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OVERVIEW OF THE HUMAN DEVELOPMENT INDEX AND ITS COMPONENTS
Overview 7
1.1.1 Definition and Importance of HDI
The Human Development Index (HDI), as defined by the UNDP, is a comprehensive indicator that measures a country's average performance across key human development dimensions It assesses a long and healthy life through life expectancy, educational attainment by considering the median years of schooling for adults and predicted years of schooling for children, and a decent standard of living based on Gross National Income per capita This multidimensional approach provides a holistic view of development beyond mere economic growth.
The normalized indices for each of these three key dimensions' geometric means make up the HDI
Human development originally emphasizes economic advancement, but it also encompasses well-being, education, climate stability, and opportunities While income is vital, human progress also relies on fundamental rights and socio-economic opportunities The Human Development Index (HDI) was developed as a more compassionate measure of progress, going beyond traditional economic indicators like GNP or GDP It evaluates multiple dimensions of development, allowing for a comprehensive assessment of a country's overall well-being Each year, the UNDP ranks nations based on HDI, with higher positions granted to those excelling in standards of living, education, and life expectancy Conversely, countries where marginalized populations lag behind tend to have lower rankings, highlighting the need for targeted 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 pomt 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 Human Development Reports, published annually by the UNDP's Human Development Report Office, detail the origin of the Human Development Index (HDI) In 1990, Pakistani economist Mahbub ul Haq introduced the HDI to shift the focus of development economics from solely national income to people-centered strategies Haq emphasized the importance of evaluating development based on human well-being alongside economic progress, advocating for a simple, composite measure of human development to influence public, scholarly, and political perspectives.
The Human Development Index (HDI) has made significant advancements over previous development measurements but still exhibits notable limitations For instance, there can be significant disparities within countries, such as North China being poorer than the southeast HDI reflects long-term indicators like life expectancy and may not promptly respond to recent short-term changes Additionally, increased national wealth does not always equate to improved economic welfare, especially if nations prioritize military spending, which can raise GNI but reduce overall well-being Moreover, higher GNI per capita can mask severe internal inequalities, as seen in countries like Saudi Arabia and Russia, which have greater GNI yet face significant disparities HDI also helps distinguish among nations with similar GNI per capita but differing economic growth patterns Finally, economic welfare depends on various other factors including the threat of war, pollution levels, and access to clean drinking water, which are not directly captured by HDI.
While the Human Development Index (HDI) represents an improvement over previous metrics, it still falls short of fully capturing the comprehensive concept of human development Despite its advancements, HDI may not entirely reflect the multidimensional nature of human well-being and progress Therefore, although useful, HDI remains an imperfect indicator for assessing true human development.
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 commonly assessed through life expectancy at birth (LE), which indicates the average number of years a newborn is expected to live based on current mortality rates Life expectancy at birth reflects the overall health conditions and healthcare quality within a country It is calculated by estimating the number of years a newborn could expect to live if current age-specific death rates remain constant throughout their lifetime A country's life expectancy index can be determined using a specific formula that provides valuable insights into its population's health status and development level.
Education is influenced by two key factors: the expected years of schooling (EYS), which estimates the total years a child starting school can anticipate based on current enrollment patterns, and the mean years of schooling (MYS), representing the average years of education completed by individuals aged 25 and older, calculated from attainment levels using official durations The Education Index (EI) is derived from these components, providing a comprehensive measure of educational development worldwide.
MYSI = MYS15 and EYSI=EYS18
Standard of living is primarily measured by gross national income per capita (GNI), which reflects the average income of individuals within a country When GNI is converted into international dollars using Purchasing Power Parity (PPP) rates and divided by the midyear population, it provides a more accurate comparison across nations GNI represents the total income generated by a country's production and ownership of factors of production, minus payments for the use of factors owned by the rest of the world This calculation helps assess a nation's economic well-being through the income index (ID) formula.
The Human Development Index (HDI) is calculated by determining key indices such as income, life expectancy, and education Specifically, the income index is derived from the natural logarithm of Gross National Income (GNI) per capita, adjusted by constants to standardize the measure Once these indices are established, the HDI is computed as the geometric mean of the life expectancy, education, and income indices, providing a comprehensive measure of human development This methodology ensures an equal weighting of all three dimensions, resulting in a balanced assessment of socio-economic progress.
