INTRODUCTION

This statistical release presents a selection of indicative findings and additional tables from Stats SA’s 1999 October household survey (OHS), with particular emphasis on poverty indicators. The survey gathered detailed information on approximately 140 000 people living in 30 000 households across the country. This release also compares available key data related to poverty in October 1999 with data from the October 1996, 1997 and 1998 surveys.

The OHS is an annual survey, based on a probability sample of a large number of households (ranging from 16 000 in 1996 to 30 000 in 1997, then back to 20 000 in 1998, and up again to 30 000 in 1999, depending on the availability of funding). It covers a range of development and poverty indicators, including unemployment rates (official and expanded), according to standard definitions of the International Labour Organisation (ILO; for these definitions see Note 1 below), access to education and access to infrastructure.

Stats SA wishes to express its sincere thanks to both the Department for International Development (DFID) of the United Kingdom and the South African Office of the Presidency for making the 1999 OHS possible. The Office of the Presidency approached DFID for funding, and DFID provided the required eight million rands to undertake the survey, with prime emphasis on poverty monitoring.

Funding from DFID also made it possible to increase the sample size from 20 000 households in 1998 to 30 000 in 1999. The sampling procedure in 1999 made use of Stats SA’s first master sample. More details of the various OHS sample sizes and sampling and weighting procedures are given in Notes 2, 3 and 4 below.

POVERTY MONITORING

Poverty measurement and monitoring are becoming increasingly important worldwide not only for informed policy development, but also to study the implementation of policies to address the issue. In 1996 the Organisation for Economic Co-operation Development (OECD) formulated a strategy for development based on the following seven international goals:

• Reduction by half of the proportion of people living in extreme poverty by 2015.
• Universal primary education by 2015.
• Elimination of gender disparity in primary and secondary education by 2005.
• Reduction of infant and child mortality by two-thirds of the 1990 levels by 2015.
• Reduction of maternal mortality by three-fourths of the 1990 level by 2015.
• Access to reproductive health services through the primary health-care system for all individuals of appropriate ages, including safe family planning methods by 2015.
• Reversal of trends of loss in environmental resources by 2015 (Udjo et al., 2000).

Since these are essentially development goals they need to be operationalised and measured in a standard way across countries. In 1998, the OECD, the United Nations and the World Bank derived a set of 22 core indicators, called the Common Country Assessment Indicators. Of these indicators, the following may be monitored through OHS 1999:

• The creation of full employment, including the extent of employment in the working age population, the unemployment rate and the informal sector as a percentage of total employment.
• Access to housing and facilities, including adequate shelter, safe drinking water and sanitation.
• Access to education, including primary and secondary education and increased literacy.
• Gender equity, including the ratio of girls to boys in secondary schools and the ratio of women to men in paid employment outside agriculture.

For those who wish to do further analyses, and to examine the indicators in more detail, the data and meta-data for this release are available in ASCII format on CD-ROM. They are also available as self-design tables linked to maps in SUPERCROSS, Australian-designed software that has previously been successfully applied to Census ’96.  

Other indicators, for example the population by sex, age and ethnicity, life expectancy by birth and sex, infant, child and maternal mortality, monetary value of a basket of food for minimum nutrition, GDP per capita and household income, are available from other Stats SA sources, for example the suite of economic series, Census ’96 and the vital statistics registers.

Table A summarises the core development indicators relevant to the OHS, and how they are measured in South Africa. The OHS gives more richness and detail than the core figures given in Table A. It also allows for more in-depth exploration of relationships between variables.

The indicators which are not available in the OHS are given in Appendix 1 at the end of this section.

For income-based poverty, the 1995 Income and Expenditure Survey (IES), rather than the OHS, has been cited here. There was insufficient time for this publication to do calculations across the various OHS data sets and harmonise the data for the relevant comparisons. These calculations will nevertheless be repeated for the OHSs in the near future. But the IES is indeed useful for this purpose. It explores income and expenditure in more depth than is possible in a multipurpose survey such as OHS. The information from the two surveys is linked, since the same households are visited for both the IES and the OHS. The next IES will take place in September and October 2000.

