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12 October 2020 | Story Andre Damons
Prof Ivan Turok
Prof Ivan Turok, National Research Foundation research professor at the University of the Free State (UFS) and distinguished research fellow at the Human Sciences Research Council (HSRC).

New evidence provides a detailed picture of the extraordinary economic fallout from the COVID-19 pandemic. All regions lost about a fifth of their jobs between February-April, although the cities began to show signs of recovery with the easing of the lockdown to level 3. Half of all adults in rural areas were unemployed by June, compared with a third in the metros. So the crisis has amplified pre-existing disparities between cities and rural areas.

Prof Ivan Turok, National Research Foundation research professor at the University of the Free State (UFS) and distinguished research fellow at the Human Sciences Research Council (HSRC), and Dr Justin Visagie, a research specialist with the HSRC, analysed the impact of the crisis on different locations in a research report (Visagie & Turok 2020).

The main conclusion is that government responses need to be targeted more carefully to the distinctive challenges and opportunities of different places. A uniform, nationwide approach that treats places equally will not narrow (or even maintain) the gaps between them, just as the blanket lockdown reflex had adverse unintended consequences for jobs and livelihoods.

According to the authors, the crisis has also enlarged the chasm between suburbs, townships and informal settlements within cities. More than a third of all shack dwellers (36%) lost their jobs between February and April, compared with a quarter (24%) in the townships and one in seven (14%) in the suburbs. These effects are unprecedented.

Government grants have helped to ameliorate hardship in poor communities, but premature withdrawal of temporary relief schemes would be a serious setback for people who have come to rely on these resources following the collapse of jobs, such as unemployed men.

Before COVID-19

In February 2020, the proportion of adults in paid employment in the metros was 57%. In smaller cities and towns it was 46% and in rural areas 42%. This was a big gap, reflecting the relatively fragile local economies outside the large cities.
Similar differences existed within urban areas. The proportion of adults living in the suburbs who were in paid employment was 58%. In the townships it was 51% and in peri-urban areas it was 45%.

These employment disparities were partly offset by cash transfers to alleviate poverty among children and pensioners. Social grants were the main source of income for more than half of rural households and were also important in townships and informal settlements, although not to the same extent as in rural areas.  

Despite the social grants, households in rural areas were still far more likely to run out of money to buy food than in the cities.

How did the lockdown affect jobs?

The hard lockdown haemorrhaged jobs and incomes everywhere. However, the effects were worse in some places than in others. Shack dwellers were particularly vulnerable to the level 5 lockdown and restrictions on informal enterprise. This magnified pre-existing divides between suburbs, townships and informal settlements within cities.
There appears to have been a slight recovery in the suburbs between April-June, mostly as a result of furloughed workers being brought back onto the payroll. Few new jobs were created. Other areas showed less signs of bouncing back.

Overall, the economic crisis has hit poor urban communities much harder than the suburbs, resulting in a rate of unemployment in June of 42-43% in townships and informal settlements compared with 24% in the suburbs. The collapse poses a massive challenge for the recovery, and requires the government to mobilise resources from the whole of society.


News Archive

Dr Abdon Atangana cements his research globally by solving fractional calculus problem
2014-12-03

 

Dr Abdon Atangana

To publish 29 papers in respected international journals – and all of that in one year – is no mean feat. Postdoctoral researcher Abdon Atangana at the Institute for Groundwater Studies at the University of the Free State (UFS) reached this mark by October 2014, shortly before his 29th birthday.

His latest paper, ‘Modelling the Advancement of the Impurities and the Melted Oxygen concentration within the Scope of Fractional Calculus’, has been accepted for publication by the International Journal of Non-Linear Mechanics.

In previously-published research he solved a problem in the field of fractional calculus by introducing a fractional derivative called ‘Beta-derivative’ and its anti-derivative called ‘Atangana-Beta integral’, thereby cementing his research in this field.

Dr Atangana, originally from Cameroon, received his PhD in Geohydrology at the UFS in 2013. His research interests include:
• the theory of fractional calculus;
• modelling real world problems with fractional order derivatives;
• applications of fractional calculus;
• analytical methods for partial differential equations;
• analytical methods for ordinary differential equations;
• numerical methods for partial and ordinary differential equations; and
• iterative methods and uncertainties modelling.

Dr Atangana says that, “Applied mathematics can be regarded as the bridge between theory and practice. The use of mathematical tools for solving real world problems is as old as creation itself. As written in the book Genesis ‘And God saw the light, that it was good; and divided the light from the darkness’, the word division appears here as the well-known method of separation of variables, this method is usually employed to solve a class of linear partial differential equations”.

“A mathematical model is a depiction of a system using mathematical concepts and language. The procedure of developing a mathematical model is termed mathematical modelling. Mathematical models are used not only in natural sciences, but also in social sciences such as economics, psychology, sociology and political sciences. These models help to explain systems and to study the effects of different components, and to make predictions about behaviours.”

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