Exploring and Measuring Quality of Life Determinants of Wage Workers in Egypt: A Structural Equation Modelling Approach

This paper aims to identify the major significant dimensions that contribute to the overall quality of life (QOL) of wage workers in Egypt. As the QOL is a complex, multidimensional, and interdisciplinary concept, forty-seven indicators under five domains were chosen to investigate the QOL determinants of wage workers in Egypt, namely job characteristics, job satisfaction, ICT access, gender equality and women empowerment, and neighborhood services and utilities. Using data from the 2018 wave of the Egypt Labor Market Panel Survey, the paper employed structural equation modelling (SEM) approach to investigate the impact of proposed dimensions on the overall QOL. In addition, multi-group SEM analysis was implemented to measure how people differ in the way they assess their QOL on the basis of various moderating variables such as the geographical region of residence, age group, and years of schooling. The findings showed the significant impact of the chosen dimensions on the overall QOL. Moreover, the multi-group models showed a significant variation among compared groups, in which the weights of dimensions vary due to the differences in socio-cultural characteristics and the surrounding environment, confirming the complexity of such a concept. As people usually differ in the way they assess their QOL, which is affected by their characteristics and priorities.


Introduction
Quality of life (QOL) studies recently received great attention from not only the research community, but also from public policy, governance, planning, and management.They usually highlight the importance of spatio-geographical and socio-economic contextual factors pertaining to QOL, well-being, and happiness, as well as their impact on social and spatial inequalities and social justice (Bougouffa & Permana, 2018).QOL is usually used to assess the overall well-being of individuals and societies.It is viewed as a general sustainable development aim, through objective and subjective economic, social, and environmental profiles (Turkoglu, 2015).
Enhancing the QOL of societies is a mirror for the achievement progress of the United Nations Sustainable Development Goals (SDGs), as it is the core of sustainable development.A society's achievement of a high level of QOL will enhance economic entities, and provide a prosperous, stable, and sustainable future for its citizens and future generations, that in turn will improve the progress in achieving SDGs.Consequently, improving individuals' QOL has become a top priority for countries and cities around the world, especially after the current global challenges such as the COVID-19 and climate change crises.In addition to the Russian-Ukrainian ongoing war and its significant impact on the global economy and investment.In which the growth is expected to halt and will escalate in turn the inflation, which will eventually lead to social unrest, especially in low-income, and net food-importing developing countries such as Egypt.
Egypt aims and continually tries to enhance the QOL of its citizens through a set of successive socio-economic and political policies.It launched the 2019 "Decent Life" initiative for rural development, along with Egypt's Vision 2030 for achieving sustainable development goals (SDGs) and the national project for the development of the Egyptian family (2021)(2022)(2023).In addition to, the 2016 national program for economic and social reform and the development of the fourth generation of cities across Egypt including the new administrative capital city in a bid to improve life quality, increase residential area, and absorb the population surge in the most populous Arab country.This interest aims to make cities more competitive, and more environmentally sustainable, which in turn enhances the QOL of their citizens in the various life aspects, such as education, health, living standard, job quality and opportunities, socio-economic opportunities, and residential and community satisfaction.
The aim of this study is to investigate the complex and multi-dimensional nature of the concept of QOL, which can be measured through multiple social and spatial factors.The empirical investigation uses data derived from the 2018 Egypt Labor Market Panel Survey (ELMPS) and carries out the structural equation model (SEM).The unit of analysis is the wage workers, who represent more than two-thirds of the total employment in Egypt (CAPMAS, 2021).In specific, this study aims to identify the impact of job characteristics and job satisfaction on individuals' well-being and overall QOL, along with factors that reflect socio-cultural and residential characteristics, for the purpose of policy evaluation and improvement.These themes are reflected in the ELMPS questionnaire, meeting the requirements of the research investigation.In other words, the study aims to highlight and confirm the association between the QOL of the wage workers with both job/working and residential/community conditions.They are complementary factors for assessing the overall QOL of individuals, and both cannot be neglected.Accordingly, the specific objectives and hypotheses of the present paper are as follows: OBJ1.To identify significant domains and related indicators considered essential for measuring QOL.In specific, What do the indicators relating to job characteristics, job satisfaction, ICT access, gender equality and women empowerment, and neighborhood services and utilities tell about the overall QOL of wage workers in Egypt?; OBJ2.To regroup the significant indicators, using latent variables to identify differences in each major facet/domain of QOL; and OBJ3.To ascertain the differential for the socio-spatial determinants of QOL because of the geographical region of residence, age group, or years of schooling.
Considering the complex and multi-dimensional nature of the concept of QOL, and the study's focus on wage workers, this paper approached the OBJ1 and OBJ2 by adopting the structural equation modelling (SEM) approach, to test the following hypotheses: H1.Do good Job characteristics (JOBCH) promote the level of the QOL? H2.Is job satisfaction (JOBSL) significantly reflected in the overall QOL? H3.Does expanding Information and Communication Technology (ICT) use improve QOL? H4.Is gender equality/women empowerment (GEWE) considered one of the good QOL practices?Are reducing gender inequality and promoting women's rights in education and employment significant indicators of good QOL?H5.Is enhancing the accessibility to education and health facilities (DTEHF) a significant determinant of the quality of neighborhood services and facilities and the overall QOL? H6.Is offering public services (PS) efficiently a significant determinant of the quality of neighborhood services and facilities and, in turn, the overall QOL? H7.Does offering Water supply and utility (WSU) contribute to the quality of neighborhood services and facilities and, in turn, the overall QOL? H8.Is offering good and efficient neighborhood services and facilities (NSF) a key determinant of the overall QOL?
For the OBJ3, the constructed SEM model was then used to estimate the measurement invariance across distinct groups by using multiple-group SEM (MGSEM), and that to test the following three hypotheses through comparisons: C1.Is there a significant difference in the overall QOL between geographical regions in Egypt?C2.Is there a significant difference in the overall QOL between individuals considering their age?C3.Is there a significant difference in the overall QOL between individuals considering their years of schooling?OBJ3 aims to test the effect of a set of moderating variables/moderators by implementing multi-group analysis.That, in turn, will reflect the variation among individuals in assessing their QOL due to a set of socio-spatial differences.In addition, confirming how the chosen domains of QOL can be affected by different factors, such as geographical region of residence, age, and years of schooling.That, in turn, confirms how the notion of QOL must be defined and understood through a complex and multidimensional lens, reflecting several aspects of an individual's and society's life, in which various socio-demographic and geo-spatial factors are correlated to QOL, along with external factors, such as global economic crises.
To our knowledge, this is the first study aiming to assess the QOL of wage workers in Egypt through a set of objective and subjective indicators derived from the 2018 ELMPS data.Furthermore, it explores not only the effect of working/job conditions, but also the socio-cultural and residential characteristics on the overall QOL.The remainder of the paper is organized as follows.Section 2 gives an overview of the QOL, concepts, and indicators.Section 3 presents the data and research methodology whereas Sect. 4 handles the data analysis and main results.Finally, Sect. 5 concludes the research and gives directions for future research.

