lnHE are positively correlated with a coefficient of 0.5353.

 + lnUEN and lnLIV are negatively correlated with a coefficient of -0.1969

 + lnUEN and lnPMI are positively correlated with a coefficient of 0.0468.

 + lnUEN and lnHE are positively correlated with a coefficient of 0.2230.

 + lnUEN and lnLGEE are positively correlated with a coefficient of 0.0845.

ESTIMATED MODELS, HYPOTHESIS TESTING AND STATISTICAL INFERENCES

Estimated Model

Using the data obtained and the STATA software to yield the result of the Ordinary Least Square (OLS) Estimation Method as follow:

Root MSE = 0,5043 lnHDI Coef Std Err t P>t [95% Conf Interval lnLIV 0.0105847 0.0070796 1.5 0.142 -0.0036834 0.0248527 lnPMI -0.0387251 0.0218378 -1.77 0.083 -0.0827363 0.0052861 lnHE 0.0782181 0.0356035 2.2 0.033 0.006464 0.1499722 lnGE

Using the result generated by STATA, we obtained the Sample Regression Model according to concluded Function in Section 2, as below:

Hypothesis Testing

3.2.1 Testing the consistency of the regression result with the theories

Independent Variables Coef lnLIV 0.0105847 lnPMI -0.0387251 lnHE 0.0782181 lnGEE 0.0377512 lnUNE -0.0345612

Based on the results calculated above, it can be seen that the independent variables all have an impact on the Human Development Index - HDI, consistent with the theory built in Section 1 and Section 2 Specifically:

 Life Insurance Volume to GDP (LIV) and HDI have a positive relationship (HDI increases by 1%, LIV increases by 0.0105847%), showing that the level of investment in life insurance affects human development.

 PM2.5 (PMI) and HDI have an negative relationship (HDI increases by 1%, PMI decreases by 0.0387251%), showing that fine dust and air pollution levels affect human development.

 Healthcare Expenditure to GDP (HE) and HDI have a positive relationship (HDI increases by 1%, HE increases by 0.0782181%), showing that the level of spending on health affects human development.

 Government Expenditure on Education to GDP (GEE) and HDI have a positive relationship (HDI increases by 1%, GEE increases by 0.0377512%), showing that the level of government spending on education affects human development.

 Unemployment rate (UNE) and HDI have an negative relationship (HDI increases by 1%, UNE decreases by 0.0387251%), showing that unempoyment rate affect human development.

Comment: These results are consistent with the thẻoies and studies above

3.2.2 Test the statistical significance of the regression coefficients of the independent variables

 H 0 : The coefficient of regression of the independent variable is not statistically significant (𝑙𝑙 = 0).

 H 1 : The coefficient of regression of the independent variable is statistically significant (𝑙𝑙≠0).

At the significance levels of 5%:

 Variable LIV: P-value = 0.142 > 0.05, therefore we reject H 1 , accept H 0 at 5% significant level.

 Variable PMI: P-value = 0.083 > 0.05, therefore we reject H 1 , accept H 0 at 5% significant level.

 Variable HE: P-value = 0.033 < 0.05, therefore we reject H 0 , accept H 1 at 5% significant level.

 Variable GEE: P-value = 0.322 > 0.05, therefore we reject H 1 , accept H 0 at 5% significant level.

 Variable UNE: P-value = 0.031 < 0.05, therefore we reject H 0 , accept H 1 at 5% significant level.

Conclusion: Regression coefficients of independent variables HE and UNE are statistically significant at significant levels of 5% LIV, PMI and GEE regression coeficients are not statistically significant at significant levels of 5%

3.2.3 Testing the joint significance of a group variables

According to the estimated model, there are 3 independent variables that not statistically significant: LIV, PMI and GEE Therefore, using STATA software to test the joint significance of this group variables

Root MSE = 0.31836 lnHE Coef Std Err t P>t [95% Conf Interval lnUN

=> LIV, PMI and GEE not statistically significant to the SRM (Sample Regression Model).

3.2.2 Testing the overall significance of the model

According to the estimation output, test statistics:

The model has overall significance at the significant level of 5%

3.3 Are the coefficients statistically significant?

3.3.1 The meaning of the estimated coefficients

 = -0.1968818 : If the independent variables equal to 0 then the expected mean value of the dependent variable is the intercept term

 = 0.0105847 : When Life Insurance Volume to GDP (LIV) increases by 1 unit and the independent variables are unchanged, the expected value of HDI will increase by 0.0105847%

 = -0.0387251 : When PM2.5 (PMI) increases by 1 unit and the independent variables are unchanged, the expected value of HDI will decrease by 0.0387251%

 = 0.0782181 : When Healthcare Expenditure to GDP (HE) increases by 1 unit and the independent variables are unchanged, the expected value of HDI will increase by 0.0782181%

 = 0.0377512 : When Government Expenditure on Education to GDP (GEE) increases by 1 unit and the independent variables are unchanged, the expected value of HDI will increase by 0.0377512%.

