Unemployment in Gauteng

Introduction

Unemployment is a significant economic challenge in South Africa, affecting millions of people and contributing to socio-economic issues such as poverty, inequality and social unrest. There are two main definitions of unemployment: official and expanded definitions. The official definition of unemployment includes individuals of the working age population (15 to 64 years) who are jobless, willing and able to work, and actively looking for employment (StatsSA, 2024). By this measure, South Africa and Gauteng’s unemployment rates stand at 32,9% and 34,2%, respectively, in the first Quarter of 2024 (Libera, 2024; SANews, 2024; StatsSA, 2024). The expanded definition of unemployment encompasses individuals who are unemployed and actively looking for work and those who are discouraged from looking for work. Discouraged workers are often overlooked in policy dialogues even though they affect the unemployment rate (Ranchhod and Dinkelman, 2007; Sauls, 2023). According to this definition, the South African and Gauteng unemployment rates are 41,9% and 38,9%, respectively, in the first Quarter of 2024 (StatsSA, 2024).

The Gauteng City-Region Observatory’s (GCRO) Quality of Life (QoL) 6 (2020/21) Survey provides valuable insights into the dynamics of the working age population (15 to 64 years) and unemployed workers in Gauteng. By utilising the data from this survey, we uncover the socio-economic dynamics of the working age population and spatial patterns of unemployment in the province. The analysis reveals that unemployment levels are disproportionately higher among individuals aged 18 to 34 years, those who have not completed high school, Black Africans, females and members of low-income households. The study also found that unemployment is unevenly distributed across the province. Wards in Gauteng’s core and economic nodes have lower unemployment levels while townships and informal settlements are characterised by higher concentrations of unemployment.

Employment status of the working age population

The GCRO’s QoL 6 survey (2020/21) explores the employment status of the working age population in Gauteng ( Figure 1). The survey asked respondents, “In the past 7 days, did you do any type of work, business, or activity for which you got paid or expected to be paid?”. Of the total respondents of the working age population, 45% indicated that they are employed. The survey also asked, “Are you unemployed and looking for work?”, and 37% of respondents reported that they are unemployed and searching for work. In comparison, 3% of respondents reported that they have given up looking for work, and 15% are not economically active (NEA). NEA refers to the working age population that is neither employed or unemployed, including full-time students, retired people, homemakers, persons with disabilities, those caring for family members and those who do not need or want to work (Leaker, 2009). QoL 6 (2020/21) survey was conducted amid the COVID-19 pandemic and associated lock downs, which had a major impact on employment in the province. While some of the results show long standing patterns, others might be moment specific.

Note: While the working age population is defined as 15 to 64 years (StatsSA, 2024), the QoL survey does not interview respondents 17 years or younger.

Figure 1: Employment status of working age population.

Socio-economic characteristics of the working age population

There are significant variations in unemployment proportions across different socio-economic characteristics (Figure 2). According to the expanded unemployment definition, the youth (18 to 34 years) have the highest proportion of unemployed workers (46%). Despite this, the youth have the lowest proportion of discouraged workers (2%), compared to the 50 to 64 age group (5%). In terms of education, individuals who have not completed high school, including those with no schooling, primary, or incomplete secondary schooling, have the highest expanded unemployment proportion (47%) compared to those who completed matric (43%) or have more than matric (24%). Discouragement is also higher among those who have not completed high school (4%).

Figure 2: Socio-economic characteristics of the working age population in Gauteng.

There are significant variations in unemployment proportions across different socio-economic characteristics. According to the expanded unemployment definition, the youth (18 to 34 years) have the highest proportion of unemployed workers (46%). Despite this, the youth have the lowest proportion of discouraged workers (2%), compared to the 50 to 64 age group (5%). In terms of education, individuals who have not completed high school, including those with no schooling, primary, or incomplete secondary schooling, have the highest expanded unemployment proportion (47%) compared to those who completed matric (43%) or have more than matric (24%). Discouragement is also higher among those who have not completed high school (4%).

Racial and gender disparities are also evident. Black Africans have the highest levels of expanded unemployment (43%), much higher than Whites (12%). Females also experience a higher level of expanded unemployment (45%) compared to males (33%). The levels of discouragement are highest among Black African and Indian/Asian groups (3% and 4%, respectively) and females (3%).

These findings are generally in line with existing literature. Previous studies found that expanded unemployment is disproportionately higher among the youth, less educated individuals, Black Africans and females (Bhorat, 2007; Wakefield & Swanepoel, 2020; Raifu and Adeboje, 2022; Sauls, 2023; StatsSA, 2023). However, our analysis also indicates that levels of discouragement are higher among older workers (50 to 64 years) compared to youth (18 to 34 years).

