Making ends meet: spatial and socio-economic variations during the 2020-2021 COVID-19 lockdown in South Africa

Figures & data

Figure 1. Covid-19 infections by province, April 2020 to August 2021

Figure 2. Distribution of unweighted survey responses, showing concentrations in the cape town metro, Gauteng and eThekwini metro.

Table 1. Distribution of population and of survey responses.

PROVINCE	Gauteng	KwaZulu-Natal	Western Cape	Eastern Cape	Limpopo	Mpuma-langa	North West	Free State	Northern Cape	SOUTH AFRICA
Population	25,8%	19,2%	11,6%	11,4%	10,2%	7,8%	6,9%	4,9%	2,2%	100,0%
Sample Response	41,9%	16,7%	12,5%	6,3%	5,0%	4,9%	5,2%	5,8%	1,8%	100,0%

Table 2. Percentage experience of different forms of socio-economic stress.

Measures of Socio-Economic Distress	Rounds (figures in percentages)
	1	2	3	4
Hunger	31	35	33	37
Fear	44	30	34	31
Been laid off, retrenched, or told ‘no work, no pay’	3
Not able to work and employer forced me to leave	5
I lost my job during lockdown and could not find a new job		16
I have the same job but my hours or wages are different		11
Unemployed, looking for work			45	46
Unemployed, not looking for work			2	2
Difficulty in covering my expenses	78	81	84	86

Figure 3. Provincial distribution of (a) hunger and (b) fear, using unweighted data (R1,2,3).

Table 3. Binary logistic regression of financial distress with background variables^a.

	B	S. E.	Wald	Sig
Constant	−1.303	.421	9.602	.002
Education status of respondent			416.034	<.001
Less than matric	−.129	.096	1.806	.179
Matric	.022	.094	.053	.818
Post-matric	−.760	.095	64.084	<.001
Age of respondent			224.663	<.001
18–24 years	.833	.215	15.058	<.001
25–34 years	1.291	.215	36.193	<.001
35–54 years	1.169	.214	29.694	<.001
55+ years	.619	.221	7.873	.005
Race of respondent			91.624	<.001
Black African	.845	.311	7.366	.007
Coloured	1.044	.313	11.141	<.001
Indian or Asian	1.128	.321	12.314	<.001
White	.540	.313	2.983	.084
Gender of respondent			18.496	<.001
Male	.770	.183	17.675	<.001
Female	.726	.183	15.807	<.001
Type of accommodation			339.351	<.001
Township or RDP house	.411	.065	40.270	<.001
Backyard shack/room in a township	.684	.077	78.358	<.001
Informal settlement	.476	.080	35.635	<.001
Suburban house	−.402	.068	35.258	<.001
Flat, apartment, or townhouse	−.080	.071	1.283	.257
Rural area	.420	.074	32.144	<.001

^aB = values in log-odds units. SE = Standard Errors. Wald = Wald chi-square values. Sig. = Significance. Model chi-square 2204.812, Significance > 0.001 with 19 degrees of freedom.

Figure 4. Comparing changes in ‘have difficulty covering my expenses’ in 20 largest loci of respondents.

Figure 5. Proportions that experienced difficulty in covering their expenses, in the 20 municipalities from which the most survey responses (unweighted) were received.

Table 4. Provincial variations - demography, employment, Covid-19, social distress.

PROVINCE	STATISTICS (SOUTH AFRICA, 2020)-2021				NICD STATISTICS 2021			UJ-HSRC SURVEY 2020–2021
	Population density (persons per km^2)	Percent of national population	Percent: Aged 60+ years	Percent: Unemployed Q2–2021	Percent: Confirmed COVID-19 infection	Percent: COVID-19 fatality	Percent: Fatalities of confirmed cases	Percent fearful	Percent: Personal hunger	Percent: Difficulty covering expenses
Gauteng	834,9	26,0%	8,5%	35,4%	5,94%	0,12%	2,05%	38,8%	34,2%	84,1%
KZ-Natal	119,6	19,3%	8,1%	32,5%	4,11%	0,12%	2,85%	42,7%	37,2%	86,3%
W Cape	52,9	11,8%	10,3%	25,8%	6,92%	0,26%	3,75%	40,2%	34,5%	83,3%
E Cape	39,7	11,3%	11,4%	47,1%	3,88%	0,20%	5,21%	37,5%	30,9%	85,0%
Limpopo	47,6	9,8%	8,9%	30,4%	1,99%	0,06%	3,21%	37,8%	31,6%	88,6%

South Africa. (2020). Blog by Khusela Diko, spokesperson of President Ramaphosa. https://www.gov.za/blog/delicate-balancing-act-save-lives-and-protect-livelihoods

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