Background on the Complex Nature of Quality of Life
Quality of life (QOL) is a complex, multidimensional, and interdisciplinary phenomenon in its nature that is difficult to define, conceptualize, and measure (Shu et al., 2022).It has been debated in various areas of social sciences including sociology, psychology, economics, demography, and politics.In addition to environmental sciences, planning and development, human geography, urban and regional studies, medicine, and public health (Gerasymczuk & Bacylewa, 2018;Mohsen et al., 2022;Boumahdi & Zaoujal, 2023).The determinants of QOL vary in terms of conceptual foundations, dimensions, indicators, and units of analysis.In addition, and since these determinants are usually correlated with each other, measuring the QOL requires a comprehensive analytical framework that includes a large number of components, that in turn allow assessing how their interrelations shape people's lives and well-being (Lawrence, 2011).
QOL embraces many concepts, such as happiness, life satisfaction, and well-being.In literature, they are sometimes used interchangeably (Bowling & Windsor, 2001;Camfield & Skevington, 2008;Yaya et al., 2019).It is generally used to assess the overall well-being of individuals and the happiness and satisfaction of societies (Haller & Hadler, 2006).But it should be noted here that QOL should not be confused with the "standard of living" notion, which is primarily based on income.However, the indicators of QOL include many aspects besides wealth and employment, such as the built environment (e.g., neighborhood conditions and resources), social resources (e.g., social belonging, family relations, community life and social support networks), social and gender equality, physical and mental health, housing/material well-being, education, work and productive activity, safety and security, and recreation and leisure.In addition, more abstract aspects such as political stability, human rights, freedom, and happiness are often related to QOL (Alvarez & Müller-Eie, 2022;Felce, 1997;Hagerty et al., 2001;Nevado-Peña et al., 2019;Nikolova, 2016;Schalock, 2004;Štreimikienė & Barakauskaitė-Jakubauskienė, 2012;Verdugo et al., 2005;Zeini, 2023).These domains/aspects are at the core of SDGs, confirming the interlinkage between achieving a high level of QOL and delivering SDGs.
Although most scholars view QOL as a construct of separate areas of life, called domains, such as health, financial situation, job, housing, environment, and leisure (Bougouffa & Permana, 2018;Felce, 1997;Hagerty et al., 2001;Schalock, 2004;van Praag et al., 2003;Verdugo et al., 2005), there is no agreement in the literature on a single specific definition or precise approach to define or evaluate QOL.There are also no clear criteria as to which aspects of society should be enhanced to improve life conditions.However, Allardt (1993) argued that all relevant life domains could be summarized with the triad "having, loving, and being".In view of that, QOL is usually defined as "goodness of life" and how to live with the surroundings happily and successfully.It is generally a key constituent in the sustainable development of societies (Nevado-Peña et al., 2019).Consistent with this, the World Health Organization (WHO) has defined QOL as "individuals' perception of their position in life in the context of the culture and value systems in which they live, and in relation to their goals, expectations, standards and concerns."(WHO, 1997).QOL has become an all-around concept to measure and promote the human lifestyle and provide a better and more sustainable living environment.Accordingly, assessing QOL is a powerful tool for community development planning.It is usually used to monitor key indicators that encompass the social, economic, environmental, health, and political dimensions of the society (Rodríguez-Domínguez et al., 2022;Talmage, 2020;Tawfik et al., 2011).
Historically, QOL research stems from two rather opposing approaches: the Scandinavian "level of living" approach and the American approach.However, many argued that integrating both may give a comprehensive perspective of QOL (Drobnič et al., 2010;Sirgy et al., 2006).
The Scandinavian approach drew on the tradition of Swedish welfare research and thus had a strong focus on objective living conditions.Moreover, the subjective evaluation of living conditions was not of interest and was even suspected to be biased.Accordingly, QOL depends crucially on "the individual's command over-under given determinantsmobilizable resources, with whose help he/she can control and consciously direct his/her living conditions" (Erikson, 1974).These resources can be either economic/tangible (e.g., wealth, income, and education) or non-economic/intangible (e.g., social bonds and relations) (Erikson, 1993).On the other hand, the American approach drew on individuals' subjective evaluations to assess QOL.This approach is the most commonly used in previous studies.Accordingly, the focus was on the individual's needs and preferences.In which living conditions as perceived by individuals and their subjective evaluation, either positive or negative, make up the core of QOL.
To sum up, the Scandinavian approach is based on objective indicators to assess the QOL, while the American approach is based on subjective indicators.The former can be therefore observed and collected by other people such as income, years of schooling, housing characteristics, and the quality and availability of public services and utilities.Whereas the latter cannot be collected by another person as they refer to a person's feelings such as their feelings about their family and society, their satisfaction with their residence place, and work conditions, as well as their perception and awareness toward society and social issues such as social protection, government reforms programs, and gender equality and women's empowerment (Ballas, 2013).
QOL studies either choose one of the previously mentioned approaches/dimensions or a combination of both.Combining both approaches is more recommended by many researchers, as they argue that both indicators are complementary to each other and lead to a comprehensive perspective of QOL (Cummins, 1997;Lee & Marans, 1980;McCrea et al., 2006).None of the two informational sources can be dismissed easily.Most influential contemporary approaches acknowledge the existence of a subjective-objective duality in QOL research.Therefore, QOL can be defined as a product of a set of complex objective and subjective indicators/factors, and one cannot improve QOL separately (Cummins, 1997;Haslauer et al., 2015).
According to the literature, the number of QOL domains, the related indicators, and the weights assigned to each remain ambiguous and incorporate complex-based relationships.They usually differ from one research to another (Rojas, 2006).Furthermore, the question: How important is having good indicators under each domain for overall QOL? has been addressed by two different concepts namely, domain hierarchy and domain salience.The former refers to the idea that life domains are cognitively structured in a hierarchical pyramid.Where the top of this pyramid represents feelings about QOL in general, the level below is reserved for satisfaction with the different QOL domains, and the bottom level relates to life events within different QOL domains.Whereas the latter assumes that different life domains, such as work, family, society, health, or leisure vary in salience.Where some pyramid domains can be more important than others (Sirgy, 2002).
Moreover, restricting the number of domains, and their related indicators to specific categories may be misleading and incorrect.Nevertheless, the number of such domains/indicators must be manageable, and domains should refer to obviously distinguishable information (Rojas, 2006).Indeed, determining the domains/indicators of QOL usually depends on the nature of the research, the study objective(s), and the target population, as well as the available data, which may restrict the focus of the study.In addition, the domains/indications may vary due to their importance.Some domains can be more important than others.In this aspect, analytical analysis can help in assigning the weights for each.
Since the focus in this study is on the QOL of Egyptian wage workers, indicators that reflect both job characteristics and job satisfaction were included in the model.In addition to three other domains namely, digital devices access/ICT use, gender equality and women empowerment, and community facilities and services.The first three domains reflect the economic and technological aspect/factors, while the fourth indicator reflects the cultural aspect toward gender equality and women's empowerment, and the last one reflects the environmental/community aspect.In addition to identifying and testing the significance of the five chosen domains and their related indicators, this study aims to test if there is a significant difference between geographical regions and represent the results by controlling other socio-demographic variables such as age group and years of schooling, using multigroup SEM (MGSEM) analysis, to fit models on data comprising groups.The following section presents the research methodology in detail, followed by the analysis results and discussion and conclusion.