Related Published Reseaches: 11 1.3 Develop Research Hypotheses (Research Questions): 13
The Human Development Index (HDI) was introduced in 1990 to measure and promote better living standards worldwide, especially in developing countries Developed by renowned economists Mahbub ul Haq of Pakistan and Amartya Sen of India, the HDI emphasizes key factors such as education and health This innovative index aims to provide a comprehensive view of human well-being beyond economic growth alone, highlighting the importance of improving quality of life globally.
The "Human Development Report" (HDR), first published by UNDP in 1990, introduced the Human Development Index (HDI) as a key measure of progress Since its inception, the HDI has been widely utilized by the UNDP and countries worldwide to evaluate and monitor human development over time, providing crucial insights into progress in health, education, and income levels across nations.
The 1994 study "Human Development Index: Methodology and Measurement" by Amartya Sen and Sudhir Anand is among the earliest and most influential research on HDI, examining factors such as population subgroups, gender, and supplementary components that impact HDI During this period, the HDI was primarily determined by three key factors: life expectancy, education, and adjusted income, with a notably complex formula While life expectancy was still a focus, its calculation differed from the current method of measuring average age, emphasizing different aspects of health and longevity.
- 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 Human Development Index (HDI), it is essential to determine three component indices: life expectancy, knowledge, and income, using minimum and maximum values for each indicator Between 1990 and 2010, the methods and content of HDI calculation in the Human Development Reports remained relatively consistent However, these calculations have limitations, primarily because HDI does not fully capture human development complexities, partly due to challenges in collecting comprehensive data across the 192 UN member states.
2010 Human Development Report used a new methodology that 1s still in use today
A 2017 systematic review and meta-analysis examined the relationship between the Human Development Index (HDI) and quality of life (QoL) across different countries over the past decade The study evaluated key HDI components—physical, psychological, social, and environmental domains—to assess their impact on overall QoL Results showed that countries in the extremely high HDI subgroup exhibited the highest average QoL score of 74.26, while the psychological domain scored the lowest at a mean of 37 The study concluded that the highest QoL levels are consistently observed in countries with very high HDI, highlighting a positive correlation between HDI and quality of life.
Recent studies highlight the interconnectedness of healthcare spending, CO2 emissions, and human development For instance, the 2020 study titled "The dynamic association between healthcare spending, CO2 emissions, and human development index in OECD countries" explores how increased healthcare investment influences environmental impact and human progress Additionally, research on "Environmental Sustainability and Human Development" emphasizes the importance of integrating sustainable practices into development policies to promote long-term health and environmental well-being These findings underscore the critical link between environmental sustainability, healthcare, and human development, guiding policymakers toward environmentally conscious growth strategies.
The 2014 study titled "Greening of Human Development Index" highlights the strong connections between health, environmental concerns, and the Human Development Index (HDI), revealing a U-shaped relationship between environmental sustainability and HDI through the Environmental Performance Index (EPI) It introduces a new index, the Environmental Human Development Index (EHDI), aimed at objectively measuring environmental progress and promoting sustainable development Additionally, a 2020 study demonstrates that healthcare costs, CO2 emissions, and HDI are interconnected through causal relationships; specifically, increased CO2 emissions raise healthcare costs in OECD countries, while higher healthcare investments can lead to more emissions due to increased energy use The study also finds a positive relationship between investment in health facilities and HDI, and notes that reducing CO2 emissions positively impacts HDI, emphasizing the interconnectedness of environmental sustainability and human development.
1.3 Develop Research Hypotheses (Research Questions):
Building on existing theories and formulas related to HDI and its determinants, this study addresses unresolved issues from previous research It aims to investigate the effect of life insurance volume on GDP and its subsequent impact on the Human Development Index (HDI) Additionally, the study examines how environmental factors, particularly air pollution, influence HDI It also explores the role of health spending and general health problems in shaping human development outcomes.
MODEL SPECIFICATION cscccsccccccccscscessseeseeeeeersnesesesenscececesesesessseeneceseseees 14 2.1 Methodology 14 2.1.1 Method used to collect data 14 2.1.2 Mlethodl trsedl 1O GHGẽU2€ (QÍ( Gà HH HT TH TH TT nh cư cư 14 2.1.3 Method used to derive the model 14 2.2 Theoretical model specification 16 2.2.1 Specify the model 16 2.2.2 Explanations ng j7 n6 nh ố ố ố
Data analysis 19 1 Source of data 19 2 Descriptive analysis 20 3 Correlation matrix between variables
This article utilizes data primarily sourced from the Data Bank, IQAir’s “2019 World Air Quality” report, and The Global Economy It focuses on cross-sectional data comprising 50 observations from countries with very high and high Human Development Index (HDI) levels in 2019.
