MEASURING QUALITY OF LIFE: ECONOMIC,SOCIAL, AND SUBJECTIVE INDICATORS

Thinkers have discussed the “good life” and the desirable society for millennia. In the last decades, scientists offered several alternative approaches to defining and measuring quality of life: social indicators such as health and levels of crime, subjective well-being measures (assessing people's evaluative reactions to their lives and societies), and economic indices. These alternative indicators assess three philosophical approaches to well-being that are based, respectively, on normative ideals, subjective experiences, and the ability to select goods and services that one desires. The strengths and weaknesses of the various approaches are reviewed. It is argued that social indicators and subjective well-being measures are necessary to evaluate a society, and add substantially to the regnant economic indicators that are now favored by policy makers. Each approach to measuring the quality of life contains information that is not contained in the other measures.     

Basic Minimum Needs,Quality of Life and Selected Correlates: Explorations in Villages in Northern India

For those involved in international development, one of the major goals is an improvement in the quality of life of the poor. Bhargava and Chakrabati (1992: 133) see the “primary objective of development at any given time is to improve the quality of life”. Indeed, the mission statement for an international development organization explicitly commits itself to the improvement of the quality of life for the “poorest of the poor” (DID, 1994). Social indicators, as “transeconomic” measures of quality of life, have “become an integral part of 'development indicators”' (Kao and Liu, 1984: 400; see, also Kahn, 1991). The connection between quality of life and development extends beyond the Third World. For example, in the U.S. Myers (1987) found quality of life influenced inmigration to Austin, Texas, thus affecting its economic development. Undoubtedly the majority of the connections between social indicators and development has been examined at the macro, or national levels using economic, health, education and other objective, comparative indices. Recognizing that such measures as GNP are oftentimes inadequate, assorted indices have been derived to gauge the changes in social development over time, e.g., the well-known Physical Quality of Life Index (PQLI) constructed by the Overseas Development Council (Morris, 1976). Many of these tend to focus on objective, material indicators (commodity possessions) as opposed to the more subjective ones (Anand and Ravallion, 1993). It is not the purpose of this paper to examine the various critics' arguments about the strengths and weaknesses of extant macro versus micro indices, but rather to lend support for the general need to assess development and social change through social indicators, whether macro or micro, objective or subjective. Ultimately, the purpose of the assessment should guide which social indicators are selected. The purpose of this paper is to examine several issues arising from the linkages between development efforts and quality of life (QOL). Using empirical data which were gathered to evaluate a community development project in the Garhwal region of Northern India, several issues, germane to both social indicators and development, will be investigated. These include: (1) the relationship between “Basic Minimum Needs” (BMNs) and QOL, (2) some methodological innovations for measuring both BMNs and QOL, and (3) selected correlates of BMNs and social indicators of QOL for Garhwali villagers. Before describing the project and its findings, we will first place it in the overall development context.     