Research Methodology
This section consists of three subsections, the first subsection presents the study area, target population and data availability.The other two subsections describe the study data, followed by an overview of the structural equation modeling (SEM) approach.

Area of Study, Target Population and Data Availability
Geographically, Egypt is situated in the northeast corner of the African continent.The total area of Egypt covers approximately one million square kilometers; however, only 6 per cent of this area is inhabited, and most of the country is desert.Administratively, Egypt is divided into twenty-seven governorates and around two hundred and thirty-one cities.Three governorates are totally urban namely Cairo, Port-Said and Suez.The remaining twenty-four governorates are subdivided into urban and rural areas.Ten governorates are found in the Nile Delta (Lower Egypt), nine are located in the Nile Valley (Upper Egypt), and the remaining are frontier governorates located on Egypt's western and eastern boundaries (CAPMAS, 2019).
Egypt ranks 14th among the world's most populous countries and 1st among the Middle East countries.The total population size in Egypt was estimated in 2021 to exceed 102 million people, approximately fifty-seven per cent of this number lives in rural areas.The population size increased by 2.6 million people each year for the last three years, and it is expected to reach 121 million people by 2030 (CAPMAS, 2022).The most recent survey for Egypt's income, expenditure, and consumption presents that thirty per cent of the population (thirty million people) are living below the national poverty line, which is the poverty line deemed appropriate for a country by its authorities (CAPMAS, 2020a(CAPMAS, , 2020b)).This percentage is expected to rise as a result of consecutive global challenges, such as Covid-19 and the Russian-Ukrainian war.Besides, climate change is contributing to poverty, food insecurity and the economy of societies, especially agricultural and rural societies.Subsequently, aiding such a huge and growing population to achieve fulfilling lives and good quality of life for the needy classes of people nationwide (i.e., poor, and marginalized individuals who live in rural and segregated areas), is an overwhelming challenge for developing countries like Egypt.It must be confronted forcefully, and with a determination that new initiatives and programs are implemented thoroughly and effectively.Which would help reduce poverty rates at the regional level and the gap between geographic regions.
Additionally, the Gini coefficient of income for Egypt was estimated to be 31.5 in 2017 (World Bank, 2022).Although the aggregate level of income inequality in Egypt seems to be relatively low and stable during the few last years, the figures mask large inequalities at the regional and governmental level (Bournakis, 2020).In more detail, there is a significant disparity at the urban and rural levels and between Egyptian governorates as well.On the urban/rural level, the value of the Gini coefficient increased in urban areas to reach 0.32 while it decreased to 0.25 in rural areas, which shows the high inequality in the standard of living in urban areas compared to rural ones (CAPMAS, 2020a(CAPMAS, , 2020b)).On the governorate level, Cairo governorate displays the highest levels of income inequality (about 0.40), and the poor represents one-third of its population.On the other hand, El-Sharqia governorate shows the lowest among the other governorates (about 0.24), and the poor population represents 13 per cent in its urban regions and 28 per cent in rural regions (CAP-MAS, 2020a, 2020b, Bournakis, 2020).This difference is actually due to the nature of each governorate and its demographics, urbanization rate and the extent of urban expansion in the desert back.On the other hand, these findings reveal a high level of socio-economic disparities, which negatively affect the level of QOL.Therefore, measuring QOL at the micro-level is vital to assessing socio-spatial inequalities and improving living standards in lagging and disadvantaged areas.This is consistent with the argument that QOL is affected by both social characteristics and environmental factors at various spatial scales and their interactions together (Bougouffa & Permana, 2018;Cummins, 1997).
Concerning QOL data and reports, there are no official national reports or surveys conducted to assess the QOL and related concepts (e.g., happiness, human well-being, and life satisfaction) in Egypt in depth.The availability of QOL data is essential in evaluating and reconstructing governmental policies to improve the living conditions of its citizens based on their actual needs and demands.However, on the international level and according to the 2020 Human Development Report, Egypt ranked 116th according to the Human Development Index (HDI) out of 189 countries, jumping 1 spot compared to the 2018 report.This report looks beyond the human development index and into the quality and sustainability of human development, as well as at how all forms of social inequalities and environmental performance affect countries worldwide.HDI is a summary measure of average achievement in key three dimensions of human development: a long and healthy life, being knowledgeable and having a decent standard of living.Each dimension composing the HDI is assessed by one or more specific variables.Specifically, the health dimension is assessed by life expectancy at birth; the education dimension is measured by the means of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age.Finally, the income dimension is measured by the logarithm of the gross national income per capita.It is closely related to QOL; however, the latter is broader and more comprehensive.
Additionally, the Human Development Report showed that Egypt ranked 102 nd according to its Gross National Income (GNI) per capita out of 189 countries and 108th out of 162 countries according to the gender inequality index (GII), which measures gender-based inequalities in reproductive health, empowerment, and economic activity.This indicates that given the level of its economy, Egypt has a lot of potentials to improve social protection, gender equality and women empowerment, education, and governance systems, as well as transform economic growth into investments resulting in further progress for human development.
Furthermore, by looking at the 2021 CEOWORLD Magazine report, Egypt ranked 8th in the Arab world and 69th globally in the best quality of life.The CEOWORLD magazine analyzed and compared 165 countries across 10 key categories: affordability, economic stability, family-friendly, a good job market, income equality, political neutrality & stability, safety, cultural influence, well-developed public education system, and well-developed public health system (Ireland, 2021).However, happiness in Egypt is slowly on the rise during the last five years, according to the 2022 World Happiness Report commissioned by the United Nations Sustainable Development Solutions Network and published by the Earth Institute at Columbia University.It uses global survey data to report how people evaluate their own lives overall in more than 150 countries worldwide.Egypt ranked 129th among the 146 nations surveyed in the report, jumping ahead 3 spots compared to the 2021 report.While Egypt is moving up the ranks, the country still ranks among the top countries with a slight rise in happiness.On a scale of 1 to 10, Egypt garnered a score of 4.7 between the years 2014 and 2016, representing 104th out of 155 surveyed countries, but averaged a score of 4.3 between the years 2019 and 2021 (Helliwell et al., 2022).
Concerning the target group in this study, the 2021 Egypt's Central Agency for Public Mobilization and Statistics (CAPMAS) report indicates that permanently paid workers represent 69 per cent of total Egyptian employment compared to 68.3 per cent in 2020, with a slight increase of 0.7 per cent.The percentage of permanently paid workers in the government sector recorded the highest percentage, reaching 98.6 per cent, followed by workers in the public sector and public works with 95.6 per cent, and the lowest percentage of permanently paid workers in the private sector outside of establishments (informal sector) at 27.3 per cent.The percentage of workers with a legal contract in the government sector reached 98.3 per cent, followed by workers in the public sector and public works with 94.5 per cent, and then workers in the private sector inside of establishments by 30.8 per cent.This percentage reaches its lowest level among workers in the private sector outside of establishments, reaching 0.9 per cent of the total waged workers in this sector.
In addition, the percentage of wage workers who participated in social insurance in the government sector was 97.2 per cent, while it reached 36.3 per cent in the private sector.The percentage of employees participating in health insurance in the government sector reached 97 per cent while it reached 28.7 per cent in the private sector within the establishments in 2021 (CAPMAS, 2021).This report includes a summary of permanently paid workers in Egypt.Therefore, to assess the overall QOL for wage workers, detailed data is needed to give us more insights into the other types of employees including intermittent, seasonal, and temporary employees, as well as more data about their socio-demographic and residential characteristics.
Consequently, in this study and due to the unavailability of official national and detailed micro-data as stated before, as well as the multiplicity of global indices that depend on different samples, indicators, weights, and accounts, the data employed in the structural equation model were drawn from the 2018 ELMPS survey.ELMPS provides detailed information about the working and living conditions of wage workers in Egypt (OAMDI, 2019).1

