measuring living standards in south africa - · pdf filejournal of green economy and...

Journal of Green Economy and Development, Volume 1, Issue 2, 2015 ISSN 2413-8673 [Online: http://journalofgreeneconomy.org]

©Ncube and Serumaga-Zake 2015 1

Measuring Living Standards in South Africa

Mluleki Ncube Email: [email protected] Philip Serumaga-Zake Email: [email protected] Graduate School of Business Leadership, University of South Africa

Abstract

The study reported in this paper analyses living standards across a spectrum of socio-economic contexts in South Africa. The key objective of the study was to identify the key dimensions that can be used to assess living standards. A mixed methodological approach was used to capture qualitative data in the first phase and quantitative data in the second phase of the study. Data drawn from 2730 participants was used to construct a tool to measure living standards. The study has contributed to knowledge by showing that there is a striking contrast between the affluent, the moderately well-off and the poor in terms of what they view as important factors that contribute to decent living standards. The affluent emphasised security, free time to pursue personal interests, and alternative energy sources as important factors for living stardards; whereas their poorer counterparts identified clean water, housing, food, transport, and entertainment as important factors.

Introduction Since the advent of living standards in the 1970’s (Chander, Grootaert, and Pyatt, 1980) there have been numerous studies that looked at the definition and measurement of living standards. There seem to be a consensus around the definition of living standards as a measure of wealth, comfort, material goods and services available to meet the needs of households (Nordhaus, 2002; Filmer and Pritchett, 1999, Hurd, and Rohwedder, 2006).

From a sociological perspective, living standards are important as they deal with social and symbolic functions of consuming goods and services (Corrigan, 1997). In South Africa the South African Advertising Research Foundation championed the development of a tool to measure living standandards (LSM). The SAARF LSM tool serves as a multivariate market segmentation index used to group South African adults according to their living standard, using indicators such as



degree of urbanisation, ownership of cars and major appliances, as well as access to basic services such as water and electricity as indicators. The SAARF living standards measure was crafted on the basis of an existing set of variables collected in the All Media and Products Survey (AMPS). Essentially, the current LSM developed by SAARF takes a market segmentation slant. As such, the focus is on how consumers respond to different products and services offered in the South African market. The tool focuses on consumption and expenditure without bringing important contextual issues that differentiate the wealth or the asset base of different socio-economic groups in South Africa. The tool does not address how income is related to living standards in both the theoretical domain and in the measure itself. The exclusion of income conflicts with the view established by scholars that identified income as a driver of living standards (see for example Booker, Singh and Savane, 1980; Coleman 2012; Hurd and Rohwedder, 2006; Martins, 2006). This view is shared by Narayan, Patel, Schaft et al (2000) who found that income and consumption are amongst the key factors that define living standards. In addition to the knowledge caveats discussed above, the SAARF LSM tool does not capture the sustainability and environmental ethics debates that have dominated literature since the advent of the Millennium Development Goals. Moreover, while the SAARF tool may be

useful for segmenting the market, it does not provide a comprehensive methodology and research execution process that allows for replication to improve validity and reliability of the tool. This knowledge gap together with the fact that there is conflicting evidence about what constitute living standards, presented an opportunity to reexamine the living standards from a post apartheid perspective. This study argues that a living standard measure should go beyond measuring consumption patterns of economic agents, and capture additional variables that enhance living standards. The aim of this study therefore, is to introduce a valid and reliable tool to measure living standards across a spectrum of socio-economic contexts in South Africa. The next section critiques the SAARF LSM tool. Subsequent to that the theoretical framework and literature that shaped the design of the study is discussed, followed by results, then discussion and conclusion.

Background: Living Standards Measure - South African Perspective

In South Africa living standards have been analysed using the SAARF LSM segmentation tool. The tool was developed on the basis that there are convergent consumer behavioural characteristics that can be tapped upon to group consumers. The tool was first developed in 1989 through the collaborate efforts of ACNielson Media



International and the South African Advertising Research Foundation (SAARF, 2012). As pointed out by Martins (2006), the SAARF LSM tool does not take into consideration the socioeconomic indicators such as access to healthcare, education and labour power. Figure 1 traces the evolution of the SAARF LSM tool from 1989/90 to the new SAARF Universal LSM (SU- LSM) of 2001 which was further modified in 2004. As pointed out by SAARF (2012) the first LSM tool developed in 1989/1990 had 13 key variables out of the 71 that were found to have a combined power in differentiating between respondents in the AMPS survey (SAARF, 2012). The variables are listed in Figure 1. In 1993 the items: ‘no VCR set’ and no ‘tumble dryer’ were discarded and new variables (‘microwave oven’ and ‘metropolitan dweller’) were included in the index. To improve the efficacy of the LSM, the number and range of variables was expanded. The revision was based on the subjective views of the users who indicated that the tool had “great reliance on the ownership of certain durables, and too little attention paid to other variables that looked, subjectively, as though they ought to be significant reflections or manifestations of a person’s ‘Living Standard’ ” (SAARF, 2012).