Determinants of Health and the Quality of Life in the Bella Coola Valley

The aim of this investigation is to obtain some baseline self-reported data on the health status and overall quality of life of all residents of the Bella Coola Valley of British Columbia aged 17 years or older, and to measure the impact of a set of designated health determinants on their health and quality of life. In the period from August to November 2001, a variety of procedures were used to ensure that all eligible residents of the Valley received a copy of our questionnaire, and 687 useable questionnaires were obtained for our working dataset. Health status was measured by SF-36 and the U.S. Centers for Disease Control healthy days items. Thirty-one items were used to measure the Provincial Health Officer’s designated determinants of health in four clusters, namely, biological, social and economic, health behaviours and health services determinants. Quality of life was measured by satisfaction levels in 13 specific domains of life (e.g., family, financial security), four global items (e.g., happiness, life satisfaction) and one global Subjective Well-Being Index. Besides obtaining baseline figures on all our measures for the Valley, we made some comparisons among our figures and those from other areas, e.g., Prince George, BC. Most of the measures indicated that the health status and quality of life of Bella Coola Valley residents were lower than those of Prince George residents. For the sample as a whole, SF-36 scores on the eight dimensions ran from 82.3 (physical functioning) to 50.0 (social functioning), with a mean of 62.7. Residents in the Valley averaged 6.5 days in the past 30 in which their health was physically not good, 5.5 days when it was mentally not good and 4.1 days when their health limited their usual daily activities. Eleven percent of respondents described their general health as “excellent” and another 27% said it was “very good”. On a 7-point scale from 1=very dissatisfied to 7=very satisfied, respondents had average life satisfaction and satisfaction with the overall quality of life scores of 5.5. For specific domains of life, the lowest mean level of satisfaction was reported for federal and provincial government officials (3.3) and the highest was reported for living partners and personal safety around home (5.8). Regarding bivariate relations, each of the eight dimensions of SF-36 was significantly correlated with a single item measure of general health, and five of the eight were significantly correlated with the number of good health days. Happiness and the Subjective Well-Being Index were positively but moderately correlated with six of the eight dimensions, and life satisfaction was positively correlated with five. Age was negatively related to general health, but positively related to life satisfaction. Not being of aboriginal descent was positively related to all of the four global health indicators and to the Subjective Well-Being Index. Education was positively related to the four global health measures but not to the three global quality of life measures. The Social Support and Good Family Indexes were positively related to all seven global measures. There was a positive correlation between six of the seven global measures and the frequency with which respondents participated in activities sponsored by voluntary organizations. Frequency of smoking was negatively associated with every global dependent variable except the Physical Health Index. Frequency of skipping meals was negatively associated and average hours of sleep per night was positively associated with all seven global measures. Turning to multivariate relationships, the four clusters of health determinants explained from 12% (SF-36 Mental Health Index) to 24% (general health) of the variance in the dependent global health variables, and from 20% (happiness) to 26% (Subjective Well-Being Index) of the variance in the dependent global quality of life variables. Adding domain satisfaction scores to the total set of predictors allowed us to explain from 20% (SF-36 Mental health Index) to 29% (general health) of the variance in the dependent global health variables, and from 39% (happiness) to 62% (life satisfaction) in the dependent global quality of life variables. By including measures of social support and good family relationships in our set of health determinants, we practically guaranteed that the latter would be relatively strongly predictive of global quality of life.     

The stability and validity of quality of life measures

Effective social indicators must be stable when individual or societal characteristics are unchanged and dynamic when circumstances alter. Highly reliable measures may be poor indicators because they are insensitive to change. Little evidence is available on the sensitivity or validity of objective and subjective indicators. A lack of panel data has restricted the assessment of the stability of subjective measures. This paper examines longitudinal data on a representative sample of 2162 Canadians interviewed in 1977 and again in 1979. Test-retest correlations of approximately 0.50 were obtained for satisfaction and self-anchoring ladder measures among respondents who reported no significant changes in their lives during the past two years. Correlations were substantially lower, as expected, for those reporting life changes. Comparisons of the absolute values of these subjective indicators show that very little change in quality of life measures occurs when stable circumstances are reported but the indicators rise or fall significantly when situations change with downward adjustments being more dramatic than upward modifications. Positive and negative life events had little effect on overall evaluations of life quality. In general, these findings provide very strong evidence for the stability and validity of subjective indicators over time. These measures, with one exception, were constant in unchanging situations and sensitive to change when it occurred.     

An Integrational Model of Quality of Life in Older Age. Results from the ESRC/MRC HSRC Quality of Life Survey in Britain

This paper is based on the results of anational survey of the quality of life of 999randomly sampled people aged 65 and over,living at home in Britain. The survey wassemi-structured, and a sample of surveyrespondents was followed up and interviewedin-depth in order to explore their perceptionsof quality of life in full. Comparisons aremade here between the results of (i) ahierarchical multiple regression model basedon theoretically derived indicators of survey respondents' ratings of their overall quality of life, with (ii) the samerespondents' own definitions of quality oflife, categorised from their responses toopen ended survey questions, and (iii) theviews of a sub-sample of these survey respondents who were subsequently interviewed in greater depth. Respondents were asked theopen ended questions on quality of life at theoutset of the survey interview in order toprevent any respondent bias from thestructured measures used.The core components, and the central planks,of quality of life, which were consistentlyemphasised by the three approaches, were psychological characteristics and outlook,health and functional status, personal andneighbourhood social capital. The lay modelsalso emphasised the importance of financialcircumstances and independence, which need tobe incorporated into a definition of broader quality of life.     