Source: Adapted from Udjo et al., 2000

The next section of this release, Key comparisons in relation to poverty monitoring, compares aspects of the 1999 OHS with those of 1996, 1997 and 1998. It examines labour market issues, including employment and unemployment according to both the official and the expanded definitions. It also examines access to infrastructure and facilities in urban and non-urban environments, as well as focusing on aspects of other poverty indicators such as access to education and gender equity in occupations in 1999. Other breakdowns relevant to poverty for OHS 1999, and several other development-related variables, are covered in the later section of Tables.

A fuller comparison of the OHSs, from 1994 to 1999, will follow at a later stage when all existing OHS data sets have been re-weighted to the new population estimates based on the 1996 population census, and harmonisation across data sets has been completed.

KEY COMPARISONS BETWEEN THE OHSs OF 1996 TO 1999 IN RELATION TO POVERTY MONITORING

Certain changes are difficult to detect over a one- or two- or even a four-year time period, since they become observable only in the longer term. For example changing patterns in the level of education of the population aged 20 years or more are difficult to isolate in such a short time frame. On the other hand, some changes, such as access to employment and to formal housing, are easier to detect over this time period.

This summary focuses on possible changes between 1996 and 1999. Such findings need to be viewed with caution, since they are based on four separate cross-sectional sample surveys. Stats SA has, however, calculated standard errors and confidence intervals for certain variables to take sampling error into account.

Labour market trends in the OHSs from 1996 to 1999, based on the official definition of unemployment

Poverty and labour market status go hand in hand. Therefore it is important to measure labour market trends and how they change over time. Table B below compares overall labour market trends from October 1996 to October 1999, based on the official definition of unemployment (see Note 1 for this definition). It presents information regarding the estimated total number of people in the age category 15–65 years (those of working age), and the estimated change in the size of this population over time.

It also indicates the number of people in this age category over the four-year period who were not economically active (for example, students, full-time homemakers and the disabled who were unable to work), and those who were economically active (both the employed and the unemployed according to the official definition of unemployment).

In the 1996 and 1997 OHS statistical releases, those working in the mining sector were excluded from the calculations of labour force statistics, since the sampling frame did not adequately cover mining hostels. But in 1998 and 1999, the sampling frame was able to adequately include these hostels. In this publication, Stats SA has included those working in the mining sector in 1996 and 1997 wherever possible, even though they were excluded previously, to make the data comparable with 1998 and 1999. The 1996 and 1997 mining employment figures were obtained from the formal establishment-based Survey of total employment and earnings (STEE).

Among the employed, Table B compares the actual number of people working in business establishments according to the STEEs of September 1996 to September 1999, with the number employed in various other types of employment, across the four OHSs. STEE does not presently collect information on the following:

1. agriculture, hunting, forestry and fishing;
2. restaurants and other eating and drinking places;
3. boarding houses, caravan parks and guest farms;
4. water and air transport;
5. financial institutions other than banks and insurance companies;
6. real estate and business services;
7. private educational services;
8. medical, dental and other health services;
9. welfare and religious organisations; and
10. recreational and cultural services.

In Table B, the heading ‘activities not covered in STEE’ includes formal sector industries, divisions and groupings which are not covered in STEE.

The table shows that:

• Overall, the number of jobs in the formal sector as measured by STEE declined from 5,2 to 4,8 million between September 1996 and September 1999 (row a).
• At the same time, the total number of those employed, including those in jobs that are not covered in STEE, increased from 9,3 million in 1996 to 10,4 million in 1999. The biggest increase took place between 1998 and 1999, from 9,4 million to 10,4 million.
• The size of the informal sector, in particular, has increased steadily over time, but especially between October 1998 and October 1999, when it appeared to increase from 1,3 million to 1,9 million (excluding domestic work). In the formal sector, the increase was mainly found in industries not covered by STEE and also in agriculture.
• At the same time, a larger proportion of people were entering the labour market and looking for work, leading to a rise in the total number of economically active people from 11,5 million in 1996 to 12,6 million in 1997 and 13,5 million in 1999 (row c).
• The numbers of unemployed people, according to the official definition, rose from 2,2 million in 1996 to 3,2 million in 1998, and stayed the same in 1999. When employment in the mining sector is included, the official unemployment rate was 19,3% in 1996, increasing to 21,0% in 1997 and to 25,2% in 1998, but then decreasing to 23,3% in 1999. The rate reflects the unemployed according to the official definition, expressed as a proportion of the economically active. It may thus be seen that the number of unemployed is unchanged between 1998 and 1999, but the proportion of unemployed is slightly smaller since the number of economically active people increased.

* The sample size was smaller (16 000 households), and more clustered, in 1996 (20 households per combined cluster of two enumerator areas or EAs), than in 1997 (30 000 households and 10 households per EA), 1998 (20 000 households and 10 households per EA) and 1999 (30 000 households and 10 households per EA). Therefore statistics by industry should be treated with caution. The measurement of employment in the agricultural sector may not be stable due to differences in sampling methodology.

** The questionnaire in 1996, while differentiating between the formal and informal sectors with regard to employers (and the self-employed), did not make this distinction for employees. Subsequent surveys have however made this distinction for employees as well, thus giving a more complete picture of the informal sector. The size of the sector in 1996 has therefore been estimated on the basis of the proportions of informal employers and employees in 1997, 1998 and 1999.

*** For a list of formal industries not covered by STEE, see the previous page.

Confidence limits

Table C indicates 95% confidence limits for each estimate, as calculated for 1998 and 1999, regarding the percentage of the total population aged 15–65 years who were economically active, the proportion of economically active who were employed, and the percentage of economically active people who were unemployed. The statistics from 1996 and 1997 are not indicated here, since confidence limits were calculated by excluding the mining industry.

The table is read as follows. Column (ii) indicates that that an estimated 48,8% of people aged 15 to 65 years said that they were economically active in 1998 while an estimated 51,5% said they were economically active in 1999. Columns (iii) and (iv) indicate that we are 95% confident that the true or population value for 1998 (estimated at 48,8%) falls somewhere between 48,0% and 49,6%. For 1999, we are 95% confident that the population value (estimated 51,5%) falls somewhere between 50,8% and 52,3%. There is indeed no overlap between the two ranges for 1998 and 1999. Therefore we are 95% confident that the difference in the percentage of people who said they were economically active in 1998 as against 1999 is not due to sampling error. Thus a higher proportion of people were economically active in 1999 than in 1998.

The table also shows that we are 95% confident that sampling error cannot account for:

• the larger proportion of people who were employed in 1999, compared with 1998, or
• the overall decreases in the unemployment rate.

Labour market growth

Figure 1 is a trend line based on the official definition of unemployment, as derived in Table B above, for 1996 to 1999. It confirms the following:

• The size of the not economically active population has shown a steadily decreasing trend over time, from its highest level of 13,4 million in 1997 to 12,8 million in 1999. This means that more people, for example previous homemakers and students, as well as new entrants to the labour market, were available for work in 1999, and had looked for work in the four weeks prior to the OHS interview, compared with 1996, 1997 and 1998.
• At the same time, the trend line for the economically active population has increased steadily over this time period, from 11,5 million in 1996 to 13,5 million in 1999.
• The trend regarding the number of employed people has also increased over time, from 9,3 million in 1996 to 10,4 million in 1999.
• The number of unemployed people increased gradually from 2,2 million in 1996 to 3,2 million in 1998 and 1999.
• Not shown in the graph is that between October 1996 and October 1999, an increasing number of people were estimated to be within the age category 15–65 years (24,7 million in 1996 rising to 26,3 million in 1999).