In the 1995 SAARF LSM tool two variables were discarded (sewing machine and metropolitan dweller) and the following new variables were added:

o Flushed toilet o No financial services used o Dishwashing liquid o Household supermarket shopper o Hot running water o No credit facility o Neither water nor electricity o No insurance policy o Telephone in home o Hut dweller

It is to be noted that contrary to the view that income has no bearing on living standards, the 1995 revision included for the first time three income related variables to the index (‘no financial services used’, ‘no credit facility’ and ‘no insurance policy). In 2000 the tool was further refined by including five additional indicators (‘built in kitchen sink’, ‘stove/hotplate/electric’, ‘video cassette recorder’, car/sedan/beach buggy/hatchback/two-seater coupe’, and discarding four (‘rural dweller’, ‘dishwashing liquid’, ‘household supermarket shopper’ and ‘electricity and water’). In 2004/05 ‘metropolitan dweller’ which was included in the tool for the first time in 1993 and discarded in 1995 was included again in the SU LSM tool in 2004. DVD player and ‘one cell phone in household’ as well as ‘house/cluster house/town house’ were included to yield an instrument with 29 variables.



Figure 1: Evolution of the SAARF LSM tool from 1989/90 to the new SU LSM: Variables included in each tool

The sewing machine variable which was removed found its way back in the 2004/05 instrument. As shown in Figure 1, the SAARF Living Standard Measure of 2004 is based on durable goods and to a limited extent services items. It is also worthy of noting that the finance related variables that were in the 2001 LSM tool were not included in the 2005 measure. In contrast to the living standard measures used in South Africa a number of studies across Africa and

around the world incorporate food items, durable goods, income, clothing, recreation, sport related expenditure, as well as health and education related consumption, among others (Kakwani, 1990; Martins, 2006; Montgomery, Gragnolati, Burke and Paredes, 1999; Nordhaus 2002). These divergent ways of looking at the living standards warrant further investigation into the structure and pattern of living standards in South Africa.


©Ncube and Serumaga-Zake 2015

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Theoretical Framework A wide variety of scholars and activists have identified modern mass consumer society as a fundamental driver of both global economic growth and environmental damage (Wilk, 2002). Wilk (2002) explains that no single theory can be applied to understand all forms of consumption. Wilk (2002) puts forth a conceptual framework which combines three complementary consumption paradigms – social, cultural, and individual choice which can

be applied to lead to multi-stranded policy solutions.

This study adopts Narayan, Chambers, Shah and Petesch (2000) framework as a way of looking at living standards with focus placed on material wellbeing, social wellbeing and bodily wellbeing. Living standards indicators are summarised by Narayan, Patel, Schafft, Rademacher et al (2000) in four major categories (see Figure 3.4): (1) human capital; (2) social capital; (3) physical capital, and (4) environmental capital.

Figure 2: The conceptual framework of the study

This conceptual framework is largely based on work done by the authors that documented the ‘voices of the poor’ from different parts of the world. The study was undertaken by the World Bank to inform poverty reduction strategies of the 2000/01 World Bank Report. The study was designed to

capture experiences, reflections, aspirations and priorities of poor people first hand. The study focused on vulnerable groups, women and men, the youth, and the elderly in Africa (Egypt, Ethiopia, Ghana, Malawi, Nigeria,



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Kyrgyz Republic, Russia and Uzbekistan); Latin America and the Caribbean (Argentina, Brazil, Bolivia, Ecuador and Jamaica); as well as South East Asia (Bangladesh, India, Indonesia, Sri Lanka, Thailand and Vietnam). The country selection was guided by the need to select different poverty contexts in different continents. Participatory research methods were used to collect data. Poverty was found to be associated with ill-being, quality of life, struggle for livelihoods, access to services, hunger, lack of access to education and health facilities, changing gender roles and responsibilities, lack of money and power, poor governance at institutional level and inequality. All these issues affected quality of life.

