The ongoing COVID-19 pandemic, and the policy measures to combat it, are having profound effects on the economic and social lives of citizens. They are threatening employment as well as the long-term livelihoods and well-being of millions around the world.
South Africa has not been exempted from the socio-economic effects of the pandemic. Its economy has been in decline since it entered a stringent lockdown as the main public health response to curb the spread of the virus in March 2020. This is reflected in its latest available statistics for both gross domestic product (GDP) and employment.
The country’s economy wasn’t in great shape even before the lockdown. It was hit hard by the global financial crisis in 2008, recording average growth just above 2% between 2008 and 2012. And now the National Treasury has forecast that the economy will contract by 7.8% in 2020 due to COVID-19 measures.
The unemployment rate in South Africa has been persistently high over time, hovering above 20% over the last decade. The official unemployment rate reached an all-time high of 30.8% during the third quarter of 2020.
Understanding the effects of the global pandemic on employment – at aggregated and sectoral levels – is therefore key for governments, policymakers, workers and employers. This should help minimise the long-term effects of the pandemic while ensuring the safety of individuals and the sustainability of businesses and jobs.
This article focuses on providing results from applied economic analysis on the sectoral winners and losers during the pandemic. We also identify the people who have been affected the most and evaluate the South African government’s policy response to minimise its effects.
So far the government’s response to address the impact of the pandemic has consisted of two main interventions: a stimulus package launched in April 2020 and in October 2020 a more long-term recovery plan. Our article focuses on the short-term stimulus package.
Given that data on sectoral GDP, aggregate GDP and poverty lag the employment figures, results from economic modelling such as the one we set out here can help provide some useful information in the meantime.
This article presents the results of our COVID-19 policy response simulations. The models trace a variety of channels through which the pandemic affected the economy.
The simulation exercise showed that the sectors and workers that were most affected by the COVID-19 pandemic were the mining/mineral sectors, the construction sector, the transport sector and most of the services sectors such as retail trade and accommodation.
But the spillover effects meant that in the end all sectors were affected. Reduced economic activities led to reduced labour and capital demand. This, in turn, led to reduced income to all agents in the economy. Households were not spared. In particular, households dependent on unskilled labour income suffered the most because these workers were the most constrained after the lockdown.
Mining and minerals were affected by the lockdown as well as the drop in the mineral prices on the world market. Based on the model results, we estimated that 864,000 were affected in a mild scenario of the COVID-19 crisis. In a severe expression of the crisis we estimate 1.3 million jobs being affected. This is in line with the results from the Quarterly Employment Statistics by Statistics South Africa. This showed losses in full-time employment of over 568,000 (-6,2%) year-on-year between June 2019 and June 2020 (at the peak of the COVID-19 lockdown) and losses of over 525,000 (-5.7%) in full-time employment year-on-year between September 2019 and September 2020.
Overall, the effects of the simulated COVID-19 pandemic were quite harsh on both the production and demand sides of the economy. The decline in GDP growth (-10%) has been largely due to the marked slowdown in economic activity coupled with widespread disruptions in both international and domestic supply chains.
Lower GDP growth and increasing unemployment invariably translate to rising unemployment and poverty rates. When extending the analysis to poverty, the modelling results show some modest increase in poverty, increasing by 2.5 percentage points.
In addition, females, particularly the poorest female-headed households, were more negatively affected. This is because they derive a larger share of their income from a lower-skilled type of work.
As the country attempts to gain control over the pandemic, our findings point to the importance of interventions in at least three areas: protecting vulnerable populations, supporting vulnerable sectors and external trade diversification.
It is important to note that, given the paucity of information on the ongoing pandemic, results of this and any modelling exercises will be shrouded by uncertainty. Hence the directions and intensity of changes must be emphasised.
Implications for policy
The most interesting aspect of our findings from a policy intervention point of view is that the decline in employment and poverty is not uniform across skill levels and gender. As is often the case during economic crises, there are winners and losers, and in this case, it is the least skilled workers and poor females who suffer the most.
This suggest that when putting together a building back strategy government should promote investments in the services sectors, help these different sectors to set up protective barriers to allow the different activities to restart and importantly recover some of the lost jobs.
A support package to increase consumers’ purchasing power, and reducing the operating costs of these businesses and industries, would also be effective interventions.
As the country intervenes to cushion the poor, measures to resuscitate economic growth must be put in place at the same time. Policy options could include increasing public investments, accelerating the implementation of existing policies and diversifying the export and import basket. This could include increasing high value added commodities in total exports and increasing the share of primary products in total imports.
The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.
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This content was originally published by The Conversation. Original publishers retain all rights. It appears here for a limited time before automated archiving.By The Conversation