 Labour market trends in the OHSs from 1996 to 1999, based on the expanded definition of unemployment

The main difference between the official and the expanded definitions of unemployment is the requirement in the former that, in order to be classified as unemployed, a person must have engaged in job seeking in the four weeks prior to the interview for the survey (see Note 1 for both definitions). These definitions have a significant effect on the size of what is considered to be the economically active population. Table D below compares overall labour market trends in 1996, 1997, 1998 and 1999, based on the expanded definition of unemployment.

* The sample size was smaller (16 000 households), and more clustered, in 1996 (20 households per combined cluster of two enumerator areas or EAs), compared with 1997 (30 000 households and 10 households per EA), 1998 (20 000 households and 10 households per EA) and 1999 (30 000 households and 10 households per EA). Therefore statistics by industry should be treated with caution. The measurement of employment in the agricultural sector may not be stable due to differences in sampling methodology

** The questionnaire in 1996, while differentiating between the formal and informal sectors with regard to employers (and the self-employed), did not make this distinction for employees. Subsequent surveys have however made this distinction for employees as well, thus giving a more complete picture of the informal sector. The size of the sector in 1996 has therefore been estimated on the basis of the proportions of informal employers and employees in 1997, 1998 and 1999.

*** For a list of formal industries not covered by STEE, see p.iii.

Breakdown of unemployment rates by urban and non-urban areas, gender and population group

Since the 1996 and 1997 OHSs did not adequately include the mining sector, and since STEE does not allow for breakdowns by urban/non-urban area, gender or population group, in this section we exclude 1996 and 1997 comparisons, and compare the OHS findings of 1999 with 1998. In both of the latter years, the inclusion of mining hostels in the sampling frame, and drawing a probability sample of hostels, ensured adequate coverage of the mining industry. (For the relevant unemployment rates for the two earlier years by type of area, gender and population group, excluding mining, in relation to OHS 1998, the reader is referred to the OHS statistical release (P0317) of May 2000.)

Unemployment rates were consistently slightly lower in 1999 than in 1998, not only by urban or non-urban place of residence, but also by gender and population group, using either the official or the expanded definition of unemployment. Table E below compares unemployment rates in 1998 and 1999 by type of area, population group and gender, in terms of the official definition of unemployment, while Table F compares them using the expanded definition.  

* Each percentage is a percentage of all people in that particular category. For example, in the block labelled (c) in column (ii) we see that in 1998, according to the official definition of unemployment, 15,5% of economically active coloured men living in urban areas were unemployed.

** Number of responses were too few for this analysis.

Table E shows that:

• The slight decrease in unemployment rates in urban areas, using the official definition, is in most cases not significant, and can be possibly attributed to sampling error. In October 1998, for example, 19,4% of economically active men in urban areas and 27,1% of economically active women in urban areas were unemployed, as against 18,4% of economically active men and 25,8% of economically active women in October 1999.
• In non-urban areas, however, the changes were appreciable. Proportionately more economically active men (26,1%) and women (36,9%) were unemployed in 1998 than in 1999 (22,7% of men and 32,3% of women). This difference cannot be attributed only to sampling error. It may indicate that higher proportions of people in non-urban areas than previously were classifying themselves as subsistence or small-scale farmers, and as informal sector workers.
• The highest official unemployment rate in October 1999 was found among economically active African women living in both urban (35,0%) and non-urban areas (34,9%).
• Economically active white men were least likely to be unemployed in 1998 (3,9%) and 1999 (4,4%).

Table F shows the corresponding breakdowns for the expanded unemployment rate. It shows a similar pattern to Table E, but, as can be expected, the unemployment rates are higher.

* Each percentage is a percentage of all people in that particular category. For example, in the block labelled (c) in column (ii) we see that in 1998, according to the expanded definition of unemployment, 21,8% of economically active coloured men living in urban areas were unemployed.

** Number of responses were too few for this analysis.

Employment trends from October 1996 to October 1999

Figure 2 indicates the employment trends between October 1996 and October 1999. The Y1 or left-hand vertical axis of the graph, and the lines on the graph, show employment in those sectors or industries which are not covered by STEE. For example it shows that employment in domestic work remained more or less constant over the four years (bottom line on the graph). The Y2 or right-hand vertical axis and the bars, on the other hand, show employment in the industries that are covered by STEE.