Data Description
The Egypt Labor Market Panel Survey (ELMPS) is a comprehensive longitudinal and nationally representative panel survey.It is carried out by the Economic Research Forum (ERF) in collaboration with Egypt's Central Agency for Public Mobilization and Statistics (CAPMAS).ELMPS 2018 is the 4th round of this survey that is repeated almost every six years.It was also carried out in 1998, 2006and 2012(OAMDI, 2019)).It provides more detailed information about parental background, schooling and qualifications, housing characteristics, facilities and services accessibility, residential mobility and migration, marriage and fertility, women's empowerment, job dynamics and earnings, and savings and borrowing behavior.In addition to its usual focus on employment and unemployment in typical labor force surveys.The 2018 ELMPS includes 15,746 households with 61,231 individuals, using a stratified random sample, with strata defined by governorates, and urban/rural location.Therefore, among all available national surveys, the 2018 ELMPS is the most suitable survey for accomplishing the objectives of this research.
The study's sample is limited to the characteristics of the head of households who have been interviewed in ELMPS 2018.The analyses draw on a sub-sample of wage workers, who constitute about 49 per cent of the total household surveyed in ELMPS 2018.Employers, self-employed, unpaid family workers, and informal sector workers were excluded, as the job data was missing.Those who are younger than 18 years old were also excluded.Before analyzing the data, a reliability test was implemented to make sure that the questionnaire items are reliable, and the findings are consistent.The questionnaire items are reliable when Cronbach's alpha value for each item is higher than 0.7 (Hinton, 2014;Nunnally, 1978).Table 1 shows the results of the reliability test.
Forty-seven variables were split into seven QOL domains, namely as follows: (1) job characteristics, (2) job satisfaction, (3) ICT access, (4) gender equality and women empowerment, (5) accessibility to education/health facilities, (6) availability of public services, and (7) water supply and utility.The last three domains can be grouped under the neighborhood services and facilities domain, which in turn reflect the quality of the standard of living.Table 2 presents definitions for all respective variables/indicators under each domain, type of indicator and data type.Table 6 (in the Appendix) presents the variables' modalities.This study employed then the structural equation model to investigate the impact of QOL determinants on the overall QOL of wage workers in Egypt.Both SPSS and Amos Graphic software were used to accomplish the study's objectives mentioned before.

