Evaluation of the efficiency in delivery of government services to ameliorate the COVID-19 pandemic in the King Cetshwayo District in South Africa: Recipients’ perspectives

Figures & data

Table 1. Description of the study variables

Variable	Description	Variable type	Categories
$\mathit{\text{Gvnt}}\mathrm{\_}\mathit{\text{ineff}}$	Government inefficiency in service delivery of COVID-19 related social security support	Categorical variable derived from four questions that asked respondents’ perceptions about government efficiency in delivering COVID-19 social security support – Dependent	Strongly disagree disagree Neutral Agree Strongly agree
$\mathit{\text{Age}}\mathrm{\_}h$	Age of the household head	Categorical -independent	≤18 years 19–30 years 31–40 years 41–50 years 50 years <
$\mathit{\text{edu}}\mathrm{\_}h$	Education level of household head	Categorical – independent	None Some Pry school Completed Pry school Completed High school Post school -certificate/Diploma/Degree Postgraduate
$\mathit{\text{emp}}\mathrm{\_}h$	Employment status of household head	Categorical – independent	Full time employed Part-time employed Self employed Unemployed
$\mathit{\text{size}}\mathrm{\_}\mathit{\text{hse}}$	Size of the household	Count – independent
$\mathit{\text{income}}\mathrm{\_}\mathit{\text{hse}}$	Income of the household	Categorical – independent	R0-R2000; R2001-R3500; R3501-R5000; R5001-R10000; R10001–20000; 20001 and above
$\mathit{\text{meals}}\mathrm{\_}d$	Number of meals per day	Count – independent
$\mathit{\text{default}}\mathrm{\_}c$	Whether the household head has defaulted on any credit in the previous year.	Binary – independent	Yes/No
$\mathit{\text{grants}}\mathrm{\_}\mathit{\text{inc}}$	Whether social grants should be increased or not	Binary – independent	Yes/No
$\mathit{\text{Government}}\mathrm{\_}\mathit{\text{intervention}}$	Whether any member of the household received social relief of distress grant (COVID-19 grant), unemployment insurance fund, any tax cut or tax deference, government guaranteed bank loan or any other government assistance.	Categorical – independent	Normal social grants (child/old age/disability) COVID-19 grant/Social relief of distress grant Unemployment insurance Tax relief Government guaranteed Bank loan Other
$\mathit{\text{Location}}$	Location of the household within the King Cetshwayo District municipality	Independent	See Table 3

Table 2. Cronbach's alpha

Average interim covariance	0.185
Number of items on scale	11
Scale reliability coefficient	0.65

Table 3. Government’s management of the pandemic – pandemic was well managed

	Government Efficiently Managed the pandemic
Location/Place	Strongly disagree	Disagree	Neutral	Agree	Strongly Agree	Total
Esikhawin	65	38	15	10	2	130
	50.00	29.23	11.54	7.69	1.54	100.00
	20.19	18.01	10.14	6.67	8.33	15.20
Nkandla	14	11	35	50	7	117
	11.97	9.40	29.91	42.74	5.98	100.00
	4.35	5.21	23.65	33.33	29.17	13.68
Melmoth	42	31	43	30	7	153
	27.45	20.2628.	10	19.61	4.58	100.00
	13.04	14.69	29.05	20.00	29.17	17.89
Eshowe	89	47	7	2	1	146
	60.96	32.19	4.79	1.37	0.68	100.00
	27.64	22.27	4.73	1.33	4.17	17.08
Mbonambi	17	18	5	16	2	58
	29.31	31.03	8.62	27.59	3.45	100.00
	5.28	8.53	3.38	10.67	8.33	6.78
Empangeni	61	25	16	6	1	109
	55.96	22.94	14.68	5.50	0.92	100.00
	18.94	11.85	10.81	4.00	4.17	12.75
Richards bay	3	13	4	0	0	20
	15.00	65.00	20.00	0.00	0.00	100.00
	0.93	6.16	2.70	0.00	0.00	2.34
Dlangezwa	31	28	23	36	4	122
	25.41	22.95	18.85	29.51	3.28	100.00
	9.63	13.27	15.54	24.00	16.67	14.27
Total	322	211	148	150	24	855
	37.66	24.68	17.31	17.54	2.81	100.00
	100.00	100.00	100.00	100.00	100.00	100.00

Pearson chi² (28) = 249.0658 Pr = 0.000. First row has frequencies, and second row depicts row percentages, and third row shows column percentages.

Figure 1. Government’s management of the pandemic.

Source: Authors’ Computation.

Figure 2. Management of the pandemic’s economic consequences.

Source: Authors’ Computation.

