nach oben

Social Indicators Research

Erschienen in:

18.03.2016

Construction and Analysis of a Composite Quality of Life Index for a Region of South Africa

verfasst von: Talita Greyling, Fiona Tregenna

Erschienen in: Social Indicators Research | Ausgabe 3/2017

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This study quantifies and analyses quality of life in the Gauteng City-Region of South Africa. First we adapt and extend a method used in research on market regulations to construct composite indices to the field of quality of life. In the adapted method we employ categorical principal components analysis suitable for the analysis of categorical data typically used in quality of life research. The newly constructed index is a comprehensive quality of life index, which includes objective and subjective as well as economic and non-economic indicators. This is the first composite index of its kind in South Africa. Second, this index is used to compare the quality of life of different demographic and socio-economic groups in the region. The quality of life scores of Africans, low income, female, older and urban informal dwellers are relatively low. Third, the explained variance of the dimensions of quality of life is compared across groups. The dimensions ‘housing and infrastructure’ and ‘social relationships’ explained the most variance for groups with lower and higher quality of life scores respectively. The results shed light on quality of life in this region, including the great unevenness of well-being. The study provides a basis for the measurement and analysis of quality of life in other regions and countries.

Vorheriger Artikel The Globalization and Peace Nexus: Findings Using Two Composite Indices

Nächster Artikel Sensitivity Analysis: A Necessary Ingredient for Measuring the Quality of a Teaching Activity Index

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

The method has also been applied by among others Groh et al. (2010), Rukšėnaitė (2011) and Szent-Iványi and Vigvári (2012), but not for analysing quality of life.

The narrow unemployment rate of the GCR (OECD 2011).

See OECD (2008) for a discussion of different weighting methods.

See Tabachnick and Fidell (2007) for a comprehensive explanation of PCA.

Using CATPCA, categorical indicators are transformed by assigning optimal scale values to the categories, thus transforming categorical indicators to numeric-valued indicators referred to as category quantifications. CATPCA, given the number of extracted components, finds optimal category quantifications, implying that the overall variance accounted for in the transformed indicators are maximised (Linting et al 2007). The category quantifications for an indicator together form that indicator’s transformation. In the optimal scaling process, the initial information provided by the categorical data is retained in the category quantifications. However the degree to which the original data is retained depends on the optimal scaling level (nominal, ordinal or numeric) selected for each indicator (Linting et al. 2007). The transformed indicators have metric properties which allow the researcher to use all standard methods normally applied to continuous indicators.

In mathematical terms the aggregation of the intermediate composite indices to derive at the composite index of quality of life (CIQoL) is as follows: ${\text{CIQoL}}_{\text{i}} = \left( {\sum\nolimits_{i = 1}^{n} {(ICI_{i} w_{i} )} } \right) \times 100$ where CIQoL is the composite index of quality of life, ICI _i = Intermediate Composite Index (i = 1 … n), and w _i is the weight of the ith ICI determined by the explained variance of the intermediate composite index (extracted component) in the dataset.

We derived the percentage by dividing the life satisfaction score of each respondent by the maximum life satisfaction score (5) and multiplying by 100.

To calculate eta squared when using ANOVA we used the formula

$Eta\;squared = \frac{sum\;of\;squares\;between\;groups}{Total\;sum\;of\;squares}$, and when using t tests we used the formula

$Eta\;squared = \frac{{t^{2} }}{{t^{2 + (N1 + N2 - 2)} }}$ (for more information on the calculation of effect sizes see Pallant 2007).

The Kish grid is commonly used in selecting cases at random when more than one case is eligible for inclusion when the interviewer calls at a sampled address or household.

No information is available in the dataset as to whether respondents were in the originally selected households or in substituted households.

Due to a field sampling error, some municipalities were over-sampled and a few were under-sampled. Therefore the data was weighted in accordance to the Census 2001 (Stats SA 2002) population data.

