Factor Enhancing Quality of Life


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Abstract

An attempt is made in this study to analyze the factors affecting the Quality of Life (QoL). It is difficult to measure the Quality of Life (QoL); hence Human Development Index (HDI) was used as the measure for QoL.  Different factors such as standard of living, education, GDP, Gross Capital formations, Urbanizations and Physicians were examined that how these programs, in turn, influenced QoL. Data was collected from World Bank (2019) and UNDP (2019) and examined for 71 United Nations member countries over the period 1990-2019. The results obtained from cross-sectional and panel data approaches suggest that the indicators used in the study significantly enhance the quality of life. The study offers important policy implications.

 

Key Words

Quality of Life, HDI, GDP, Gross Capital Formations, World Bank

 

Introduction

Quality of life is a difficult concept to pinpoint and defines. The researchers think that due to GDP per capita and other such economic statistics, the standard of living of countries and citizen can best be addressed.

It is possible to know the quality of life of people by observing a number of characteristics such as a decent car, place of living, food, health care and education etc., yet the economist refers to the average GDP as a measure of standard of living.

Achieving a better quality of life (QoL) is the goal of every economy across the globe. The United Nations (2015) introduced Sustainable Development Goals (SDG’s) for their 2030 agenda, which is based on 17 goals. These goals cover an array of socio-economic, equality, health, and environmental issues. It starts from the eradication of poverty to the improvement in health, towards the standard of life and attainment of better education and carbon-free environment that affects the life of the individuals directly or indirectly. Thus, SDGs focus on those dimensions which are closely connected with the betterment of the QoL (UN, 2017).

According to UNDP (1998), QoL involves the fulfilment of basic needs, which include health, education, and standard of living, while Sirgy et al. (2004) called it a qualitative measure of well-being which includes consumer, health, social, economic and physical well-being. Previously, scholars and economists measured QoL through GDP per capita (Nussbaum & Sen, 1993; Majeed & Mumtaz, 2017), but Anand and Sen (1999) and Majeed (2019) stated that QoL is not only affected by income rather there are also some other major sociological, physiological and human dimensions of individual’s life which were being ignored previously. Therefore, Sirgy et al. (2004) stated that QoL is a subjective measure, and to empirically measure it; the best proxy is the “Human Development Index (HDI)”.

 

Objectives of the Study are to

i.      Analyze the main determinants of Quality of Life.

ii.    The impact of independent variables, and their magnitude, on the Quality of Life.

iii.   The determinants were then used to test the hypothesis shown below.

 

The Hypotheses

i.         There is a positive correlation between HDI and Standard of Living.

ii.       There is a positive correlation between HDI and EDU.

iii.     There is a positive correlation between HDI and GDP.

iv.     There is a positive correlation between HDI and Gross Capital formations.

v.       There is a positive correlation between HDI and Urbanizations.

vi.     There is a positive correlation between HDI and Physicians.

 

Literature Review

The Human Development Index (HDI) was initiated by the United Nations in 1990, and it became global phenomena. HDI hereby offer answers to the question about the living standard of the people.  According to the United Nation HDI consist of three indicators such as Health, Education and standard of living.  Life expectancy at birth is used as a measure of health indicators. The second indicator, education, is measured as the mean year of schooling and average years of schooling, while the gross national income per capita is used as a measure for standard of living. These are shown in the figure below.

Figure 1: Components of Human Development Index

 

The Human Development Index (HDI) is used to intake the develop ent status of a country. HDI was initiated in 1990 and used across the globe to measures different development indicators such as education level, life expectancy and standard of living of the county people. The critic of HDI indicates that too few indicators of HDI are used as chosen randomly (Berenger & Verdier-Chouchane, 2007).

Economist across the globe used an index of indicators to measured HDI. The nine indicators are i. Marerial well being, ii.  Political stabilities, iii. Security, iv. Community life, v. family life, vi. Climates, vii. Gender equality, viii. Freedom of expressions and ix. health (Kenny, 2005).