In 2019, Jare analyzed five key indicators to assess overall development These include the Human Development Index (HDI), which measures social and economic progress; Life Insurance Volume (HIV), reflecting the financial security of citizens; PM2.5 levels (PM), indicating air quality and environmental health; Healthcare Expenditure to GDP ratio (HEE), demonstrating investment in public health; Government Expenditure on Education to GDP (GEE), highlighting priorities in educational development; and the Unemployment Rate (UEN), providing insight into the employment landscape These indicators collectively offer a comprehensive view of Jare's socio-economic and environmental status.
To create the summary of variables as below, we run the summarize contmand (sum InHDI InLIV InPMI InHE InGEE InUNE)
Variable Obs Mean Std Dev Min Max
InUNE 50 1.678786 0.5834206 -0.3285041 2.850707 from which can be pointed out: ¢ - Human Development Index (HDI): InHDI has a mean value of -0.138231, the minimum value ( -0.3467246) in Egypt and the maximum value ( -0.0439519) in
Norway exhibits a low standard deviation of 0.0781679, indicating stable economic indicators across the country The life insurance volume to GDP (LIV) ratio has a mean value of 0.1147721 globally, with notable variability, ranging from a minimum of -3.218876 in the United Arab Emirates to a maximum of 2.076938 in Denmark, which has a standard deviation of 1.315275 The average PM2.5 (PMI) level across 50 countries is 2.650961, with Slovenia displaying the highest pollution index at 3.688879 and Iceland the lowest at 1.722767, with a standard deviation of 0.4997828 Healthcare expenditure as a percentage of GDP (HE) averages 1.546593 across these nations, with the United States leading at 2.819592 and a minimum of 0.11 in another country, reflecting significant disparities in healthcare investment worldwide.
(2.047693) im Denmark with a standard deviation of 0.2816094. rate (UEN); the mean of InUEN of 50 nations
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
A correlation coefficient (expressed as a number) indicates the strength and direction of a relationship between two or more variables However, it is important to note that a correlation does not imply causation; just because two variables are related does not mean that changes in one directly cause changes in the other Understanding this distinction is essential for accurate data analysis and interpretation.
Correlation coefficient Strength of the correlation
The analysis reveals that the Human Development Index (HDI) is positively correlated with Life Insurance Volume to GDP (r = 0.4242), Healthcare Expenditure to GDP (r = 0.5357), and Government Expenditure on Education to GDP (r = 0.4437), supporting the hypothesis that higher human development aligns with increased financial protection, healthcare investment, and educational spending Conversely, HDI shows a negative correlation with PM2.5 levels (r = -0.5618) and Unemployment Rate (r = -0.5357), indicating that greater human development is associated with lower pollution and unemployment rates These relationships highlight the significant impact of socio-economic and environmental factors on human development outcomes, emphasizing the importance of policies aimed at enhancing healthcare, education, and environmental quality to promote sustainable development.
Development Index and Unemployment rate; this is in accordance with the initial expectation
The analysis reveals significant correlations among the independent variables Specifically, the variables ô + InPMI and InLIV are negatively correlated with a coefficient of -0.2904, indicating an inverse relationship Conversely, ô + InHE and InLIV show a positive correlation with a coefficient of 0.3315, suggesting that higher values of ô + InHE are associated with increased InLIV Additionally, the variables ¢ + InHE and InPMI exhibit a strong negative correlation of -0.5, highlighting a significant inverse link between these factors Furthermore, ¢ + InGEE and InLIV are positively correlated with a coefficient of 0.1192, whereas another variable shows a negative correlation of -0.5459, underscoring the varying degrees of association among these independent variables These findings underscore the complex interrelationships influencing InLIV in the study.
The article highlights various correlations between key variables, with plating exhibiting a coefficient of 0.5353, indicating a strong positive relationship, while Trelated shows a negative correlation of -0.1969 InGEE and InPMI are negatively correlated, whereas InFE and InHy demonstrate positive associations Additionally, InUEN is negatively correlated with lnLIỆ and positively correlated with InPMI WEN and LGEE have a positive correlation coefficient of 0.2230, emphasizing their strong linkage Overall, these findings provide valuable insights into the relationships among the variables, which are essential for optimizing relevant strategies and improving performance.