The income figure includes salaries, grants, pensions and any other source of income. Household income was used in the analysis as neither individual income nor a continuous income indicator were available in the survey. An individual income measure was calculated, converting bracket midpoints to a per capita measure. This was found to be highly correlated with the household income measure. The estimated per capita measure was also used in the analysis as a robustness check, and the results were highly consistent with those from the household measure, which was preferred. R1 is approximately US$0.9 (March 2014).

Amount of variance in the indicator explained by the component.

Seven was the total number of dimensions of quality of life used to guide the selection of the indicators (see the list of dimensions in Sect. 4).

The Kaiser rule, known as the Kaiser’s criterion or the eigenvalue rule is related to the eigenvalue of each principal component. The eigenvalue (variance) for each principal component indicates the percentage of variation explained in the data. Using this rule, components with an eigenvalue of 1.0 or more are extracted.

32 % was derived by dividing 19.43 % of the variance explained by the first extracted component by 60 %, which is the sum of the variances of each of the five extracted components (19.43 + 13.13 + 10.01 + 9.81 + 7.48) (see Table 3).

The number of Asians included in the sample were too small for robust statistical analysis, therefore Asians were excluded for the analyses in Sects. 6, 7.

For reasons of space, Table 5 only shows racial groups. For the other group categories, similar tables are not shown, but relevant results are noted in the text; the additional tables are available from the authors on request.

Employed is defined as a person who has worked in the last seven days before the interview.

See Table 1.

The tolerance value of all the variables were >0.1 and the Variance Inflation Factor (VIF) of all the variables were <2. On inspection of the correlation matrix no signs of multicollinearity were found.

Results from the SAARF development index (2011) also noted the importance of the ‘socio-economic status’ dimension in improving South Africans’ well-being and highlighted the need for higher levels of employment.

African National Congress. (1996). Reconstruction and development programme. http://www.anc.org.za. Accessed 20 March 2012.

Berenger, V. (2007). Multidimensional measures of well-being: Standard of living and quality of life across countries. World Development, 35, 1259–1276.CrossRef

Bookwalter, J. T., Fuller, B. S., & Dalenberg, D. R. (2006). Do household heads speak for the household? A research note. Social Indicators Research, 79(3), 405–419.CrossRef

Booysen, F. (2002). An overview and evaluation of composite indices of development. Social Indicators Research, 59(2), 115–151.CrossRef

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New York: Lawrence Erlbaum Associates, Publishers.

Cummins, R. A. (1996). The domains of life satisfaction: An attempt to order chaos. Social Indicators Research, 38(3), 303–328.CrossRef

Cummins, R. A., Mc Cabe, M. P., Romeo, M. P., & Gullone, E. (1994). The comprehensive quality of life scale instrument development and psychometric evaluation on tertiary staff and students. Educational and Psychological Measurement, 54, 372–388.CrossRef

Dasgupta, P., & Weale, M. (1992). On measuring the quality of life. World Development, 20, 119–131.CrossRef

Development Research Africa. (2009). South Africa—Gauteng City-Region Observatory Quality of Life Survey 2009. https://www.datafirst.uct.ac.za. Accessed 12 Feb 2012.

Diener, E. (1995). A valued based index for measuring national quality of life. Social Indicators Research, 36(2), 107–127.CrossRef

Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well-being: Three decades of progress. Psychological Bulletin, 125(2), 276–302.CrossRef

Dowrick, S., Dunlop, Y., & Quiggin, J. (2003). Social indicators and comparisons of living standards. Journal of Development Economics, 70, 501–529.CrossRef

Easterlin, R. A. (1974). Does economic growth improve the human lot? In P. A. David & M. W. Reder (Eds.), Nations and households in economic growth: Essays in honor of Moses Abramovitz. New York: Academic Press Inc.

Economist Intelligence Unit. (2005). Economist intelligence unit. http://www.economist.com/topics/economist-intelligence-unit. Accessed 16 Feb 2013.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299.CrossRef

Gauteng City-Region Observatory (GCRO). (2009). GCRO Quality of Life Survey (dataset). Johannesburg: Global Print.