Similarly, income supports quality of life, and studies have argued that income per capita is the best measure for QoL (Nussabaum & Sen, 1993; Majeed & Mumtaz, 2017); however, as pointed out by Anand and Sen (1999) and Majeed (2019) that although GDP per capita positively affects QoL, it is not the only factor as it does not capture the sociological and psychological aspects of human life. Among these factors are health, education, capabilities of individuals, provision of opportunities, freedom to choose, and live their life accordingly, which enhances their standard of living and thus improves human development.

Besides income, education is the major determinant of QoL, which is also highlighted by Anand & Sen (1999). Education enhances QoL by increasing the knowledge and skills which support access to jobs and enhanced productivity (Majeed and Khan, 2019). Education also equips with the power to decide and the freedom to choose among different alternatives (Nourzad & Powell, 2003; Majeed, 2019).

Adverse health status also affects the QoL. The lack of medical facilities and the number of physicians available per person has a determinantal effect on QoL. For human development, the number of physicians per person is important as it reflects the development of a country (Nourzad & Powell, 2003; Asongu, 2013).

Urbanization improves QoL through the availability of better facilities, such as health, education, improved infrastructure, and employment opportunities. Urbanization has a great influence on HDI (Nourzad and Powell, 2003). Urbanization negatively affects QoL.  The reason is that overpopulation put pressure on the available resources. Migration leads to overcrowding and overexploitation of resources and thus having adverse effects on QoL.

Another factor documented in the literature that contributes to human development is gross capital formation. Gross capital formation represents the level of investment in an economy. Higher investment in a country leads to higher employment opportunities. The increase in employment level supports a better standard of living which will lead to improved QoL. Therefore, the model constructed can be represented as below,

𝑄𝑜𝐿𝑖𝑡 = (Standard of living𝑖𝑡, 𝐺𝐷𝑃𝑖𝑡, 𝑆𝑒𝑐𝑜𝑛𝑑𝑟𝑦 𝑆𝑐𝑜𝑜𝑙 𝐸𝑛𝑟𝑜𝑙𝑙𝑚𝑒𝑛𝑡𝑖𝑡, 𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡, 𝐺𝑟𝑜𝑠𝑠 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐹𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛𝑖𝑡, 𝑃𝑦𝑠𝑖𝑐𝑖𝑎𝑛𝑠𝑖𝑡)

 

Table 1. Variables Description

Variables

Definition

Unit

Source

Dependent Variable

Human Development Index (HDI)

“The Human Development Index (HDI) was developed in 1990, is used to measure a country socio-economic achievement such as education level, life expectancy and standard of living of the county.

0-1

“UNDP (2019)”

Independent Variable (Focused)

Standard of Living

“The gross national income per capita is used as a measure for standard of living”.

National Income divide by population

“World Bank (2019)”

Independent Variables (Controlled)

GDP

“The produced goods and services in a country during a period of one year with its financial value is known as GDP.”

Constant of 2010 US$

“World Bank (2019)”

Secondary School Enrollment

Measures literacy among persons aged 15 years the population of the corresponding official school age.

% Net

“World Bank (2019)”

Urban Population

Urban population refers to people living in urban areas as

defined by national statistical offices.

% of Total Population

“World Bank (2019)”

Gross Capital Formation

Gross capital formation (formerly gross domestic investment) is measure as the increase in the fixed assets of the economy plus the net changes in inventories.

Constant of 2010 US$

“World Bank (2019)”

Physicians

Physicians include generalist and            specialist          medical practitioners.