Measuring Wellbeing in Developing Countries: The Case of Algeria

The International Wellbeing Index (IWI) has been developed as a complementary measure to already well-known economic measures, and as a tool for cross-cultural comparisons. It comprises two subscales: the Personal Wellbeing Index (PWI) and the National Wellbeing Index (NWI). The aims of this paper are two-fold. Firstly, to test the psychometric characteristics of the IWI. Secondly, to study how people of Algeria, a third world country that is yet suffering from a harsch economical and social situation, respond to questions dealing with their own lives and life in their country in general, compared to samples from more developed countries where wellbeing was previously measured. The IWI items were presented to individuals either in a questionnaire form to be self-rated or in interview sessions. The total of 1417 answers were analysed. As was expected, a very low satisfaction on both scales was found, compared to the results that were reported in countries, such as Australia or Hong Kong. Comparisons on the demographic characteristics basis show that women are significantly more satisfied than men with their personal lives, though no differences were found with regard to NWI. Eldest and youngest age groups rated the PWI significantly higher than other age groups. Education groups comparisons showed higher ratings on both subscales in favour of groups with no education and those with university levels. Marginal statistically significant differences were found with regard to the PWI in favour of the higher earning group, but no differences in the NWI. No statistically significant results were found as far as marital status, number of children, and income are concerned. The results add to the evidence of the usefulness of the scale to predict satisfaction of people with their own lives and life in their country. Its psychometric performance was proved to be very high in terms of validity, reliability and sensitivity. The results were interpreted on the light of the Homeostasis Theory and the particular situation of the Algerian society.     

Measuring Well-Being Over Time: The Adjusted Mazziotta–Pareto Index Versus Other Non-compensatory Indices

Most of the socio-economic phenomena such as development, well-being, and quality of life have a multidimensional nature and require the definition of a set of individual indicators to be properly assessed. Often, individual indicators are summarized and a composite index is created. One of the main problems in constructing composite indices is the choice of a method which allows time comparisons. In this paper, we consider the Adjusted Mazziotta–Pareto Index, a non-compensatory composite index used by the Italian National Institute of Statistics for measuring “Equitable and Sustainable Well-being” in Italy. An empirical comparison with some traditional non-compensatory indices is presented and an Influence Analysis is, for the first time, performed in order to assess the robustness of the index.     

Non-Farm Rural Ontario Residents’ Perceived Quality of Life

This paper presents a study of Ontario non-farm ruralresidents perceived quality of life. The researchsought to understand residents perceived quality oflife both in absolute and relative terms. Measurementfocused on indicators of satisfaction with personallife, community and environment and quality of life incomparison to others. A pre-tested questionnaire wasmailed to a random sample of non-farm rural residentsin Brock and Uxbridge Townships, which lie within onehours drive northeast of metropolitan Toronto. Nearly forty-two (41.8) percent of the residentsreturned completed questionnaires. The majority ofrespondents indicated they were satisfied with mostaspects of the quality of their lives. The demographiccharacteristics that were consistently found to havesignificant associations with indicators of absolutequality of life and relative quality of life wereincome, length of residency, presence or number ofchildren in the home and township. Other demographiccharacteristics almost as consistently found to besignificantly associated with indicators of absoluteand relative quality of life were gender, followed byage, marital status, amount of land surrounding thehome and education. Comparing objectively-baseddemographic characteristics and more subjectivelyderived indicators of absolute and relative quality oflife provided a useful way of understanding quality oflife perceptions of Ontario rural non-farmresidents.     

