Business Report Economy

A geostatistical and longitudinal analysis of youth underdevelopment: The case of S’kodi in Daveyton

Pali Lehohla|Published

Explore how geostatistical analysis of Thabo's life in S’kodi unveils the harsh realities of youth underdevelopment in South Africa and the urgent need for targeted interventions.

Image: Henk Kruger/Independent Newspapers

To understand the journey of Thabo from Skodi through the Lehohla Ledger I have generated a Local Indicator of Spatial Association (LISA) image that visualizes the socio-economic analysis of Skodi in Daveyton.

This graphic maps the geographical dynamics using Global Moran’s I and LISA mapping to define Thabo's environment.

The image illustrates the spatial auto-correlation in Ekurhuleni, showing that Skodi is part of a "High-High cluster" of deprivation and unemployment.

Additionally, it highlights the "neighbor effect" on educational progress and subsequent economic development, showing how localized conditions affect individual trajectories through a "spatial lag" that influences future access to work-seeking opportunities and human capital advancement.

Who is Thabo?

Thabo is a 14-year-old Grade 8 student residing in Sekoti Phola, a neighborhood within the township of Daveyton in South Africa's Gauteng Province.

Thabo’s world is defined by stark contrasts and systemic challenges.

Image: Supplied.

To understand Thabo, one must first understand that he is not merely an individual navigating his adolescent years; he is a statistical and social construct defined by his geography.

In the context of the provided analysis, Thabo serves as a powerful case study illustrating how historical spatial planning, economic inequality, and environmental factors converge to shape the developmental trajectory of young South Africans.

The socio-economic baseline: life in the "high-high" cluster

Thabo’s world is defined by stark contrasts and systemic challenges. Daveyton is situated peripherally to the economic hubs of Johannesburg and Pretoria, making physical access to job markets difficult and expensive.

The broader municipality of Ekurhuleni suffers from extreme inequality, with a Gini coefficient of 0.633.

Within this region, Sekoti Phola embodies the complexity of South African townships, characterized by a mix of formal RDP housing and burgeoning informal settlements.

For Thabo, this environment translates into a daily reality of "hustling" for survival.

He is growing up surrounded by 6,000 to 8,000 unemployed individuals concentrated per square kilometer, creating intense competition for resources and limiting his exposure to formal economic pathways.

This phenomenon is scientifically captured using the Global Moran’s I and Local Indicators of Spatial Association (LISA) mapping.

These geostatistical tools mathematically "define" Thabo as a resident of a "High-High" cluster.

This means he lives in a geographical hotspot where high levels of socio-economic deprivation are surrounded by other areas experiencing similar deprivation.

This positive spatial autocorrelation creates a powerful "neighbor effect." It dampens Thabo's potential for upward mobility because the economic inactivity is "spatially contagious," reinforcing poverty and limiting his local social capital and job networks.

This environment also forces Thabo to navigate strained infrastructure.

Frequent power outages caused by aging systems and illegal connections, waste management deficiencies, and inadequate water supply impede his educational focus and overall well-being. This creates a precarious backdrop where environmental risk, such as extreme urban heat, compounds his economic vulnerability.

Thabo’s educational and career milestones: a 15-year projection

The analysis utilizes a longitudinal lens, drawing on multi-decade census data to project Thabo’s future at key milestones, from age 14 to 29.

His life path is not predetermined, but the probabilities are heavily skewed by his current coordinates.

  • Age 14 (The Grade 8 Baseline): The First Major Threshold Thabo is currently in a critical phase. Grade 8 marks the entry into secondary education, where success determines progression to matriculation. However, the system is plagued by repetition and dropout. Statistics show a 1-in-6 chance that Thabo might not progress to Grade 9 on time. Since compulsory schooling ends after Grade 9 or age 15, there is a strong temptation for youth in Sekoti Phola to exit the system early to enter the informal labor market, driven by immediate household needs.
  • Age 19 (The Five-Year Projection): The Post-Schooling Transition In five years, Thabo should ideally have completed his National Senior Certificate (Matric). His trajectory will be largely defined by the educational "stream" he chose: General Academic, Technical Vocational, or Technical Occupational. A significant risk is that he could fall into the NEET category (Not in Employment, Education, or Training), which affects over 37% of his age group. Spatial marginalization, including the high cost of transportation and internet access needed for job seeking, discourages labor market participation. His best chances come from workplace experience or the vocational route (like a TVET program), which provides a valued "experience dividend."
  • Age 24 (The Ten-Year Projection): The Labor Market Reality By the time Thabo is 24, his economic status will be heavily stratified by his level of education, creating a stark "Education Divide." University graduates face an unemployment rate of 23.9%, compared to over 51% for those without a Matric. If he successfully leveraging post-schooling education, his chances of employment and higher earnings are vastly improved. However, if he remains in Sekoti Phola without qualifications, he will likely be navigating precarity in the informal sector, working through programs like the Youth Employment Service (YES) or small, local businesses.
  • Age 29 (The Fifteen-Year Projection): The Township Economy and Climate Risk At 29, Thabo enters economic maturity. In a successful trajectory, he could be an entrepreneur contributing to the township's substantial informal economy. However, the long-term effects of spatial marginalization may also be fully realized, with many youth permanently excluded from the formal labor force. This stage also presents a new challenge: environmental vulnerability. Historically disadvantaged areas like Daveyton are more vulnerable to urban heat exposure. Climate models project quadrupling the number of hot nights by 2050. This means a 29-year-old Thabo, living in a crowded dwelling, faces tangible health consequences that further compound his economic disadvantage.

Breaking the spatial trap

Ultimately, the analysis of Thabo's life path shows that his future is mathematically tied to the outcomes of his geographical neighbors. While a LISA map defines him as a resident of a "vulnerability hotspot," it also places him within a dense and vibrant community that is central to South Africa's economic future.

Dismantling the "geography of exclusion" that defines Sekoti Phola is essential for Thabo’s success. The 10,000-word study highlights specific intervention pathways: spatially targeted education support, infrastructure to bolster the informal economy, programs expanding workplace experience, and climate adaptation strategies.

Thabo of Sekoti Phola is more than just a name; he is a case study of the broader South African struggle to achieve equality regardless of where a person is born.

His future is not predetermined, but rather depends on whether policymakers can successfully implement place-based strategies to replace the spatial trap with a true "beacon of opportunity."

As we head to the Local Government Elections, we have to reflect on the Century 1976 to 2076  and ask the difficult question of what my generation of 1976 hands down to that of 2026 and what will the generation of 2026 hand down to that of 2076.

Dr Pali Lehohla is a Professor of Practice at the University of Johannesburg, a Research Associate at Oxford University, and a distinguished Alumni of the University of Ghana. He is the former Statistician-General of South Africa.

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