Per       1000

People

“World Bank

(2019)”

 

Model, Data, and Methodology

Theoretical Model

QoL being a subjective measure, cannot be empirically estimated (Sirgy et al., 2006); therefore, the current study used the

“Human Development Index (HDI)” to measure QoL. Therefore, the QoL model can be expressed:

𝑄𝑜𝐿𝑖𝑡= 𝑓(standard of living𝑖𝑡 , 𝐺𝐷𝑃𝑖𝑡 , 𝑆𝑒𝑐𝑜𝑛𝑑𝑟𝑦 𝑆𝑐𝑜𝑜𝑙 𝐸𝑛𝑟𝑜𝑙𝑙𝑚𝑒𝑛𝑡𝑖𝑡𝑖𝑡, 𝑈𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 , 𝐺𝑟𝑜𝑠𝑠 𝐶𝑎𝑝𝑖𝑡𝑎𝑙 𝐹𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛𝑖𝑡 , 𝑃𝑦𝑠𝑖𝑐𝑖𝑎𝑛𝑠𝑖𝑡)

 

Empirical Model

To check the determinant of QoL, it is therefore important to empirically investigate potential indicators in improving QoL. Therefore, the empirical model constructed for investigating the relationships is mentioned below,

𝑄𝑜𝐿  𝛼+ 𝛼1standard of living 𝑖𝑡  + 𝛼2𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑡  + 𝛼3𝐺𝐷𝑃𝑖𝑡  + 𝛼4𝑢𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖 + 𝛼5𝑔𝑟𝑜𝑠𝑠 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛𝑖  + 𝛼6𝑝𝑦𝑠𝑖𝑐𝑖𝑎𝑛𝑠𝑖𝑡  𝜇𝑡  + 𝜀𝑖𝑡-------------------------------------------------------------------------------------(1)

Where “i” represents “cross-sections while” “t” represents “time”. QoL is quality of life that is measured by “HDI (range 0- 1, 0 is for the lowest HDI while 1 is for the highest HDI rank)”, the standard of living  is “people access to basic resources)”, education represents “secondary school enrollment (% net)”, “GDP (constant 2010 US$)” is used to capture the effect of income on QoL, urbanization (% of the total population) is used to analyze the effect of urbanization on QoL, “gross capital formation (constant 2010 US$)” is used to capture the effect of investment on QoL while “physicians (Per 1000 people)” are used to capture the effect of health expenditures on QoL. α0 represents the intercept terms, while 𝜃𝑖 and 𝜇𝑡 represent country-specific unobservable effects and time effects, respectively. The term εit is an error term.

 

Data and Methods

The current study has taken the data from World Bank (2019) and UNDP (2019). The analysis is based on the panel data of 71 UNDP member countries from 1990-2019. The decision about the countries and the time frame is based on the availability of the data. The GDP, urbanization, and gross capital formation are transformed into logarithmic form as it controls Heteroscedasticity and provides consistent findings.

The study concluded for panel analysis; the study employed different novel techniques, which include pooled OLS, random effects, fixed effects, and IV-Fixed effect (for controlling endogeneity). The incorporation of different methodologies is justified through their specific characteristics. As pooled OLS does not account for country-specific and time-specific characteristics; therefore, random effects and fixed effects were employed to control these effects. Random effects give meaningful results if there is no correlation between country-specific characteristics and repressors, while fixed effect assumes that there exists a correlation between country-specific effects and the regressors. However, the fixed effect cannot deal with the endogeneity problem; therefore, to tackle this problem; IV-Fixed effects have been employed.

 

Results and Discussion

Panel Analysis

Table-2 incorporates the results obtained from panel analysis. The techniques used are pooled OLS, random effects, fixed effects, and IV-Fixed effects, presented in Column 1-4, respectively. Column 1 shows the results of the pooled OLS technique. The results indicate that the regression coefficient of the standard of living positively contributes to a better QoL. The coefficient depicts that 1 percent increase in the availability of standard of living boosts the QoL by 0.0012 percent.  Moreover, the increase in urbanization and gross capital formation negatively contribute to the quality of life. It specifies that an increase in urban population leads to overcrowding, exploitation of the resources, and an increase in pollution, which adversely affects the quality of life.