Good Health is Not the Same as a Good Life: Survey Results from Brandon,Manitoba

The aim of this investigation was to obtain some baseline self-reported data on the health status and overall quality of life of a sample of residents of the city of Brandon, Manitoba aged 18 years or older, and to measure the impact of a set of designated health determinants, comparison standards and satisfaction with diverse domains of life on their health and quality of life. In May and June 2010, 2,500 households from the city of Brandon, Manitoba were randomly selected to receive a mailed out questionnaire and 518 useable, completed questionnaires were returned. Baseline health status data were obtained using the 8 SF-36 dimensions of health and 13 items from the United States Centers for Disease Control and Prevention, Behavioral Risk Factor Surveillance System. Determinants of health and overall quality of life included measures of socializing activities, a Good Neighbourhood Index, Social Support Index, Community Health Index, a measure of free-time exercise levels, health-related behaviours, use of drugs, health care issues, a set of domain-specific quality of life items, a set of measures concerning criminal victimization, worries and behaviours concerning victimization and the basic postulates of Multiple Discrepancies Theory. Overall life assessment, dependent variables included Average Health, happiness, a single item measure of satisfaction with life as a whole, a single item measure of satisfaction with the overall quality of life, the Satisfaction With Life Scale, Contentment with Life Assessment Scale and a Subjective Wellbeing Index. Using multiple regression, we explained as much as 75% of the variance in Subjective Wellbeing scores and as little as 45% in happiness scores. Four clusters of health determinants explained from 20% (Happiness) to 44% (Average Health) of the variance in the dependent variables. Adding comparison standards and domain satisfaction scores to the set of health determinants increased our total explanatory power by only 2% points for Average Health (from 44 to 46%), but more than doubled our explanatory power for Happiness (from 20 to 45%) and for satisfaction with the overall quality of life (from 31 to 67%). As well, our explanatory power for the single item of Life Satisfaction increased from 34 to 66%, for the Satisfaction With Life Scale from 39 to 74%, for the Contentment With Life Assessment Scale from 36 to 60%, and for Subjective Wellbeing from 42 to 75%. This provided very clear evidence that self-perceived good health is not equivalent to perceived quality of life, confirming evidence reported in our earlier studies. The three most important take-home messages from this investigation are (1) in assessing the relative influence of any alleged determinants of health and the quality of life, different sets of alleged determinants will appear to be more or less influential for different dependent variables. Therefore, (2) researchers should use diverse sets of determinants and dependent variables and (3) it is a big mistake to use measures of health status as if they were measures of the perceived quality of life.     

Measuring and understanding the well-being of South Africans: Everyday quality of life in South Africa

South Africa has a Gini co-efficient of 62, one of the world’s highest (Finmark: Project FinScope 2004 and 2005, FinMark Trust, Johannesburg). Hence, measures of wealth are ubiquitous social indicators in South Africa. However, a growing emphasis in government towards measurable service delivery targets and remedial action to redress the inequalities of our past makes the reliable measurement of people’s quality of life in greater depth in quantitative terms an imperative.We have developed a simple framework to measure people’s quality of life in key domains that extend beyond that simply of wealth, using composite indices to allow progress to be tracked and to make valid comparisons across our diverse population. Termed the Everyday Quality of Life Index (EQLi), it comprises a suite of measures encompassing socio-economic status (with special reference to poverty), urbanisation, health (nutrition, exercise and fitness), stress/pressure, quality of the environment, satisfaction of human needs, connectivity, optimism, subjective well-being (happiness, after Diener and Lucas: 2000, in M. Lewis, J.M. Haviland (eds.), Handbook of Emotions. (2nd ed) (Guilford, New York)), and the overall measure of well-being, the EQLi itself.The initial framework was developed from a structured questionnaire administered to a probability sample of 2000 South African adults in 2002. From this, a 52-item shortlist was derived to create the series of measures. This has been tested and refined in three subsequent annual studies, each of 3500 people across urban and rural South Africa. In 2004, items involving work as well as determining the balance of skills and challenges at work using the concept of “flow” (Csikszentmihalyi: 1990, Flow: The Psychology of Optimal Experience (Harper and Row, New York)) were added.This paper outlines the rationale behind the selection and development of these measures, describes the EQL of South Africans using these and other key measures and concludes with implications for policy-makers and service providers in South Africa. Some marketing implications are also given: there is a growing emphasis worldwide on corporate social investment initiatives and, particularly in South Africa, on community upliftment and development – poverty alleviation and improving the lives of the disadvantaged (“people” rather than “consumers”). Further, people’s well-being affects how they react to marketing activities.     