Table 4. Ordinal regression coefficients – determinants of government inefficiency perceptions

		Model 1	Model 2	Model 3
Dependent variable: $\mathit{G}\mathit{\text{vnt}}\mathrm{\_}\mathit{\text{ineff}}$
$\mathit{\text{age}}\mathrm{\_}\mathit{\text{head}}$		0.4022*** [0.743]	0.3937*** [0.083]	0.2048** [0.088]
$\mathit{race}\mathrm{\_}\mathit{h}$
Indian		0.8783** [0.390]	0.9939** [0.456]	0.8650* [0.467]
Coloured		0.5431 [0.426]	0.2258 [0.458]	0.3953 [0.464]
White		−0.1803 [0.519661]	0.1394 [0.638]	0.0068 [0.650]
$\mathit{\text{edu}}\mathrm{\_}h$		0.0093 [0.058]	−.0946762 [0.0655]	−0.0870 [0.069]
$\mathit{\text{emp}}\mathrm{\_}h$		−.14542*** [0.062]	−0.2293*** [0.073]	−0.1799** [0.076]
$\mathit{\text{size}}\mathrm{\_}\mathit{\text{hse}}$		−0.0093 [0.020]	−0.0160 [0.022]	−0.0162 [0.022]
$\mathit{\text{income}}\mathrm{\_}\mathit{\text{hse}}$		−0.1243*** [0.044]	−0.0471 [0.051]	0.0817 [0.055]
$\mathit{\text{meals}}\mathrm{\_}d$		−0.0673 [0.091]	−0.1117 [0.102]	−0.1605 [0.108]
$\mathit{\text{default}}\mathrm{\_}c$		0.4772*** [0.159]	0.6371*** [0.178]	0.0917 [0.193]
$\mathit{\text{grants}}\mathrm{\_}\mathit{\text{inc}}$			0.6571*** [0.181]	0.2534 [0.196]
$\mathit{Government}\mathrm{\_}\mathit{intervention}$
$\mathit{\text{SRDgrant}}$			−0.3035* [0.175]	−0.3486* [0.181]
$\mathit{\text{UIF}}$			0.9332** [0.378]	0.8637** [0.388]
$\mathit{\text{Taxrelief}}$			−0.0782 [0.242]	0.4916* [0.260]
$\mathit{\text{Bankloan}}$			0.4318 [0.398]	0.7169* [0.419]
$\mathit{\text{Other}}$			−0.7961** [0.308]	−0.1755 [0.338]
$\mathit{location}$
$\mathit{\text{Nkandla}}$				2.1656*** [0.290]
$\mathit{\text{Melmoth}}$				1.4187*** [0.270]
$\mathit{\text{Eshowe}}$				−0.7430*** [0.271]
$\mathit{\text{Mbonambi}}$				1.3454*** [0.353]
$\mathit{\text{Empangeni}}$				0.1463 [0.339]
$\mathit{\text{Richardsbay}}$				0.8842* [0.463]
$\mathit{\text{Dlangezwa}}$				1.5960*** [0.302]
Number of observations		855	689	689
Log Likelihood		−1024.91	−801.32	−738.78
LR-chi2		50.81 (0.000)	76.50 (0.000)	201.58 (0.000)
Pseudo R2		0.024	0.045	0.120
Wolfe & Gould Prop of Odds (chi2)		22.01 (0.578)	30.17 (0.067)	32.47 (0.070)

***, **, * means significant at 1%, 5%, and 10% respectively. Numbers in parentheses [] are standard error and () are p-values.

Table 5. Ordinal regression odds-ratios - determinants of government inefficiency perceptions

	Model 1	Model 2	Model 3
Dependent variable: $\mathit{G}\mathit{\text{vnt}}\mathrm{\_}\mathit{\text{ineff}}$
$\mathit{\text{age}}\mathrm{\_}\mathit{\text{head}}$	1.49*** [0.111]	1.48*** [0.123]	1.22** [0.108]
$\mathit{race}\mathrm{\_}\mathit{h}$
$\mathit{\text{Indian}}$	2.40** [0.940]	2.70** [1.234]	2.37* [1.11]
$\mathit{\text{Coloured}}$	1.72 [0.734]	1.25 [0.574]	1.48 [0.689]
$\mathit{\text{White}}$	0.83 [0.433]	1.14 [0.734]	1.00 [0.655]
$\mathit{\text{edu}}\mathrm{\_}\mathit{\text{head}}$	1.00 [0.058]	0.90 [0.059]	0.91 [0.063]
$\mathit{\text{emp}}\mathrm{\_}\mathit{\text{head}}$	0.86** [0.054]	0.79*** [0.058]	0.83** [0.063]
$\mathit{\text{size}}\mathrm{\_}\mathit{\text{hsehold}}$	0.99 [0.020]	0.98 [0.021]	0.98 [0.021]
$\mathit{\text{income}}\mathrm{\_}\mathit{\text{hsehold}}$	0.88*** [0.038]	0.95 [0.049]	1.08 [0.059]
$\mathit{\text{meals}}\mathrm{\_}\mathit{\text{day}}$	0.93 [0.085]	0.89 [0.091]	0.85 [0.092]
$\mathit{\text{default}}\mathrm{\_}\mathit{\text{credit}}$	1.61*** [0.256]	1.89*** [0.338]	1.09 [0.212]
$\mathit{\text{grants}}\mathrm{\_}\mathit{\text{inc}}$		1.92*** [0.350]	1.28 [0.253]
$\mathit{Government}\mathrm{\_}\mathit{intervention}$
$\mathit{\text{SRDgrant}}$		0.73* [0.129]	0.70* [0.128]
$\mathit{\text{UIF}}$		2.54** [0.962]	2.37** [0.921]
$\mathit{\text{Taxrelief}}$		0.92 [0.223]	1.63* [0.426]
$\mathit{\text{Bankloan}}$		1.54 [0.613]	2.04* [0.858]
$\mathit{\text{Other}}$		0.45** [0.139]	0.83 [0.284]
$\mathit{location}$
$\mathit{\text{Nkandla}}$			8.72*** [2.537]
$\mathit{\text{Melmoth}}$			4.13*** [1.115]
$\mathit{\text{Eshowe}}$			0.47*** [0.129]
$\mathit{\text{Mbonambi}}$			3.83*** [1.358]
$\mathit{\text{Empangeni}}$			1.15 [0.393]
$\mathit{\text{Richardsbay}}$			2.42* [1.123]
$\mathit{\text{Dlangezwa}}$			4.93*** [1.491]
Number of observations	855	689	689
Log Likelihood	−1024.91	−801.32	−738.78
LR-chi2	50.81 (0.000)	76.50 (0.000)	201.58 (0.000)
Pseudo R2	0.024	0.045	0.120
Wolfe & Gould – Approx Prop	22.01 (0.578)	30.17 (0.067)	32.47 (0.070)