ESTIMATED MODELS, HYPOTHESIS TESTING AND STATISTICAL INFERENCES
Estimated Model: 25 ng 2n nan
Using the data obtained the STATA so to yield the of the Ordinary Least Square (OLS) |
IHHDI Cocf Std Err t P>t [95% Conf Interval InLIV 0.0105847 0.0070796 1.5 0.142 -0.0036834 0.0248527 InPMT -0.0387251 0.0218378 -1.77 0.083 -0.0827363 0.0052861 InHE 0.0782181 0.0356035 2.2 0.033 0.006464 0.1499722
Using the result generated by STATA, we obtained the Sample Regression Model according to concluded Function in Section 2, as below:
Hypothesis Testing 25
3.2.1 Testing the consistency of the regression result with the theories
The analysis indicates that all independent variables significantly impact the Human Development Index (HDI), aligning with existing theoretical frameworks Specifically, there is a positive relationship between Life Insurance Volume to GDP (LIV) and HDI, with a 1% increase in HDI corresponding to a 0.0106% rise in LIV, highlighting the importance of life insurance investment in human development Conversely, PM2.5 (PMI) shows a negative correlation with HDI, where a 1% increase in PM2.5 levels results in a 0.0387% decrease in HDI, emphasizing the detrimental effect of air pollution on 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
EE increases by 0.0377512%), showing education affects human development oyment ) and I have an negative relationship (HDI
1%, 0387251%), showing that unempoyment affect devel the théoies and studies above
3.2.2 Tast the of the regression coefficients of the
Hy: The coefficient of regression of the mdependent variable is not
Variable LIV: P-value = 0.142 > 0.05, therefore we reject Hi, accept Hp at 5% significant level
Variable PMI P-value = 0.083 > 0.05, therefore we reject Hi, accept Hp at 5% significant level
Variable HE: P-value = 0.033 < 0.05, therefore we reject Ho, accept Hy at 5% significant level
Variable GEE: P-value = 0.322 > 0.05, therefore we reject Hi, accept Ho at 5% significant level
Variable UNE: P-value = 0.031 < 0.05, therefore we reject Ho, accept Hi at 5% significant level.
The regression analysis indicates that the coefficients for the independent variables HE and UNE are statistically significant at the 5% significance level, highlighting their strong influence on the dependent variable In contrast, the coefficients for LIV, PMI, and GEE are not statistically significant at the 5% level, suggesting their limited impact within the model These findings underscore the importance of HE and UNE in explaining the variation in the outcome, while LIV, PMI, and GEE do not show significant effects.
3.2.3 Testing the joint significance of a group variables
Based on the estimated model, three independent variables—LIV, PMI, and GEE—are not statistically significant To assess their collective impact, STATA software was used to test the joint significance of these variables The results indicate that the combined effect of LIV, PMI, and GEE is not statistically significant, suggesting these variables do not significantly influence the dependent variable in the model at a sample size of 50.
Source SS df MS NV" ý
Adj R-squared = 0.0299 Total 5.11961316 49 0.104481901 Root MSE = 031836
InHE Coef Std Err t Pt [95% Conf Interval
Do not reject Ho, accept Hi => B; = Bz = Bs = 0
=> LIV, PMI and GEE not statistically significant to the SRM (Sample Regression Model)
3.2.2 Testing the overall significance of the model
Hy: R2=0 Hi: R2>0 According to the estimation output, test statistics:
Reject null hypothesis Ho, accept Mi
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
Intercept term: ° Bo = -0.1968818 : If the independent variables equal to 0 then the expected mean value of the dependent variable is the intercept term
* p, = 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%
An increase of 1 unit in PM2.5 (PMD) is associated with a 0.0387% decrease in the Human Development Index (HDI), assuming other factors remain constant Conversely, a 1-unit rise in Healthcare Expenditure as a percentage of GDP (HE) correlates with a 0.0782% increase in HDI, highlighting the positive impact of healthcare investment Additionally, Government Expenditure on Education as a percentage of GDP also contributes to HDI improvement, emphasizing the importance of educational funding for human development.