• The graph shows a steady downward trend in formal employment and an increase in informal employment.

It also shows a slight increase in agricultural employment, perhaps because of improved capture of small-scale and subsistence farmers as being employed, rather than as being not economically active. Although the differences are not statistically significant year-by-year, this may be due to smaller sub-sample sizes, since the trend is clear.  

 Access to infrastructure in urban and non-urban areas by population group of household head

Access to infrastructure is another important indicator of poverty. This varied, not only by whether the household lived in an urban or non-urban area, but also by the population group of the household head.

Table G below indicates these findings. Only the percentages of all households with access to infrastructure, and those households headed by Africans, are shown in this table, since relatively few coloured-, Indian- and white-headed households did not have access to these facilities.

The table shows the following:

• Across all four years, a larger proportion of urban households continued to have access to infrastructure than non-urban households.
• The access of African-headed households to infrastructure in general, as well as in urban and non-urban areas, had improved in most respects in October 1999 compared with October 1996, taking sampling error into account. For example, in 1996, 48% of African-headed households had access to running water in the dwelling or on site. This proportion had increased to around 56% in 1999.
• African-headed households in non-urban areas were the least likely group, overall, to have access to infrastructure, in all four years. For example, in 1996, only 21% of non-urban African-headed households had access to running water inside the dwelling or on site, compared with 78% of African-headed households in urban areas. By 1999, however, the proportion of African-headed households in non-urban areas with access to running water in the dwelling or on site had increased to 27%.
• In urban areas, a more or less constant proportion of households had access to running water inside the dwelling or on site. The differences in the proportion of households in urban areas with access to running water inside the dwelling or on site across the four years are not statistically significant. (Those readers who require more information on confidence limits for a variety of variables across the four years should contact Stats SA’s User Enquiries.)

* Each percentage is a percentage of all households in that particular category. For example, in column (ii) of the second row of the block labelled (c) we read that in non-urban areas in 1996 25,4% of all households had running water inside the dwelling, in the backyard or on the site where they lived.  

Type of dwelling in which households live in urban and non-urban areas

Figure 3 shows that approximately 67% of households were living in a formal dwelling such as a house on a separate stand, a flat in a block of flats, a townhouse or a retirement village in October 1999. Among African-headed households in urban areas, 59% were living in these types of dwellings, while 25% were living in shacks, 10% in backyard rooms, 4% in other accommodation such as a tent or caravan, and 1% in traditional dwellings. Among African-headed households in non-urban areas, 28% were living in traditional dwellings. Amongst households headed by other population groups, the vast majority was living in formal dwellings. Coloured- and Indian-headed households in non-urban areas are not shown in the graph, since the sample size was too small for further breakdowns. These figures have not changed significantly over time. 

Access to education

Figure 4 indicates the proportion of people aged 20 years or more, in five-year age groups, in total and in urban and non-urban areas, who said they could read in at least one language in October 1999. The lines on the graph show the proportion of people in urban and non-urban areas who claimed they could read. The bars show the total proportion of people making this claim.

The graph shows the following:

• The overall proportion of people who said that they could read decreased with increasing age. For example, 95% of those aged 25 to 29 years said that they could read. But only 58% of those aged 65 years or more said that they could read.
• In urban areas, a higher proportion in the older age categories claimed they could read, compared with those in non-urban areas. For example, 78% of those aged 65 years or more living in urban areas claimed they could read, as against 38% of those in this age category in non-urban areas.
• As age decreases, a steeper increase in the proportion of those who can read is found among people living in non-urban areas, compared with those in urban areas. In non-urban areas, for example, 48% of those aged 60 to 64 years claimed they could read, increasing sharply with a decrease in age to as many as 96% among those aged 20 to 24 years. In urban areas, on the other hand, 84% of those aged 60 to 64 years claimed they could read, increasing to 99% among those aged 20 to 24 years.