 

Table 2. Panel Analysis

Variables

Pooled OLS

Random Effects

Fixed Effects

IV-Fixed Effects

The dependent Variable is QoL (HDI)

Standard of living

0.00018***

0.00161***

0.0490***

0.445***

-(0.0004)

-(0.0003)

-(0.0080)

(0.0610)

Education

0.00129***

0.00172***

0.00145***

0.0966***

-(0.0002)

-(0.0001)

-(0.0001)

(0.00591)

GDP

0.114***

0.0800***

0.114***

-0.0301***

-(0.0034)

-(0.0035)

-(0.0036)

(0.00235)

Gross

Capital Formation

-0.0116***

-0.003

-0.0307***

-0.00957

-(0.0022)

-(0.0026)

-0.006

(0.00684)

Urbanization

-0.0306***

-0.0599***

-0.0249***

0.00215

-(0.0063)

-(0.0038)

-(0.0024)

(0.00148)

Physicians

0.00406***

0.00896***

0.00537***

0.000356***

-(0.0020)

-(0.0016)

-(0.0014)

(0.0011)

Constant

-1.809***

-0.551***

-1.977***

-1.840***

-(0.0811)

-(0.0408)

-(0.0812)

(0.0839)

Observations

520

520

520

520

F-Statistics

564.73

-

421.14

564.73

Wald Chi2

-

1550.11

-

-

R-Square

0.862

0.9244

0.948

0.830

LM Test

0.0000

-

-

-

Hausman Test

-

0.0000

-

--

Note: Standard errors in parentheses * p < 0.1, ** p < 0.05, *** p < 0.01

 

Moreover, the regression coefficient of gross capital formation is 0.0116 percent depicting a decrease in quality of life. While the coefficient of GDP, secondary school enrollment, a number of physicians indicates an improvement in the quality of life.

The value of R-square is 0.86 percent, indicating that explanatory variables explain 86 percent of the variation in the quality of life, while F-statistics states that the explanatory power of the variables in the model is strong.

In pooled OLS technique, individual as well as country-specific characteristics are being ignored, which leads to violation of assumptions related to the error term. Random effects solve those violations by incorporating separate intercepts in the model, which is assumed to be random. This assumption of separate intercepts states that the model is exogenous. Column 2 of Table-2 displays the result obtained by applying the random effects technique, which deals with country-specific characteristics. The results indicated that the variables used in the model are significant and supported by the theory and literature. The LM test is applied to choose between pooled OLS and the random effects technique. The p-value suggests that random effects are better than pooled OLS. The p-value is significant at a 1 percent level of significance.

The random-effects technique assumes that there exists no correlation between country-specific characteristics and repressors, while fixed effects, in contrast, assume that there exists a correlation between regressors and country-specific characteristics. It controls time-invariant characteristics that can be correlated with the independent variables. Column 3 of Table-2 displays the results obtained from fixed-effects techniques. All the variables possess expected signs according to the theory depicting that the results obtained are consistent and robust across the techniques. Standard of living improves the quality of life along with education, GDP, and a number of physicians. Hausman test was employed for the selection between random and fixed effects. The null hypothesis suggested that the random effect is better. The p-value is less than 5 percent indicating the rejection of the null hypothesis. To construct the links among explanatory and dependent variables, the country-specific characteristics are important as intended by Hausman.

Moreover, one problem with the fixed effects is that it does not deal with the problem of endogeneity. Therefore, to address this problem, the study has used the IV-fixed effect technique.

 Standard of living is being instrumentalized by its lag values along with regional dummies. The endogeneity-free results are reported in the 4th column of table-2. It depicts the positive relationship between standard of living and QoL, and the result is statistically significant. Regarding the overall significance of the model, the value of F-statistics and Wald-Chi2 is depicting that all the independent variables are the true determinants of the QoL. Moreover, the value of R-square is also illustrating that almost 70- 80 percent of the variations in the QoL is being explained by the independent variables, and the model is the best fit.