Structural Equation Modelling Approach
Structural equation modelling (SEM) is a statistical multivariate and data science technique that reconciles factor analyses, regressions, and path analysis.In which multiple causal relationships between measured and latent variables may be simultaneously specified in terms of direct, indirect, and total associations (Hair et al., 2021).SEM is a nearly 100-year-old method that has progressed over three generations.The first generation of SEMs developed the logic of causal modeling using path analysis.SEM was then morphed by the social sciences to expand its capacity by including factor analysis.The third generation presents the "structural causal model", and the "integration of Bayesian modeling" (Fan et al., 2016).
SEM is simply a methodology for representing, estimating, and testing a network of relationships between variables, both measured variables and latent constructs.It is an extremely comprehensive and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique.It has become a very popular data-analytics technique in social sciences.Moreover, it is proposed recently to combine SEM with machine learning algorithms with the aim of building an explainable and persuasive model (Li et al., 2021).
Basically, SEM aims to generate strong, explicit, and distinct links between theoretical and empirical ideas (Grace et al., 2010).Its strengths are twofold: (a) it contains a measurement model with latent components/constructs to account for measurement errors, and (b) its ability to disentangle causal relationships and test competing models and theories.It provides a scientific framework to interpret complex networks involving large numbers of dependent and independent variables with multiple complex chains of effects in a single run.Compared to traditional statistical techniques, SEM is usually used for building scientific knowledge from evidence.It is mainly concerned with building and evaluating models to extract a scientific understanding of complex systems.In other words, due to the complexity of social reality, SEM is a powerful analysis tool because it enables analyzing causal relationships between multiple variables (McLeod et al., 2015;Schreiber et al., 2006).
However, like any technique employed in data analysis, SEM presents certain deficiencies.One of the main limitations of SEM relates to the assumption that the sample data follows a multivariate normal distribution so that the means and covariance matrices contain all the information.The use of too many latent variables in the equation may lead to poor instruments.To overcome such limitation, multiple indicators and multiple causes models can be applied to scale down the number of latent variables.Moreover, any good fit model may still have the omission of vital variables.Hence, SEM is impotent in terms of detecting feasible omitted variable bias problems in model estimation.Herein lies the power of regression analysis.Therefore, the main power of SEM pertains to its ability to 'assess the fit of theoretically derived predictions to the data' (Ramlall, 2016).
To further explain the process of developing and analyzing a SEM, Fig. 1 presents a flowchart of the basic steps of SEM.In general, implementing a SEM goes through a logical sequence of steps, including (1) model specification and identification; model estimation; model evaluation and testing; and model modification (usually needed).These steps are usually iterative.Problems at a subsequent step may require returning to a prior step.The principal goal of this iterative process is to discover a model that makes theoretical sense and has acceptably close correspondence to the data.Once uncovered such a model, the researcher can comfortably interpret the results (Schumacker & Lomax, 2010;Yu et al., 2022).
In more detail, SEM is usually a two-step procedure.One is Exploratory Factor Analysis (EFA).The other is Confirmatory Factor Analysis (CFA).Following the principles of the SEM approach, the EFA identifies the composite/latent variables which constitute the chosen previously mentioned dimensions of QOL in the context of this study.As the SEM has the advantage of supporting latent variables that are unobservable variables; so, the observable indicators/variables can be used to estimate them in the model (Aref & Okasha, 2020;Gefen et al., 2000).
EFA refers to all statistical methods which summarize data by a set of composite/latent variables.Principal component analysis (PCA), factor analysis (FA), canonical analysis and multiple correspondence analysis (MCA) are examples of EFA methods.For qualitative data (e.g., multiple nominal or ordinal variables), it is strongly advised to use categorical principal component analysis (CATPCA).The CATPCA gives the freedom to specify the measurement level of each indicator used in the analysis.It utilizes a process known as optimal quantification to assign numeric values to the categorical variables.The quantification of the variables is carried out in a way so as to account for the maximum variance in the quantified variables (Linting et al., 2012).
SEM uses graphical illustrations to link the relationships between measured variables/ indicators and composite/latent variables, and between composite/latent variables themselves, and that is based on the study's objectives (Wynne, 1998).Furthermore, it allows researchers to test the differences in the parameters of the model across different groups using the capability of multi-group analysis (MGSEM).Compared to traditional statistical methods that normally utilize one statistical test to determine the significance of the analysis and the reliability of the results, SEM relies on several statistical tests to determine the adequacy of model fit to the data.In other words, to obtain reliable and valid results from the SEM model, its goodness of fit must be tested through a set of fit indices.The value of these indices should exceed the recommended cut-off level, presented in Table 3, to demonstrate that the model fits the data well (Hooper et al., 2008;Kline, 2011).

Data Analysis and Results
This section consists of two subsections.The first section presents the general model.Whereas the second section discusses the results of comparison models using the multigroup SEM (MGSEM) analysis to compare and test the differences in the parameters of the model across different groups by controlling a set of spatial and socio-demographic factors.Their values range from 0 and 1.They must be greater than 0.95 to denote a good fit