 = -0.0345612 : When Unemployment rate (UNE) increases by 1 unit and the independent variables are unchanged, the expected value of HDI will decrease by 0.0345612%

3.3.4 The mechanism of found relationship:

As demonstrated above, only the HE and UNE indices are statistically significant in this HDI Sample Regression Model, the PMI, LIV and GEE indices are not statistically significant This can be explained for the following reasons:

 First, the LIV - Life Insurance Volume to GDP index compared with the HDI has a positive relationship, but it is important as demonstrated above that LIV is not statistically significant with the SRM of the HDI The reason for the low influence of LIV on HDI is because: HDI is formed from 3 main factors, including human health But the LIV index only measures the value of life insurance purchased in terms of GDP, but buying life insurance only ensures cost reduction and compensation when people have health problems, not It is not possible to deduce the level of human health.

 Second, PMI - PM2.5 index to measure air pollution levels of areas Although, the HDI calculates one-third according to health, but here health is calculated by the average life expectancy in the regions, so the air pollution index despite its effect on health and life expectancy jar However, this effect is too small to affect the HDI.

 Third, Health Care Expenditure - HE is a measure of the level of expenditure by people in health in regions/countries by GDP As stated above, HDI is calculated partly on the life expectancy index, so people's financial expenditure(represented by the HE index) is very close and the level of health investment affects directly affect the longevity of people Therefore, HE is a statistically significant index.

 Next, like PMI, GEE is an index of government spending on health, although there is some influence to increase life expectancy, but these effects are too small compared to HDI.

 Finally, the UNE unemployment index, which directly affects the income part of the HDI calculation, because the lower the unemployment rate, the more people have jobs, the average income will also increase accordingly Therefore, HDI and UNE also have a negative relationship.

From the results above, the most suitable estimation model obtained by our group is: lnHDI i = (-0.196881)+ 0.0105747.lnLIV + (0.0387251).lnPMI + 0.0782181.lnHE + 0.0377512.lnGEE

The model explains the variation of HDI according to the variables LIV, PMI, HE, GEE, and UNE The parameters are all statistically significant and the model is consistent with the assumptions and expectations of the classical linear model without any defect problems Specifically, all independent variables have an influence on the dependent variable at the significance level of 5% and the model has the coefficient of determination R = 2 0.5043 which shows that the variables can explain 50,43% of the variation in the dependent variable (lnHDI).

The study is mostly based on the views and data of Data Bank Through the process of research and analysis, our group found that Healthcare Expenditure to GDP and Unemployment rate are the factors that influence (postively and negtaively) the human development index (HDI) in each country while Government Expenditure on Education to GDP, Life Insurance Volume to GDP and PM2.5 are not sufficient to conclude The impact of these factors on HDI in 50 countries with very high and high HDI is consistent with the theoretical model.

A summary of a nation's progress may be created by analyzing and contrasting theHDI To determine how these factors affect a country's HDI, it is required to investigate the HDI of several emerging nations As a consequence, each nation is able to recognize its weaknesses and build more specialized human development plans while also being self-aware of its own position and trends in global life People are a nation's most important resource, thus as they develop, the nation will become more powerful and affluent The advancement of humanity is the advancement of the nation.

Human Development Index (HDI) https://hdr.undp.org/data-center/human-development-index#/indicies/HDI https://corporatefinanceinstitute.com/resources/economics/human-development-index/ https://hdr.undp.org/data-center/human-development-index#/indicies/HDI https://www.economicshelp.org/blog/glossary/human-development-index/ https://www.geeksforgeeks.org/human-development-index-definition-importance- limitations/ https://www.shorttutorials.com/how-to-calculate-hdi/hdi.html

Related Public Research https://ora.ox.ac.uk/objects/uuid:98d15918-dca9-4df1-8653-

60df6d0289dd/download_file?file_format=application

%2Fpdf&safe_filename=HDI_methodology.pdf&type_of_work=Report https://hdr.undp.org/content/human-development-report-2010 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5401920/ https://link.springer.com/article/10.1007/s10668-020-01066-5 https://papers.ssrn.com/sol3/papers.cfm?abstract_id$26073