Table-3 shows the level of hypothesis, as proposed above in this study, for possible acceptance and rejections based on the results of the regression analysis.

 

Table 3. Summary of hypotheses tested

Hypotheses

Accept /Reject

There is a positive correlation between HDI and Standard of Living.

Accepted

There is a positive correlation between HDI and EDU.

Accepted

There is a positive correlation between HDI and GDP.

Accepted

There is a positive correlation between HDI and Gross Capital formations.

Reject, the correlation is negative

There is a positive correlation between HDI and Urbanizations.

Reject.  The correlation is negative

There is a positive correlation between HDI and Physicians.

Accepted

 

The regression model, as calculated above, hereby resulted in the proposed model, is hereby presented as follows:

𝑄𝑜𝐿= -1.809 + 0.00018 standard of living𝑖𝑡 + 0.00128𝑒𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛𝑖𝑡 + 0.114𝐺𝐷𝑃𝑖𝑡 -0.0306 𝑢𝑟𝑏𝑎𝑛𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑖𝑡 - 0.0116𝑔𝑟𝑜𝑠𝑠 𝑐𝑎𝑝𝑖𝑡𝑎𝑙 𝑓𝑜𝑟𝑚𝑎𝑡𝑖𝑜𝑛𝑖𝑡  + 0.00406𝑝𝑦𝑠𝑖𝑐𝑖𝑎𝑛𝑠𝑖𝑡  𝜇𝑡

The coefficient depicts that 1 percent increase in the standard of living boosts the QoL by 0.00018 percent. The coefficient of GDP, secondary school enrollment, the number of physicians indicates improvement by 0.114percent, 0.00128percent and 0.00406 percent, respectively, in the quality of life, while the regression coefficient of gross capital formation is -0.0116 percent, depicting a decrease in quality of life.

 

Conclusion

QoL is one of the fundamental aspects of economic development, and to sustain it; it is important to overcome the indicators influencing it. For sustainable development, availability and affordability of educational support, better standard of living, GDP etc., is of supreme importance as it enhances QoL. Hence this study examined the role and relationship of the different indicator with QoL in 71 United Nations member countries between 1990 and 2019. To fulfil the purpose, the study used HDI to measure QoL as it is a subjective measure. The study has conducted both panels as well as cross-sectional analysis. The results are from the Pooled OLS, Random effects, and fixed effects.

The results obtained support improved QoL through the indicators used. The problem of endogeneity is addressed using IV-Fixed effects and Two-SLS. Our findings remain robust across the techniques. The IV-Fixed effects deal with endogeneity, time-variant, and time-invariant characteristics, respectively. The robustness of the results is confirmed through sensitivity analysis. The results indicate that the regression coefficient of the standard of living positively contributes to a better QoL. The coefficient depicts that 1 percent increase in the standard of living boosts the QoL by 0.0012 percent. Results are consistent with the study of ADB (2016) and UNDP (2006).

Moreover, the increase in urbanization and gross capital formation negatively contribute to the quality of life. It specifies that an increase in urban population leads to overcrowding, exploitation of the resources, and an increase in pollution, which adversely affects the quality of life.

The value of R-square is 0.86 percent, indicating that explanatory variables explain 86 percent of the variation in the quality of life, while F-statistics states that the explanatory power of the variables in the model is strong.

Therefore, based on our findings, it is suggested that the governments of all countries should ensure to divert their resources for the improvement of the standard of living of their citizen accessibility to education and health be made possible at the doorstep. Improvement in Gross domestic production enhance the quality of life; hence all measure be made for increased productivities.

The study also has some limitations. The study was unable to conduct regional as well as country-specific analysis because of the data limitations in the case of the standard of livingly managed. The analysis is based on only 71 “United Nations member countries,” which can be extended to other member countries based on the availability of data.

 


 

 


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