Living standard Measure: A developmental perspective The UN has been concerned with living standards for some time. In 1952 the UN’s Economic and Social Council adopted a resolution (434B IXIV) which called together industry experts to prepare a report on the most appropriate methods of quantifying and defining the standards of living and variances within developing countries (Altimir and Sourrouille, 1980). This was to be completed under the guidance of the then Secretary General in conjunction with the ILO and other various agencies (Altimir and Sourrouille, 1980).From the 1950’s to the 1980’s various reports were drafted that aimed to enhance the reliability and validity of a living standard measure. In the 1970s, a group of academics joined

forces with the World Bank to develop a living standard measure for the developing countries (Musgrove, 1982; Ainsworth and Van der Gaag, 1988). As with all developing countries that have a large agricultural economy, due to the seasonality of the work and the dispersion of targeted groups, significant difficulties were observed and experienced in so far as the definition, quantification, measurement and conceptual application of living standards.

Methodology

The study was designed in such a way that a universe of items that could possibly define living standards in South Africa were identified using qualitative methods in the first phase of the study. A total of thirty key informants made up of experts and practitioners working in the development, marketing, and sociological fields were interviewed to identify the unobservable traits of living standards. In the second phase, quantitative research involved carrying out a survey made up of a set of items collected in the first phase.

Sampling issues

The unit of analysis for the study was a “household” in South Africa and the actual respondent was the head of the household. The sample selection was guided by the need to represent different socio-economic groups in urban and non-urban settings.



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The study was conducted in nine provinces and twenty one municipalities using computer assisted personal interviewing (CAPI), because of its efficiency in managing field studies (Kelly et al, 2008). The randomness of the survey design was important in ensuring that the households selected for the study were sufficient enough to provide a guide of the living standards in the country as a whole. The urban population represented individuals that live within the metropolitan cities and large towns. The non-urban population represented individuals that live in small towns, villages, settlements and farms on the outskirts of major and other local municipalities that had been selected. A total of 2800 respondents were selected to form part of the study’s sample. However, after the data validation process, the remaining sample size was n=2730. Eighty percent (80%) of the respondents were young people (between 18 and 34 years of age). Those categorised as black formed the largest proportion (75%) of respondents. More than two thirds of the respondents earn less than R10, 000.00 before tax and only 3.4% of the sample earn R20, 000.00 and above. Data collection An interview protocol with the following questions was used to collect qualitative data:

• In your view what are the most important attributes of a high standard of living?

• Name three key things that would improve your standard of living?

Quantitative data was collected using a 36-item survey instrument. During the data cleaning, coding and validation process binary nonmetric variables were all recoded with the aim of transforming nonmetric data into a metric form. Negatively worded items were also recoded to facilitate interpretation of descriptive statistics. The data validation process resulted in a reduction of items from 36 to 22 as depicted in Table 1.

Data analysis

Qualitative data was analysed using content analysis guidelines. Narratives from the participants were transcribed using Tesch’s coding (Creswell, 2007). In following Tesch’s analysis approach, coding was initiated with predefined themes. After all the patterns were identified, the themes were clustered into topics that reflect their meaning (Miles and Huberman, 1994). Identification of topics involved evaluating, sorting, categorising, comparing and synthesising the coded data. Quantitative data was analysed using exploratory factor analysis (EFA). EFA is particularly useful because of its capacity to demonstrate the dimensionality of the Living Standard tool by identifying the number of latent constructs and the underlying factor structure of a set of living standard indicators.



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Table 1: Quantitative data instrument

Findings

Phase 1 Findings: Qualitative Data

Participants provided a range of factors that they perceived to be contributors to a decent lifestyle. These include car ownership, access to clean water and sanitation, education, monthly income in terms of employment, health, fuel, electricity and home ownership. A noticeable difference in views was observed in the responses that came from participants in the higher socio-economic groups. For example, one respondent placed emphasis on the fact that domestic help allows for extra free time to pursue other interests

Dimension Item Source Physical Capital 1. Food consumption

2. Land ownership 3. Property ownership 4. Vehicle ownership 5. Livestock ownership 6. Ownership of small durable items 7. Ownership of large durable items 8. Use of financial services

Narayan, Patel et al (2000) SAARF LSM Qualitative data (Phase 1)

Human capital 1. Employment 2. Access to education 3. Access to health services 4. Infrastructure

Narayan, Patel et al (2000) Qualitative data (Phase 1)

Social capital 1. Married with children 2. Social networks 3. Community support g 4. Safety and security


Environmental capital 1. Access to clean water 2. Sanitation 3. Waste management facilities 4. Source of fuel 5. Arable land/grass 6. Renewable energy


“…that I can afford somebody to help me out once a week in my house is an important factor that contributes to a decent lifestyle….”