Multidimensional Wellbeing: An Index of Quality of Life in a Developing Economy

Human wellbeing indices can shed light on a society’s quality of life. This study ranks human wellbeing by employing objective and subjective indicators of quality of life for hundred districts of Pakistan. Households level data used for the analysis includes ‘The Pakistan Social and Living Standards Measurement Survey’ for the year 2006–2007. The human wellbeing is examined in four domains: education, health, living conditions and economic situation. Principal component analysis is employed for indexing human wellbeing, rated in five quintiles are generated. The paper demonstrates the importance of education, health and living condition domains in determining the human wellbeing. Objective indicators of education i.e., adult literacy rate, net primary enrolment and gender equality in education are important variables in ranking of districts. Economic status of the households and communities are important variables in subjective perception of wellbeing. The results indicate substantial variation in human wellbeing among districts of Pakistan It may be considered that disparity in objective condition and in subjective perceptions are adequately depicting wellbeing differences. Finally, it is argued that objective indicators and subjective wellbeing measures are needed in unison to understand human quality of life and to make informed policy decisions.     

Objective and subjective indicators of community evaluation: A Pennsylvania assessment

The relationships between subjective and objective measures of well-being were assessedusing data from a survey of Pennsylvania residents and county-level statistical measures complied from secondary sources. Following Ross, Bluestone, and Hines (1979), objective social indicators were derived to measure socioeconomic status, family status, health status, and alienation for Pennsylvania countries. These indices were only modestly intercorrelated, suggesting that they measured somewhat different ideas. Subjective well-being was assessed by asking more than 3000 individual respondents to a mail survey to rate the quality of their communities. Responses were dichotomized and logistic regression used to assess the effects of the objective indicators of county well-being to individual community evaluations. The relationships were small, and inconsistent. Implications of the findings are discussed.     

Health and the Quality of Life

The aim of this investigation was to explain theimpact of peoples self-reported health on theirlevels of satisfaction with their health, and theimpact of these things plus satisfaction with otherspecific domains of their lives on the perceivedquality of their lives. The latter was operationalized as general happiness, satisfactionwith life as a whole and overall satisfaction with thequality of life. Seven hundred and twenty-three (723)usable questionnaires returned from a mailout randomsample of 2500 households of Prince George, BritishColumbia in November 1998 formed the working data-setfor our analyses. Among other things, mean respondentscores on the SF-36 health profile were found to belower than published norms from the UK, USA,Netherlands and Sweden, but higher than scores fromAberdeen, Scotland. Mean scores on the CES-Ddepression scale also indicated that our respondentstended to have more depressive symptoms thancomparison groups in Winnipeg and the USA. A review oftrends in mean scores on 17 quality of life items(e.g., satisfaction with family life, financialsecurity, recreation, etc.) from 1994, 1997 and 1998revealed that there were only 7 statisticallysignificant changes across the four year period andthey were all negative. Multivariate regressionanalysis showed that health status measured with avariety of indicators could explain 56% of thevariation in respondents reported satisfaction withtheir health. A combination of health status plusdomain satisfaction indicators could explain 53% ofthe variation in respondents reported happiness, 68%of reported life satisfaction and 63% of reportedsatisfaction with the overall quality of life. Sixtypercent of the explained variation in happiness scoreswas attributable to self-reported health scores, whileonly 18% of the explained variation in satisfactionwith life and with the overall quality of life scoreswas attributable self-reported health scores.     

The State and Fate of Nations: Implications of Subjective Measures of Personal and Social Quality of Life

The equation of `more with `better – ofstandard of living with quality of life – is at theheart of a growing international debate aboutindicators of progress. At one level, the debate isabout the adequacy of GDP (Gross Domestic Product) asthe dominant indicator of national performance. However, the debate also reaches far beyond thisquestion to challenge conventional thinking aboutprogress. Quality of life includes both objective andsubjective elements, so indicators of progress shouldinclude measures of how people feel about their lives.Drawing mainly on Australian data – but also on USand international studies – this analysis examines anddifferentiates between subjective measures of personal and social quality of life, anddiscusses their use in evaluating whether life isgetting better – or worse.     

The quality of life in large American cities: Objective and subjective social indicators

The concept of ‘quality of life’ as a tool of comparative social indicators research is analyzed. Inter-city comparisons of objective and subjective measures of well being are presented and the distinctiveness of these two dimensions of the quality of life is documented. The paper concludes with some observations on the implications that this distinctiveness has for the use of the concept ‘quality of life’ in future social indicators research.     