Gauteng City-Region Observatory (GCRO). (2011). The city-region review. Johannesburg: Global Print.

Gauteng Planning Commission. (2012). Gauteng 2055: A discussion document on the long-term development plan for the Gauteng City-Region. Johannesburg: Gauteng Provincial Government Republic of South Africa.

Gauteng Provincial Government. (2012). Socio-economic review and outlook. http://www.gautengonline.gov.za. Accessed 3 Aug 2012.

Gifi, A. (1990). Nonlinear multivariate analysis. Chichester, England: Wiley.

Greyling, C. T. (2013). Measurement and analysis of quality of life of the diverse population of the Gauteng City-Region (PhD Thesis). Johannesburg. University of Johannesburg.

Groh, A. P., von Liechtenstein, H., & Lieser, K. (2010). The European venture capital and private equity country attractiveness indices. Journal of Corporate Finance, 16(2), 20–224.CrossRef

Hagerty, M. R., & Land, K. C. (2007). Constructing summary indices of quality of life, a model for the effect of heterogeneous importance weights. Sociological Methods and Research, 35(40), 455–496.CrossRef

Hansen, T., & Slagsvold, B. (2012). The age and subjective well-being paradox revisited: A multidimensional perspective. Norsk Epidemiologi, 22(2), 187–195.CrossRef

Higgs, T. (2007). Measuring and understanding the well-being of South Africans: Everyday quality of life in South Africa. Social Indicators Research, 81(2), 331–356.CrossRef

Hinks, T., & Gruen, C. (2007). What is the structure of South African happiness equations? Evidence from quality of life surveys. Social Indicators Research, 82(2), 311–336.CrossRef

Human Sciences Research Council. (2013). South African Social Attitudes Survey (SASAS). http://www.hsrc.ac.za/en/departments/sasas. Accessed 10 Oct 2012.

Klasen, S. (2000). Measuring poverty and deprivation in South Africa. Review of Income and Wealth, 46(1), 33–58.CrossRef

Linting, M. (2007). Nonparametric inference nonlinear principal components analysis: Exploration and beyond. Doctoral Thesis. University of Leiden. Leiden. https://openaccess.leidenuniv.nl/handle/1887/12386. Accessed Dec 2014

Linting, M., Meulman, J., Groenen, P. J., & Van der Kooij, A. J. (2007). Nonlinear principal components analysis. Introduction and applications. Psychological methods, 12(3), 336–358.CrossRef

Maslow, A. H. (1943). A theory of human motivation. Psychological Review, 50(4), 370–396.CrossRef

Meulman, J. J., Van der Kooij, A. J., & Heiser, W. J. (2004). Principal components analysis with optimal scaling transformations for ordinal and nominal data. In D. Kaplan (Ed.), The Sage handbook of quantitative methodology for the social sciences (pp. 49–70). London: Sage.

Møller, V. (2012). South African quality of life trends over three decades, 1980–2010. Social Indicators Research,. doi:10.1007/s11205-012-0120-y.

Morris, D. (1979). Measuring the conditions of the world poor, the physical quality of life index. New York: Pergamon Press.

National Planning Commission. (2012). The national planning commission. http://www.npconline.co.za. Accessed 5 April 2013.

Naudé, W., Rossouw, S., & Krugell, W. (2009). The non-monetary quality of city life in South Africa. Habitat International, 33, 310–326.CrossRef

Nicoletti, G., Scarpetta, S., & Boylaud, O. (2000). Summary indicators of product and market regulation with an extension to employment protection legislation. www.oecd.org/eco/eco. Accessed 8 Aug 2013.

OECD. (2008). Handbook on constructing composite indicators: Methodology and user guide. Paris: OECD Publishing.

OECD. (2011). OECD Territorial Reviews: The Gauteng City-Region, South Africa 2011. OECD Publishing,. doi:10.1787/9789264122840-en.