***, **, * means significant at 1%, 5%, and 10% respectively. Numbers in parentheses [] are standard error and () are p-values.

Table

Coefficients	Model 1	Model 2	Model 3
Dependent variable:
$\mathit{\text{Gvnt}}\mathrm{\_}\mathit{\text{ineff}}$
$\mathit{\text{age}}\mathrm{\_}\mathit{\text{head}}$	0.5075*** [0.110]	0.5204*** [0.113]	0.3746*** [0.122]
$\mathit{race}\mathrm{\_}\mathit{h}$
Indian	1.3060** [0.529]	1.0004* [0.533]	0.7453 [0.540]
Coloured	0.2510 [0.693]	−0.0897 [0.668]	0.4091 [0.682]
White	0.0555 [0.902]	0.5101 [0.914]	−0.4899 [0.965]
$\mathit{\text{edu}}\mathrm{\_}h$	−0.0093 [0.082]	−0.0749 [0.084]	−0.0914 [0.089]
$\mathit{\text{emp}}\mathrm{\_}h$	−0.1943*** [0.091]	−0.2004** [0.094]	−0.2450** [0.098]
$\mathit{\text{size}}\mathrm{\_}\mathit{\text{hse}}$	0.0169 [0.033]	−0.0094 [0.034]	−0.0132 [0.036]
$\mathit{\text{income}}\mathrm{\_}\mathit{\text{hse}}$	−0.0886 [0.063]	−0.0638 [0.065]	0.0665 [0.070]
$\mathit{\text{meals}}\mathrm{\_}d$	−0.1732 [0.136]	−0.1912 [0.139]	−0.2404 [0.148]
$\mathit{\text{default}}\mathrm{\_}c$	0.6074*** [0.252]	0.7604*** [0.263]	0.3219 [0.289]
$\mathit{\text{grants}}\mathrm{\_}\mathit{\text{inc}}$		0.5525*** [0.241]	0.2723 [0.263]
$\mathit{Government}\mathrm{\_}\mathit{intervention}$
$\mathit{\text{SRDgrant}}$		−0.1452 [0.231]	−0.1937 [0.245]
$\mathit{\text{UIF}}$		1.4401*** [0.483]	1.1890** [0.521]
$\mathit{\text{Taxrelief}}$		0.0925 [0.323]	0.5549* [0.349]
$\mathit{\text{Bankloan}}$		0.9328* [0.482]	1.1018* [0.510]
$\mathit{\text{Other}}$		−0.4978 [0.403]	−0.0045 [0.445]
$\mathit{location}$
$\mathit{\text{Nkandla}}$			2.3680*** [0.390]
$\mathit{\text{Melmoth}}$			1.5623*** [0.398]
$\mathit{\text{Eshowe}}$			−0.8421** [0.362]
$\mathit{\text{Mbonambi}}$			1.1744** [0.489]
$\mathit{\text{Empangeni}}$			0.2302 [0.399]
$\mathit{\text{Richardsbay}}$			1.2023** [0.606]
$\mathit{\text{Dlangezwa}}$			1.4270*** [0.419]
Wolfe & Gould	19.02 (0.268)	24.05 (0.240)	24.30 (0.332)
Likelihood ratio	20.99(0.179)	25.81(0.172	25.91(0.256)
Wald	21.89(0.147)	25.71(0.176	25.92(0.256)
Number of observations	386	386	386
Pseudo R^2	0.039	0.065	0.151