Parsimonious normed fit index
Its value should be greater than 0.5

Basic Structural Equation Modelling
Structural Equation Modeling (SEM) was implemented to develop, validate, and test the structural model.Firstly, the exploratory factor analysis (EFA) model was implemented using SPSS 28, precisely the CATPCA technique, as the included variables were either nominal or ordinal.Secondly, confirmatory factor analysis (CFA) using maximum likelihood estimation (MLE) was employed to assess the model fit and the influence of the chosen domains on the overall QOL of wage workers in Egypt.Specifically, CFA is adopted to test the proposed study's hypotheses, as depicted in Table 4, along with previous studies that use the related and similar indicators to each domain (see Table 2).
The SPSS output of the EFA yielded seven factors that constitute the seven chosen mentioned domains before.A CFA was then employed to assess the relationship between QOL domains and the overall QOL, which illustrates the standardized regression weights.Where the QOL domains are considered latent variables that are measured by a different set of observed indicators/variables.In other words, CFA was used to posit that the overall QOL construct consists of five underlying sub-constructs and each sub-construct is measured by a certain number of items using the 2018 ELMPS questionnaire data (OAMDI, 2019).
Figure 2 presents the initial structural model.This initial model includes all the proposed observed variables/indicators that affect the overall QOL.In the graphical form, observed variables/indicators were enclosed in rectangular shapes.While latent variables were enclosed in elliptical shapes.Hypotheses about the determinants of overall QOL were presented by a directional arrow.To obtain reliable results from this initial model, its goodness of fit must be tested through the previously mentioned fit indices (see Table 3).Its fit indices were as follows: χ2/df = 21.31,RMSEA = 0.053, CFI = 0.874, NFI = 0.869, NNFI (TLI) = 0.867, and PNFI = 0.825.
A set of successive modifications were made to the initial model to attain a good model.For example, the observed variables that have low regression weights with their respective latent variables were removed from the model.Public services (PS), water supply and utility (WSU) and distance to education and health facilities (DTEHF) domains were grouped under the neighborhood services and facilities (NSF) domain, which reflects the living conditions.Then, correlations were added between the observed variables' residuals to improve the model fit indices.
Finally, and as shown in Fig. 3, all the model fitness indices are within the acceptable range.Consequently, the model can be accepted, and its results can be considered reliable and valid.Table 7 (in the Appendix) displays the unstandardized estimate and its standard error (S.E.).In addition to the critical ratio (C.R.), which is the estimate divided by the standard error.The probability value related to the null hypothesis (p-value) is displayed with the three asterisks (***), which have a value for p with a significance smaller than 0.001.
It is obvious from the final model that all the QOL domains affect the overall QOL, but with different weights.As shown JOBCH (0.7) has the highest impact on the overall QOL of wage workers.Followed by ICT (0.58), JOBSL (0.45) and NSF (0.45), whereas GEWE (0.32) has the lowest impact.That reflects the significance of these chosen domains on the overall QOL, especially the JOBCH domain, which is an important component of the overall QOL, as it affects the overall QOL highly compared to the other domains.Therefore, attention should be paid to the job characteristics domain, to improve the overall QOL.
The model illustrates that all the selected observed indicators/variables have significant loadings with their respective domains/latent variables.Therefore, improving the    2).In addition to the size of the workplace itself, which had a significant impact as shown in Fig. 3, as one of the observed indicators of JOBCH.These findings are consistent with previous studies, as improving such a domain and a set of different related indicators will lead to an enhanced QOL (e.g., Drobnič et al., 2010;el Badawy et al., 2018;Hong et al., 2015;Sirgy et al., 2006;Yeh, 2015).Compared to JOBCH, the job satisfaction (JOBSL) domain has less impact on overall QOL, which was determined by a set of subjective indicators about the working conditions and to what extent the individual is satisfied with their job and tasks assigned to him.
Fig. 3 The final model: the impact of the five QOL domains on the overall QOL However, paying attention to the JOBSL domain's indicators is important as it may give insights into what needs to be provided to enhance their working conditions and job satisfaction, and in turn the overall QOL, as many argue before (e.g., Ezzat & Ehab, 2019;Fabry et al., 2022;Lee, 2022;Pichler & Wallace, 2009;Rose et al., 2006;Viñas-Bardolet et al., 2020).Moreover, Drobnič et al., (2010) argue that good working conditions are the key element that in a straightforward manner affects people's quality of life, especially the issue of security, such as security of employment and pay which provides economic security.However, they also argue that other working conditions such as autonomy at work, good career prospects and an interesting job usually translate into high job satisfaction, which in turn increases life satisfaction indirectly.Therefore, adding more questions concerning these indicators in ELMPS's upcoming surveys is highly recommended.
The previous findings also reflect the significant weak correlation between the objective indicators of the JOBCH domain and the subjective indicators of JOBSL.This is consistent with many who argue that objective indicators in general may have a poor relationship with subjective indicators (Haslauer et al., 2015).In other words, the unmatched conditions between objective QOL and subjective QOL indicators may be the cause of the moderateto-low association between them (i.e., the unhappy rich and the happy poor).The unhappy rich are those who have good objective QOL and bad subjective QOL.While the happy poor are those who have bad objective QOL and good subjective QoL.For instance, an individual may have many advantages in his job but may express job dissatisfaction and vice versa (Cummins, 1997;Lee & Marans, 1980;McCrea et al., 2006).
On the other hand, many empirical investigations reveal a significant correlation between objective and subjective indicators of job quality.For instance, Wallace et al. (2007) examined the relation between job characteristics and overall level of life satisfaction, and they concluded that such relation is mediated by job satisfaction.Pichler and Wallace (2009) found significant relationships in the expected direction between objective indicators of working conditions of employees and overall job satisfaction, such as occupational class, type of contract, or supervision responsibilities, as well as between job satisfaction and subjective evaluations, for instance, job demands, autonomy, career prospects, or job security.Moreover, Viñas-Bardolet et al. (2020) analyzed the importance of working life on the subjective wellbeing of workers.They tackled this point of analysis from the following two perspectives: (1) how job characteristics affect job satisfaction and other life domains, namely health, education, accommodation, standard of living, family, and social life.( 2) to what extent job satisfaction contributes to the overall all life satisfaction, as compared with the contributions of the other life domains.Lee (2022) analyzed the effects of trade unions' presence in workplaces and union membership on workers' overall life satisfaction, and it is mediated by four channels namely, job satisfaction, social security, real wage, and fringe benefits.(Fabry et al., 2022).To sum up, the previous findings reflect the two main characteristics of QOL: (1) the multidimensional nature of QOL, which includes multiple domains and indicators; and (2) QOL involves both objective and subjective factors/components.Therefore, there is no agreement on one specific definition or a precise approach to measuring QOL.Therefore, and consequently, previous empirical investigations' results vary because of the nature and objectives of the research as well as the availability of data.
In addition to JOBCH and JOBSL, improving the ICT domain's indicators is a key element for assessing QOL.This is consistent with previous theoretical and empirical studies (e.g., Kenny, 2002;Madon, 2000;Nevado-Peña et al., 2019).In this study, ICT domain was determined by four objective indicators, namely does the individual own a Landline phone, does the individual own a desktop computer, does the individual own a laptop, notebook, or tablet computer, and how the individual connects to the internet.This reflects the importance of such domain as an indication of the availability of ICT infrastructure and individuals' access to basic ICTs to facilitate their lives and working conditions, especially in modern information societies.Moreover, ensuring equal access to technology and digital assets is a key factor in promoting social inclusion, well-being, and QOL as well as reducing social and economic inequalities present in society.
The findings also confirm the importance of the neighborhood services and facilities (NSF) domain as one of the factors for assessing the overall QOL.This is consistent with previous studies (Ibem & Aduwo, 2013;Ihsan & Aziz, 2019;Li & Song, 2009;Michalos & Zumbo, 1999;Mohit & Azim, 2012).In this study, NFS's related sub-domains and indicators reflect the quality of standard of living and to what extent the individual may be satisfied with the place of residence, and to what extent the public facilities and services are available and of good quality.The good indicators of this domain will enhance the QOL of citizens, in which government policies must ensure equal access to education and health facilities for all individuals, regardless of their income, gender, or geographical region.In addition to reducing spatial/residential inequality by ensuring equal investment in physical infrastructure, public services, and utilities.However, the weight for NFS is low compared to the JOBCH and ICT.This is not consistent with the study by Bougouffa and Permana (2018) who showed that the numerous NSF sub-dimensions affect different QOL domains.Overall, this is indicating the need for more policies to improve this important aspect of life for individuals, that mainly related to education, health, and residential satisfaction.This in turn may be a mirror for some sort of socio-economic inequality and spatial/residential inequality.
Finally, although Egypt has realized significant advances toward improving women's well-being and social status over the last few decades (Fakih & Ghazalian, 2015), the gender equality and women empowerment (GEWE) domain has the least impact compared to the others.The domain mainly reflects the extent to which the individuals agree with a set of statements that reflect their perception toward gender equality and women empowerment, especially equality in education and employment.GEWE was included in the model, as many scholars of subjective QOL expand their research on determinants of QOL, by including social equality in general and gender equality in particular, along with many other social factors (Tesch-Römer et al., 2008).As they argue that much attention should be paid in assessing the correlation between gender equality and its related indicators and QOL of societies and linking this with the nature and the culture of communities (Audette et al., 2018;Fabry et al., 2022).
In fact, Egypt faces significant challenges in achieving SDG 5 "Gender Equality" and has not seen improvement in a number of indicators.At the economic level, female labor force participation in Egypt is among the lowest in the world, at only 18.46% in 2019.Overall, the country's moderate Gini coefficient conceals gender inequality and geo-spatial in several areas, especially in education and economic opportunities.These figures justify the low impact of the GEWE domain.Therefore, eliminating gender inequality and empowering women through awareness programs are key determinants for promoting women's QOL and in turn society's QOL.In the end, this will improve Egypt's progress toward achieving SDGs.Overall and in brief, enhancing all the above domains and related indicators, including both working and living conditions, is important for enhancing the QOL of wage workers in Egypt.