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Figure 3: Most highly cited factors that affect living standards

On the issue of time, some respondents indicated that they need extra time to spend with family and children and the fact that their quality of life would be enhanced if they can have a few extra hours each week.

A list of factors that respondents felt should be in place to enable a decent lifestyle are highlighted in Figure 3. The size of the circle reflects the importance of the factor as perceived by most respondents. Safety, was the most cited item followed by income. There were common themes among different social actors. However, high-income earners and more educated individuals were more concerned about security, safety, health and leisure time to achieve work-

life balance. People living in informal settlements were more concerned about housing, employment, transport issues and ownership of assets. Middle-income earners seem to emphasise employment, access to credit, mobility and entertainment. Social networks and support are more important to middle and low income earners than high-income earners. High-income earners expressed the importance of environmental sustainability in all life’s activities.

Items with high living standard scores are those in the physical asset category (including size of property, value of property); educational qualification, which is in the human capital category.

Alternative energy source to Eskom

Safety

Clean water

Income

Education

Public service

Transport

Infrastructure

Time

Good job/employment



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Phase 2 Findings: Factor analysis

Twenty two items emanating from the first phase of the research were checked for departure from normality, homoscedasticity and linearity before factor analysis was conducted; and none of the factor analysis assumptions were violated. A visual examination of the partial correlation matrix was done. The KMO measure of sampling adequacy (MSA) and Barlett test of sphercity are further tests that were conducted to check the appropriateness of factor analysis (Child, 1990) as a tool to construct the the LSM tool. The MSA measure falls under an acceptable range with a value of .75 (Norusis, 1998). The Barlett test shows that non-

zero correlations exist at the significant level of .0001.

To select the number of factors to be retained for further analysis, Kaiser’s criteria were used to drop all components with eigenvalues under 1.0. Based on the Kaiser’s latent root criteria, six factors were retained. Table 2 shows that the first component accounts for 16.65% of the total variance extracted from the factors. The Scree Plot as illustrated in Figure 4 plots the number of factors or components on the X - axis and the corresponding eigenvalues on the Y-axis (Catell, 1966). The factor solution resulted in six factors with an eigenvalues greater than one. Table 3 shows each indicator with its corresponding factor loadings on each component.

Table 2: Factors Extracted and Total Variance Explained

Component

Initial Eigenvalues Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Eigenvalues % of Variance

Cumulative % Total

% of Variance

Cumulative % Total

1 3.662 16.647 16.647 3.662 16.647 16.647 3.431

2 2.237 10.168 26.815 2.237 10.168 26.815 2.331

3 1.669 7.586 34.401 1.669 7.586 34.401 1.610

4 1.357 6.168 40.569 1.357 6.168 40.569 1.487

5 1.247 5.668 46.237 1.247 5.668 46.237 1.380

6 1.160 5.273 51.510 1.160 5.273 51.510 1.581



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Figure 4: The Scree Plot

The factor loadings are the correlation coefficients between the variables and the factors. Items loading high on more than one factor were eliminated to ensure discriminant validity. The item: ‘I belong to a network group’ loaded strongly with factors 4, 5 and 6. As such, the item became a candidate for deletion. All items with a communality value of less than .3 were candidates for deletion, because the items would not have sufficient common explanation in the factor solution. However, no item in the factor solution had a communality of less than .3.

Table 3 shows that size of property, value of property and ownership of a computer, a geyser and a generator load strongly with factor 1, with corresponding factor loadings of .660, .659, .652, .551 and, .544 respectively.

Education qualification (.671), education expenditure (.675) and domestic help (.581) also loaded strongly with factor 1. Affluence resources is a label that captures the eight items in this dimension very well. As can be seen in Figure 5, “well informed neighbourhood” and influential neighbourhood” loaded strongly with the second factor, followed by trustworthy neighbourhood, then secure neighbourhood. This particular dimension is given the label: neighbourhood safety and connectedness. Piped water and toilet type loaded very strongly with the 4th factor labeled as environmental capital.