Osberg, L., & Sharpe, A. (2000). International comparisons of trends in economic well-being. New York: Paper presented at the annual meeting of the American Economic Association.

Pallant, J. (2007). SPSS: survival manual (3rd ed.). New York: McGrawHill.

Posel, D. R., & Casale, D. M. (2011). Relative standing and subjective well-being in South Africa: The role of perceptions, expectations and income mobility. Social Indicators Research, 104(2), 195–223.CrossRef

Ram, R. (1982). Composite indices of physical quality of life, basic needs fulfilment, and income. A Principal Component Representation. Journal of Development Economics, 11, 227–247.CrossRef

Rossouw, S., & Naudé, W. (2008). The non-economic quality of life on a sub-national level in South Africa. Social Indicators Research, 86(3), 433–452.CrossRef

Rossouw, S., & Pacheco, G. (2012). Measuring non-economic quality of life on a sub-national level; A Case Study of New Zealand. Journal of Happiness Studies, 13, 439–454.CrossRef

Rukšėnaitė, J. (2011). Composite indicators as an additional measure for the assessment of the Lithuanian economy. Lithuanian Journal of Statistics, 49, 47–51.

Seekings, J. (2001). The uneven development of quantitative social science in South Africa. Social Dynamics, 27(1), 1–36.CrossRef

Sen, A. (1985). Well-Being, agency and freedom: The Dewey lectures. Journal of Philosophy, 82(4), 169–221.

South African Audience Research Foundation. (2011). Development Index. http://www.saarf.co.za. Accessed 15 Feb 2013.

South African Institute for Race Relationships. (2011). The South African Development Index. www.sairr.org.za. Accessed 3 Feb 2013.

Statistics South Africa (Stats SA). 2002. Census 2001 Statistical release—P0301.4/Statistics South Africa. Statistics South Africa. Pretoria.

Stiglitz, J., Sen, A., & Fitoussi, J. P. (2009). Report by the commission on the measurement of economic performance and social progress. www.stiglitz-sen-fitoussi.fr/en/index/html. Accessed 12 Nov 2012.

Stroup, M. D., & Stephen, F. (1992). Economic freedom, democracy, and the quality of Life. World Development, 20, 119–131.CrossRef

Szent-Iványi, B., & Vigvári, G. (2012). Spillovers from Foreign Direct Investment in Central and Eastern Europe: An index for measuring a country’s potential to benefit from technology spillovers. Society and Economy, 34(1), 51–72.CrossRef

Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Pearson Education.

United Nations Development Progamme. (2010). Human Development report. New York: UNDP.

Vyas, S., & Kumaranayake, L. (2006). How to do (or not to do)….Constructing Socio-economic Status indices: How to use principal components analysis. Oxford: Oxford University Press.

Titel: Construction and Analysis of a Composite Quality of Life Index for a Region of South Africa
verfasst von: Talita Greyling
Fiona Tregenna
Publikationsdatum: 18.03.2016
Verlag: Springer Netherlands
Erschienen in: Social Indicators Research / Ausgabe 3/2017
Print ISSN: 0303-8300
Elektronische ISSN: 1573-0921
DOI: https://doi.org/10.1007/s11205-016-1294-5

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2017

Using ClustOfVar to Construct Quality of Life Indicators for Vulnerability Assessment Municipality Trajectories in Southwest France from 1999 to 2009

The Income Inequality Hypothesis Revisited: Assessing the Hypothesis Using Four Methodological Approaches

Erratum to: Country-Level Socioeconomic Indicators Associated with Healthy Life Expectancy: Income, Urbanization, Schooling, and Internet Users: 2000–2012

A Triangulated and Exploratory Study of the Relationships Between Secularization, Religiosity, and Social Wellbeing

Multidimensional Child Deprivation and Poverty Measurement: Case Study of Bosnia and Herzegovina

Sensitivity Analysis: A Necessary Ingredient for Measuring the Quality of a Teaching Activity Index