Further breakdowns show that:

• Overall 89% of men and 85% of women reported being able to read. There is no difference in the proportions of women and men able to read up to age 35, but fairly marked differences among older people. Of those aged 65 or older, 62% of men as against 55% of women reported being able to read.

Although the question about being able to read was not asked in earlier OHSs, the proportion of those who have not attended school in each age category shows a similar trend over time. 

Gender equity

To examine one aspect of gender equity, we look at the proportion of employed men and women in different types of occupations in OHS 1999.

Figure 5 indicates that men and women tend to cluster in different occupational categories. For example:

• 20% of men are in craft and related trade occupations or work as artisans, compared with 5% of women.
• Less than 1% of men are domestic workers, as against 17% of women.
• 6% of men are in clerical occupations, compared with 15% of women.

The occupations in which mainly women are found tend to receive lower remuneration than those in which mainly men are found.

NOTES

The urban population constituted 54,1% of the total population according to Census ’96. In the weighting matrix for the 1999 OHS, the proportionate distribution of the population by urban and non-urban areas was based on the population census of 1996. The urban/non-urban proportion is one of the variables used to weight successive OHSs to the population distribution of Census ’96 (the others in 1999 were age, gender and population group), thereby rendering them comparable in respect of other variables. It follows that urbanisation cannot be detected from successive OHSs, but will be measured by comparing Census ’96 with Census 2001.

8. The mining sector

The reader is reminded that the Stats SA releases of the findings of OHS 1996 and 1997 excluded those working in the mining sector. In some of the tables given in this statistical release, however, this sector is included, since it was possible to draw an adequate sample of mining hostels.

Stats SA has calculated 95% confidence limits for some key variables, in 1996, 1997, 1998 and 1999. These are available on request to users who require this information.  

A household consists of a single person or a group of people who live together for at least four nights a week, who eat together and who share resources.

Population group describes the racial classification of a particular group of South African citizens. The previous government used legislation to impose this type of classification, to divide the South African population into distinct groupings on which to base apartheid policies. For quite a different reason it remains important for Stats SA to continue to use this classification wherever possible. It clearly indicates the effects of discrimination of the past, and permits monitoring of policies to alleviate discrimination. Note that, in the past, population group was based on a legal definition, but it is now based on self-perceptions and self-classification. An African/black person is someone who classifies him/herself as such. The same applies to a coloured, Indian/Asian or white person.

A hostel is a communal living quarter for workers, provided by a public organisation such as a local authority, or a private organisation such as a mining company. These were residential dormitories established primarily for migrant workers during the apartheid era, and they continue to house people working in certain industries, such as the mining industry.

Institutions are communal temporary, semi-permanent or permanent living arrangements for people in special circumstances, for example prisons, police cells, school boarding facilities, homes for the aged or the disabled, hotels and hospitals.

Workers include the self-employed, employers and employees. 

DATA SETS, REPORTS AND STATISTICAL RELEASES AVAILABLE FROM STATS SA

The 1996, 1997, 1998 and 1999 OHS data sets, weighted to the 1996 population census (adjusted upwards to take population growth into account in 1997 and 1998) are available on CD-ROM from Stats SA’s User Enquiries.

They are also being made available in easy-to-run map-linked tabulation software called SUPERCROSS. For further details please contact User Enquiries.

Comparisons between four past OHSs (1994 to 1997) in respect of employment and unemployment and the associated breakdowns have already been issued, both as a statistical release (PO317.10) and as an analytical report: Unemployment and employment in South Africa (1998). These publications are available from User Enquiries. Because the 1996 census results were not yet available to use for weighting when they were written, the data in these two publications had to be weighted according to the post-enumeration survey of the census. They differ slightly from those reported here. 

REFERENCE

Udjo, E.O., Orkin, F.M. & Simelane, S. (2000). Levels of social indicators in South Africa in relation to international goals of development. Paper presented at the Economic Commission for Europe Seminar: Statistics for Social Development, Geneva, 27 June 2000.

Source: Adapted from Udjo et al., 2000