Multi-group Structural Equation Modeling (MGSEM)
Afterwards, a more in-depth analysis was implemented to answer the third research question by conducting a set of comparisons to ascertain the differential for the socio-spatial determinants of QOL.In specific, the following three comparisons were tested: C1.Is there a significant difference in the overall QOL between geographical regions in Egypt?C2.Is there a significant difference in the overall QOL between different age groups (i.e., based on the age of the individuals)?C3.Is there a significant difference in the overall QOL between individuals considering their years of schooling?
In more detail, the aim of these comparisons is to investigate if there were significant differences between geographical regions in Egypt.Whether the geographical region of residence of wage workers had a significant impact on the overall QOL.In addition, if there were significant differences among different age groups or regarding the years of schooling of individuals.To accomplish this research objective, multi-group SEM (MGSEM) analysis was implemented.The results of the tested hypotheses are summarized in Table 5.As shown, they confirm the significant impact of such socio-spatial factors on the overall QOL.There is a significant difference among individuals when moderating variables such as geographical region of residence, age group, and years of schooling.This is in line with the claim that social characteristics, environmental conditions, and their interactions at different spatial scales all have an impact on QOL (Bougouffa & Permana, 2018;Cummins, 1997).

The Moderating Effect of Geographical Region on the Relationship of QOL Domains and the Overall QOL
For the wage workers residing in Greater Cairo in the survey period, as shown in Fig. 4, ICT had the highest impact (0.82) on the overall QOL, compared to the other domains and in comparison, to the other two geographic regions (Lower Egypt and Upper Egypt).This in turn reflects the spatial inequality in ICT infrastructure, and the difference among geographical regions in their accessibility to ICT (i.e., digital divide).JOBSL and GEWE had the same impact (0.34) on the overall QOL of Greater Cairo's residents, whereas NSF had the least impact on their overall QOL.For both Upper Egypt and Lower Egypt, the JOBCH domain had the highest impact (0.81 and 0.78, respectively), followed by ICT (0.51 and 0.45, respectively), but with different significant weights.Whereas the JOBSL domain had the same impact (0.44) in both regions.Both GEWE and NSF had a low impact on the overall QOL in the three regions.Consequently, improving QOL domains and related indicators is essential for enhancing the overall QOL.Taking into consideration the significance of the socio-cultural and economic impact of such geographical regions and how this was reflected in the impact ranking of the chosen domains, and in turn, uncovering the preferences and needs of the wage workers residing in these regions.In addition, government policies must ensure equal investment in ICT infrastructure, physical infrastructure, public services, and utilities.More awareness programs about women's issues are needed especially in Lower Egypt.By adding age groups as moderators, the model displayed significant differences between different groups as well, as shown in Fig. 5.For the wage workers who belonged to the age group (> 45), the JOBCH domain had the highest impact (0.7) on their overall QOL, compared to the other domains.That reflects their need for job stability with all the benefits that the work side can offer.ICT, JOBSL, and NSF had a higher impact as well (0.53, 0.46, and 0.45 respectively).Like for those who belong to the age group (31-45), but with slight differences in weights.Whereas for the wage workers who belonged to the age group (18-30), both JOBCH and ICT domains had the same impact (0.6) on their overall QOL, followed by NSF (0.47).That may reflect their need to cope with modern technology for enhancing their life and work life, especially in the era of modern information society, where the usage of knowledge of information and ICT is at a high level.While JOBSL had

The Moderating Effect of Years of Schooling on the Relationship of QOL Domains and the Overall QOL
When the years of schooling was added to the model as a controlling variable to assess the impact of QOL domains on overall QOL, the model displayed a significant difference among groups.As shown in Fig. 6, for those who had more than seven years of schooling, JOBCH had the highest impact on their overall QOL (0.72), followed by JOBSL (0.53) and ICT (0.52).Whereas for those with seven years of schooling or less, all the domains had an impact equal to 0.41 or less.This confirms well-educated people are more likely than less-educated people to attain a higher level of QOL in all life aspects (Edgerton et al., 2012).In other words, the QOL of individuals is usually strongly influenced by their education level and that in turn has its impact on their economic and job opportunities.Also, they are more likely to be satisfied with their working life and indicate in turn a high level of quality of working life, that in turn positively affects the overall QOL.These findings assert that ensuring equal access to good education without discrimination is a crucial indicator for achieving a high level of QOL in society and a vital goal of SDGs.