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Table 3: Pattern Matrix of Living Standard Measure Items

1 2 3 4 5 6 Communality

Size of property .660 .099 -.128 -.076 -.254 .099 .565

Property value .659 -.069 .121 -.174 -.244 -.197 .617

Do you own a generator? .544 .069 -.033 .160 -.048 .224 .376

Do you own a computer .656 -.002 .266 .002 .244 .002 .545

Do you own a geyser .551 .026 .452 .079 -.065 .005 .572

Health facility .026 .074 .142 .634 -.031 .081 .439

Travel time to health facility -.099 -.090 .051 .718 -.051 -.024 .533

Job seeking in last 2-4 weeks -.054 .102 -.029 .022 .545 .156 .342

Education expenditure in the last 12 months .671 -.098 -.299 .091 -.005 -.061 .523

Education qualification .675 .020 -.041 -.141 .241 .037 .494

Employment status -.012 .104 -.015 .130 -.749 .176 .598

Belong to a network group .085 .032 -.185 .369 .336 -.337 .420

Family support -.067 -.059 .066 -.050 -.016 -.657 .424

Domestic help .589 .075 .036 .002 -.095 -.095 .408

Free time to pursue personal interests .022 .135 .093 .017 .056 -.551 .354

Community support -.017 .101 -.260 .367 -.134 -.442 .462

I live in a safe and secure area .113 .482 -.089 -.146 -.105 -.312 .458

My area has trustworthy neighbours -.032 .624 -.017 -.252 -.087 -.263 .576

Well informed neighbourhood .001 .859 .072 .085 .031 .076 .732

Influential neighbourhood -.010 .828 -.002 .109 .093 .121 .684

Piped water .190 -.017 -.786 -.009 -.025 .020 .631

Toilet type .228 -.027 .617 .241 -.119 -.171 .578



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Figure 5: Newly proposed South African LSM

The alternative living standard tool combines 21 indicators depicted in Figure 5 into a single measure, representing what is held in common across the six factors in the tool. To test for the robustness and reliability of the new Living Standards Framework, Cronbach’s alpha was used (Nunnally, 1978). The findings showed that the overall framework’s internal consistency meets the acceptable mark of .7.



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Discussion and Conclusion

To capture a broader understanding of living standards in South Africa, respondents were asked to explain in their own words what they thought are important factors that contribute to a decent standard of living. Despite the diversity of living standards among the participants, the idea of income as a key driver of living standards was a common theme. This finding is consistent with views of a number of authors such as Martins (2006), Deaton and Grosh (2000); Hurd and Rohwedder (2006), amongst others. The findings of this study have buttressed the established idea that living standards is a multidimensional construct with income, health, education, safety, access to credit, access to transport, access to clean water, access to cheaper energy, public service delivery including quality infrastructure and free time as the key contributors to a decent standard of living.

These findings have also shown that living standards is driven by ownership of physical assets with income being the main determinant of ownership. In addition, income is to a large extent determined by educational qualifications, access to health and basic infrastructure as well as access to public goods. In other words income makes it possible to afford good education, private health facilities that are better equipped and facilities that

offer higher quality service. Income allows one to pay for domestic help in order to have access to free time to pursue personal interests. These findings have also shown that social networks, family support, and neighbourhood safety are important factors that enhance the living standards, thus confirming the views expressed by Narayan, Patel, Schaft et al (2000); and Filmer and Pritchett (1999) about the relationship between income and living standards. Filmer and Pritchett (1999) support the view that household wealth determines education attainment, which in turn influences living standards of households. This paper has has contributed to knowledge by showing that: there is a striking contrast between the affluent, the moderately well-off and the poor in terms of what they view as important factors that contribute to decent living standards. The affluent emphasised security, free time to pursue personal interests, alternative energy sources as important determinants of living stardards, whereas their poorer counterparts identified clean water, housing, food, transport, and entertainment as key contributors to decent living standards. The results showed that the moderately affluent valued education, health, community and neighbourhood support. Irrespective of demographic composition of a household, affluence indicators including property or land ownership, owning a computer and a generator,



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being educated and having sufficient income to pay for education, were identified as important determinants of living standards in South Africa. Although environmental capital, wellness capital and human capital have been viewed as important enough to enhance living standards, these subdimensions ranked in the bottom of the living standards ladder. Further research with a larger universe of items from literature and key informants is necessary to improve the validity of the subdimensions of the new LSM tool.

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