An increase of one unit in GEE, with all other variables held constant, is associated with a 0.0377512% increase in the expected HDI Conversely, a rise of one unit in the Unemployment Rate (UNE), while other factors remain unchanged, results in a 0.0345612% decrease in the expected HDI.
3.3.4 The mechanism of found relationship:
In the HDI sample regression model, only the HE and UNE indices are statistically significant, while the PMI, LIV, and GEE indices are not The LIV (Life Insurance Volume to GDP) index shows a positive relationship with HDI but lacks statistical significance, as HDI encompasses broader factors such as human health that cannot be inferred solely from life insurance data, which primarily reflects cost reduction and compensation rather than health levels Additionally, although the PMI (PM2.5 air pollution index) impacts health and life expectancy, its effect on HDI—calculated mainly based on average life expectancy—is too minimal to be statistically significant.
Health Care Expenditure (HE) measures the level of health-related spending relative to a region's or country's GDP Since the Human Development Index (HDI) is partially based on life expectancy, the HE index closely correlates with overall health outcomes Higher health investment directly impacts longevity, making HE a statistically significant indicator of population health.
GEE, similar to PMI, is an index that measures government spending on health While increased health expenditure can have some positive impact on improving life expectancy, these effects are minimal when compared to the broader influence of the Human Development Index (HDI).
The UNE unemployment index significantly influences the income component of the Human Development Index (HDI), as lower unemployment rates lead to higher employment levels and increased average income Consequently, there is an inverse relationship between HDI and UNE unemployment rates, highlighting how improved employment conditions contribute to higher human development.
From the results above, the most suitable estimation model obtained by our group is:
The model effectively explains the variation of HDI based on variables such as LTV, PMI, and HE, aligning with the assumptions of the classical linear model All independent variables significantly influence the dependent variable (nHDI) at the 5% significance level With a determination coefficient (R²) of 0.5043, the model accounts for approximately 50.43% of the variability in HDI Our research and analysis from Data Bank reveal that Healthcare Expenditure to GDP significantly impacts HDI, highlighting its importance in human development assessments.
Unemployment rates are key factors that influence government expenditure on education as a percentage of GDP and life insurance volume to GDP, though PM2.5 levels alone are insufficient to draw definitive conclusions The impact of these variables on the Human Development Index (HDI) remains consistent with theoretical models across 50 countries with very high and high HDI levels, highlighting their significant roles in shaping human development outcomes.
Analyzing and contrasting a nation's Human Development Index (HDI) is essential for understanding its progress Investigating the HDI of emerging nations helps countries identify their strengths and weaknesses, allowing them to develop targeted human development plans Recognizing their position and global trends fosters greater self-awareness, which is vital for strategic growth People are the most valuable resource for any nation; as they grow and evolve, the nation becomes more powerful and prosperous Ultimately, the advancement of humanity directly contributes to the nation's development.
REFERENCE Human Development Index (HDI) https://hdr.undp org/data-center/human-development-index#/indicies/HDI https://corporatefinanceinstitute.com/resources/economics/human-development-index/
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Deviopment | Volume to expenditure Unemployment
Index GDP to GDP rate
(HDD (LIV) | (PMD (HE) (GEE) (UNE)
*summary of the varibales in the regression model* sum tnHDT LnLTV LnPMTI tnHE LnGEE LnUNE ẹụniahi# 0,82@ps 0Wean Bt Dev.5.74 Min 3,34 Max 3,91
„ COFF LnHDT LnLTV 1nPMT tnHE LnGEE LnUNE
| TnHDT tTnLTV TnPMT LnHE 1nGEE LnUNE
regGlHnA tnLIV tn6MŸÓhHE inGee Pobre 39,1 5,35 3,5
Source Ss df MS Number of obs) = 50
Adj R-squared = 8.4480 TotaL -299400711 49 006110219 Root MSE = - 05808
TnHDT Coef Std Err t P>|t| [95% Conf Interval] tnLIV 0105847 0070796 1.50 0.142 -.0036834 - 0248527
*test the statistically significant variables*
Egy Plce 0,787 0.94 Ig Number of obs 2.6 50
My al 4.P698?65 5 48 + 1013538 68 Rosayared 31 0.0497 h h h Adj R-squared = 0.0299
LnHE Coef Std Err t P>|t| [95% Conf Interval]
Covariance matrix of coefficients of regress model
Germany ,9 5 5 11,7 e(V) | InUNE _cons nÙNE | .89601688 , sata REE ii Fhingign gyrus