The proportion of expanded unemployed workers decreases as household income increases. Households earning R12 801 or more have a lower proportion of unemployed individuals compared to those earning R1 - R3200. This suggests that individuals in higher-income households may feel less pressure to search for work due to financial support from extended family members (Putter et al., 2021). Lower-income groups have a higher proportion of actively searching for work because they cannot afford to be unemployed. They also have higher levels of discouragement due to repeated failures and limited resources (Shah and Sturzenegger, 2022).

Spatial variability of unemployed workers in Gauteng

According to the expanded unemployment definition, 47% of the labour force in Gauteng is unemployed (Figure 3). Although unemployment is a challenge across most municipalities, it is more severe in some municipalities than in others. Unemployment rates range from a low of 35% in Midvaal to a high of 55% in Lesedi. Lesedi, Rand West, and Emfuleni local municipalities face the most severe unemployment challenges, with more than 50% of their labour force unemployed. In the three metropolitan municipalities—Tshwane, Johannesburg, and Ekurhuleni—close to half of the labour force is jobless. In contrast, Midvaal, Mogale City, and Merafong City experience the least severe unemployment challenges.

Figure 3: Employment and unemployment status of respondents across municipalities.

Note: The QoL Survey cannot be used to generate unemployment rates that are strictly comparable with the Labour Force Survey (LFS) and other official statistics. Unlike the LFS, which enumerates all members 15 years and older in the households selected for enumeration, QoL interviews randomly select an adult 18 years and older in the selected household. This leads to different results. While the unemployment rates generated should not be compared with official labour market statistics, they are nonetheless usefully indicative.

Figure 4 illustrates the spatial variability of unemployment levels in Gauteng wards. There are lower unemployment levels in core areas and major economic nodes. This includes areas such as Sandton, Randburg, and Midrand in the northern parts of Johannesburg; Alberton, Benoni, Edenvale and Springs in Ekurhuleni; Centurion and Pretoria Central in Tshwane; Vereeniging in Emfuleni; Krugersdorp in Mogale City; and Elandsridge in Merafong.

Figure 4: Spatial variability of unemployment levels in Gauteng wards.

The map also reveals that townships, informal settlements and some inner-city areas have the highest proportions of unemployed respondents. Areas where unemployment is over 50% include Soshanguve, Bekkersdal, Lenasia, Fleurhof, Mamelodi, Ekangala, Hammanskraal, Khutsong and Bronkhorstspruit. There are unemployment hotspots, where more than 80% of the labour force is unemployed. These hotspots include Sebokeng, Orange Farm, Katlehong, Tembisa, Tsakane and Mabopane. Additionally, there is clustering of unemployment according to neighbourhood types. Townships such as Mabopane and Soshanguve form clusters of unemployment higher than 50%. This high unemployment does not spill over into adjacent affluent suburbs. Suburbs such as Centurion and Sandton exhibit clustering of lower than 50% unemployment. Therefore, the problem of higher unemployment is mainly a township and informal settlement problem.

Several factors contribute to the higher levels of unemployment in townships and informal settlements. Compared to other neighbourhoods, townships and informal settlements supply mostly low-skilled workers, while the region’s economy generates higher-skilled jobs (Shah and Sturzenegger, 2022). Also, young, less educated workers often reject low-wage jobs due to a perceived lack of dignity and insufficient wages (Webb, 2021; Dawson, 2022). Additionally, local employment opportunities are limited despite investments in local economic development programs like Urban Renewal Projects and Industrial Parks (Gumbo and Mafela, 2020). Lastly, the spatial job mismatch from living far from job centres makes job search difficult, due to expensive and time-consuming commutes (Kerr, 2017; Shah and Sturzenegger, 2022).

Conclusion

Despite being an economic powerhouse and producing the largest number of formal jobs in the country, Gauteng faces high unemployment levels. The analysis revealed that unemployment tends to be higher among the youth, less educated, Black Africans, females and individuals from low-income households.

Spatial disparities in the distribution of unemployment are also evident. Unemployment is disproportionately higher in townships, informal settlements and some inner-city areas. Clustering of unemployment is also evident. Wards with higher unemployment levels tend to be geographically closer, indicating that unemployment in one area influences neighbouring areas. In contrast, medium- and high-income suburbs do not experience similar levels of unemployment, even when adjacent to high-unemployment areas.

References

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Comments and input: Richard Ballard and Graeme Götz

Map design: Jennifer Murray

Suggested citation: Modiba, M., Ndlovu, T., Musiyandaka, D., Khanyile, S. and Semu, T. (2024). Unemployment in Gauteng. Map of the Month. Gauteng City-Region Observatory. July 2024. https://doi.org/https://doi.org/10.36634/AJFY1899.