Indicators of gender equality for American states and regions

The indicators described in this paper are measures of status equality (as compared to measures of status attainment). Status equality was operationalized by expressing the status attainment scores for women as a percentage of the scores for men. Indicators of equality in the economic, political, and legal spheres of life were computed for each of the 50 U.S. states. The indicators were combined to create an overall Gender Equality Index and a subscale for each of the three spheres. Analyses of internal consistency reliability and construct validity are presented. Large state-to-state and regional differences were found for all three spheres. In respect to the overall Gender Equality Index the scores ranged from a low of 19 (i.e. 19% of what is needed for equality with men) to a high of only 60%.Paper presented at the 1986 meeting of the American Sociological Association. The research reported in this paper is part of the State and Regional Indicators Archive (SRIA). A bibliography listing papers and books of the SRIA is available from the Family Research Laboratory, University of New Hampshire, 128 Horton Social Science Center, Durham, NH 03824.     

Exploring the Quality of Life of People in North Eastern and Southern Thailand

The assumption that development brings not only material prosperity but also a better overall quality of life lies at the heart of the development project. Against this, critics assert that development can undermine social cohesion and threaten cultural integrity. Rarely, however, is the impact of development on well-being rigourously analysed using empirical data. This is what the Wellbeing in Developing Countries Group at the University of Bath aims to do drawing on fieldwork carried out in four developing countries, which addresses the themes of resources, needs, agency and structure, and subjective Quality of life (QoL). The first phase of the QoL research in Thailand aimed to explore the categories and components of quality of life for people from different backgrounds and locations with the aim of developing methods for QoL assessment in the third phase of the WeD QoL research. The study presents data obtained from rural and peri-urban sites in Southern and Northeastern Thailand (two villages in Songkhla and three in Khon Kaen, Mukdaharn, and Roi-et). Participants were divided into six groups by gender and age, and were divided again by religion (Buddhist and Muslim) and wealth status in the South. Data collection was conducted between October and December 2004 using focus group discussions, semi-structured interviews, and the Person Generated Index. Content analysis was used for data analysis. The use of a qualitative approach enabled the gathering of empirical data that reflects the sources of difficulty and happiness in the lives of participants. Respondents identified 26 aspects to their quality of life, including family relations, health and longevity, income and having money, jobs, housing, education, debt, and so on. The results reveal clear similarities and differences in the role of traditions, religious beliefs, and values in the lives of people living in remote rural or peri-urban areas in Northeastern and Southern Thailand. These results, together with the findings from Peru, Ethiopia, and Bangladesh, will inform the rest of the WeD research and be used to develop measures to assess the quality of life of people living in developing countries.     

Quality of life after radiation therapy: A study of 309 cancer survivors

(1) Comparison of patients with a national sample reveals no difference in income (adjusted for inflation), marital status and education level but patients, who are typically older, are more often retired or disabled. (2) Overall, perceived quality of life is nearly the same for patients and national sample; apparent differences can be readily explained by factors other than cancer or its treatment. (3) While some minor differences among patients were found as a function of their site of cancer, treatment modality, or health status, no evidence was found of significant detrimental effects due to radiation therapy or perceived quality of life. (4) Contrary to expectations of deteriorated quality of life, compared to the national baselines the patients are actually more satisfied with their lives as a whole.     

Possibilities and Limitations for the Measurement of the Quality of Life in Urban Areas

Recently, quality of life has become a commonly used concept and is showing growing significance in economic and political terms. Additionally, quality of life issues have been recognized within the migration processes. In one view, quality of life relates to the degree to which the necessary conditions for satisfaction exists in a given society or region.It is argued that quality, as a measurable variable would refer to both the subject and the object of inquiry. In this sense, quality of life has two aspects, psychological and environmental ones. Nevertheless, this limitation constitutes a major problem since some researchers have totally neglected the perception of the people who live there, while another limitation is about the components, indices and indicators chosen in a certain analysis.In this paper, the aim is to test some measurement methods of the quality of life and to show the possibilities and limitations of a developing area in comparison to developed ones. In this way, some comparative analysis can be performed on the measurement methods of the quality of life levels with respect to regional variations.