Conclusions and Recommendations for Future Research
To accomplish the first two objectives of this study, SEM was firstly employed to assess the impact of a set of QOL domains on the overall QOL for wage workers in Egypt.This study selected seven domains, that are retrieved through the related literature references, taking into consideration the available data from the 2018 ELMPS.These dimensions were re-addressed by the following five domains: job characteristics, job satisfaction, ICT access, gender equality and women empowerment, and neighborhood services and facilities.The last domain is comprised of three sub-dimensions, namely accessibility to education/health facilities, availability of public services, and water supply and utility, reflecting in turn the overall standard of living.
The findings showed that all the chosen domains significantly positively affect the overall QOL.In addition, the related chosen indicators under each domain had significant loadings with their respective domain.Therefore, improving related indicators under each domain will lead to in their overall QOL.The JOBCH domain and its related objective indicators had the highest impact on the overall QOL, confirming being in waged/paid employment is consistently ranked as one of the most important determinates of a high QoL.Followed by the ICT domain, revealing a clear relationship between technology and QOL; the digital citizen is happier and values living in regions/cities with technological capacity, which are committed to achieving sustainable growth.
Compared to all domains, the GEWE domain had the least impact, which is consistent with the figures that reported less progress in achieving gender equality, which represents one of the Sustainable Development Goals (SDGs).In this aspect, tackling gender inequality by ensuring that women have equal access to education, and economic and job opportunities has become a top priority for national governments.This will close the gender gap and increase investment in education and health for future generations, and in turn, improve society's QOL.On the other hand, this aligns with the SDGs, especially SDG3 "Good Health and Well-Being", SDG4 "Quality Education", SDG5 "Gender Equality", and SDG10 "Reduced Inequalities".
The third objective was then accomplished by implementing a set of comparisons via multi-group SEM analysis.These comparisons aim to investigate if there were significant differences between different geographical regions, as well as by using socio-demographic variables such as age group, and years of schooling as controller variables.The findings revealed that there were significant differences among the compared groups, by controlling each of the three controller variables.In other words, the socio-cultural, economic, and geographical differences affected how individuals view their overall QOL, and their priorities.
In specific, the findings confirm the significant spatial/residential inequality between different regions and the digital divide.This is in line with the claim of many that the well-educated and digital citizen is better off and can live in cities with technological capacity, which are committed to achieving sustainable economic and environmental growth.Moreover, the diffusion of internet access, together with citizens' use of it and the technological capacity of different regions, are relevant variables for measuring and developing an information society.In this aspect, more government policies must be implemented to ensure equal investment in ICT infrastructure, it serves as a veritable tool to drive social inclusiveness and reduce many forms of social segregation.This in turn will enhance the overall society's QOL and achieve significant progress in SDGs especially SDG9 "Industry, Innovation, and Infrastructure", SDG10 "Reduced Inequalities", and SDG11 "Sustainable Cities and Communities".
In relation to the contribution of this paper's results to the existing literature, the findings showed that it is important to consider a broad range of QOL domains and related indicators.Investigating the role of multiple objective and subjective domains in explaining and assessing overall QOL is vital because of its complex and multifaceted nature.Moreover, investigating different groups by controlling some variables leads to significant results.These results reflect the priorities of such groups, and their preferences and needs, resulting from the differences in social-cultural characteristics and the surrounding environment.In other words, what they seek and how they view their overall QOL, and which of the QOL domains/aspects will enhance their overall QOL.
However, overall, for the majority of surveyed individuals, both JOBCH and ICT had the most significant and positive highest impact, but with different weights.These results align with those of previous studies where JOBCH and ICT are significant determinants of QOL, that align with many of SDGs, especially SDG8 "Decent Job and Economic Growth", SDG9 "Industry, Innovation, and Infrastructure", SDG10 "Reduce Inequality", SDG11 "Sustainable Cities and Communities".Overall, this study confirms how achieving progress in the chosen domains not only enhances the QOL of individuals but is also a mirror of progress in achieving many of SDGs.
Certainly, this research has some limitations which may serve as opportunities for future studies.The unavailability of in-depth micro-data that reflects the wide range of QOL domains and its related indicators relevant to this concept limited this study.On the other hand, it led to limiting the study to the subgroup, which is wage workers.Employers, selfemployed, unpaid family workers, and informal sector workers were excluded, as the job data were missing.The availability of such data can give us the opportunity to investigate the difference among individuals based on their employment status and type/sector of work.
Consequently, a well-constructed official national survey that assesses the QOL of Egyptian citizens is highly needed, that includes all the aspects of QOL, like the European Quality of Life Survey (EQLS).The availability of such a survey can help the Egyptian government, policymakers, and other stakeholders to (1) recognize groups at risk and issues of concern, (2) monitor trends by providing reliable and homogenous indicators on these issues, (3) contribute to country policy development on living conditions, (4) measure the country's progress toward SDGs, and (5) track key trends in the quality of people's lives over time, taking into consideration any external factors such as the global economic crises.
A QOL assessment section may be included in the ELMPS survey in the coming waves, by including a multiple set of objective and subjective questions.For example, questions that reflect their residential satisfaction, community life, safety conditions, work-life balance, economic capacity, social involvement, community participation, political and economic stability, government reforms programs, etc.A well-designed and in-specific questionnaire can be accomplished in future studies to assess the quality of life of a specific target population (e.g., informal sector workers); as the research findings showed that each sub-population had its needs and preferences, which of course is reflected in the weights of the domains.
Finally, more in-depth analysis can be conducted to investigate the causal relationship among QOL domains and how they are interrelated in a complex manner, taking into consideration the joint effect of several socio-demographic factors and external factors such as the ongoing global challenges such as Covid-19, the Russian-Ukrainian war, and climate change, that may have its significant impact on QOL of individuals and societies.Based on the findings of this research, it is expected that some countries/individuals are more prone to poor QOL than others during global challenges/crises due to their social and demographic characteristics, economic and financial status, and geographical positioning.

Fig. 1
Fig. 1 Structural equation modelling process Job characteristics (JOBCH) promote the level of the QOL in a positive-significant waySirgy et al. (2006),Drobnič et al. (2010),Fakih and Ghazalian (2015),Hong et al. (2015);Yeh (2015), el Badawy et al. (2018), and Dibeh et al. (2019a) H2 (JOBSL ← QOL) Job satisfaction's indicators (JOBSL) are significantly reflected in the overall QOL Rose et al. (2006), Pichler and Wallace (2009), Ezzat and Ehab (2019), Dibeh et al. (2019a), Viñas-Bardolet et al. (2020), Fabry et al. (2022), and Lee (2022) H3 (ICT ← QOL) Expanding the usage of Information and Communication Technology (ICT) positively improves the overall quality of life Madon (2000), Kenny (2002), and Nevado-Peña et al. (2019) H4 (GEWE ← QOL) Gender equality/women empowerment (GEWE) is a significant determinant of the overall QOL Tesch-Römer et al. (2008), Fakih and Ghazalian (2015), Audette et al. (2018), and Fabry et al. (2022) H5 (DTEHF ← NSF) Enhancing the accessibility to education and health facilities (DTEHF) is a significant determinant of quality of neighborhood services and facilities, and in turn the overall QOL Michalos and Zumbo (1999), Li and Song (2009), Mohit and Azim (2012), Ibem and Aduwo (2013), and Ihsan and Aziz (2019) H6 (PS ← NSF) Offering public services (PS) in an efficient way is a significant determinant of the quality of neighborhood services and facilities and in turn the overall QOL H7 (WSU ← NSF) Offering Water supply and utility (WSU) contributes to the quality of neighborhood services and facilities and in turn the overall QOL H8 (NSF ← QOL) Offering good and efficient neighborhood services and facilities (NSF) is a key determinant of the overall QOL related indicators to each domain, and especially JOBCH, will have a significant positive impact on the level of QOL.In this study, JOBCH was determined by a set of significant objective indicators.For instance, what is offered to individuals by the work

Fig. 2
Fig. 2 The initial model: the impact of QOL domains on the overall QOL weights of the QOL domains are equal among individuals regardless to their years of weights of the QOL domains are different among individuals due to the difference in the years of schooling 4.2.2The Moderating Effect of Age on the Relationship of QOL Domains and the Overall QOL

Fig. 4 Fig. 5
Fig.4The impact of the five QOL domains on the overall QOL by moderating region of residence Fig.6The impact of the five QOL domains on the overall QOL by moderating years of schooling

Table 1
Reliability statistics of the relevant variables for each domain

Table 3
Goodness-of-fit indices of SEM, and acceptable fit

Table 4
The proposed study's hypotheses Hypothesis

Table 5
Results of Multi-Group SEM (MGSEM) Analysis Test Comparison# Hypotheses