A LED puzzle

Looking for workBecause of high unemployment and poverty levels, all South African municipalities have been tasked to promote local economic development (LED) as part of their integrated development plans (IDPs). Various central and provincial government departments, organizations such as SALGA as well as consultants provide LED guidance and support to the municipalities. And academics do research on LED and small towns in South Africa and publish their results in scientific journals. Yet, given the above, there is an aspect that really puzzles us.

A vast amount of unstructured data is world-wide being collected and stored digitally within businesses, organizations, and governments1. The ever-growing mountain of data is increasingly viewed as a resource that can be mined for value, both now and in the future. New tools, such as ‘machine learning’ have been developed to extract value and the insights are typically gleaned from correlations, which alone can have extraordinary value2,3. Called ‘Big Data’, this development is expected to transform many aspects of life, and also the professions4.

Our research has since 2010 detected a number of correlations (regularities) that link demographic, economic and entrepreneurial characteristics of South African towns5. For instance, we have detected correlations between: 1. gross value added and population, 2. gross value added and total personal income, 3. Total personal income and enterprise numbers, 4. enterprise numbers and the numbers of enterprises in a wide range of business sectors, and 5. enterprise numbers and the number of different enterprise types. These relationships have significant implications for entrepreneurship in South African towns and for LED planning because they offer predictive capabilities. Given the Big Data trends in the world, it is puzzling to us why our results have found minimal, if any, application in the support and/or planning of LED in South Africa. Even academic LED and small-town research6 gives scant attention to these quantitative predictive capabilities. Recently, important environmental impact assessments (EIAs) also totally ignored this research.

Is there an explanation for why this happens? Perhaps. Our research and findings are positioned in the nexus between ecology and economics. Years ago, Thomas Kuhn7 investigated the structure of scientific revolutions. He coined the term paradigm for the basic concepts and experimental practices of a scientific discipline. He showed that information within a paradigm is easily recognized by scientists and reacted upon. But information that is not consistent with the paradigm is not easily seen and might in some cases even be ignored. Joel Barker referred to such cases as ‘paradigm paralysis’. It seems as if a case of paradigm paralysis might be at work here. If so, it might deny South African communities the benefits of useful predictions.


  1. Martin Ford. The rise of the Robots: Technology and the threat of mass unemployment. Oneworld Publications.
  2. Viktor Mayer-Schonberger and Kenneth Cukier. Big Data. John Murray.
  3. Daniel Kahneman. Thinking, fast and slow. Penguin.
  4. Richard and Daniel Susskind. The future of the professions: How technology will transform the work of human experts. Oxford.
  5. Google Daan Toerien ResearchGate for publications
  6. For example: Hoogendoorn, G. & Visser, G. (2015). South Africa’s small towns: A review on recent research. Local Economy: DOI: 10.1177/0269094215618865.
  7. Thomas Kuhn. The structure of scientific revolutions. University of Chicago Press.

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