Success indicators among black owned informal Small Micro and Medium Enterprises’ (SMMEs) in South Africa

Figures & data

Figure 1. Johannesburg and Pretoria SMMEs concentration. Source: GCRO (2015).

Table 1. High SMMEs concentrated zones.

	City of Tswane Metro Municipality	City of Johannesburg Metropolitan Municipality
1	Atteridgeville	Johannesburg CBD
2	Pretoria CBD	Soweto
3	Mamelodi	Alexandra
4	Hammanskraal	Midrand
5	Soshanguve	Randburg
6	Marabastat	Roodepoort

Table 2. Distribution of economic activity in Gauteng Province.

Industry	Gauteng province
Manufacturing	40.5%
Wholesale and retail	35.5%
Transport, storage and communication	34.2%
Other services, i.e. hairdressing	23.5%

Source: Statistics South African (2015b).

Table 3. Demographic and descriptive statistics of respondents.

	Male	Female
Gender	201 (51.54%)	189 (48.46%)
	Primary	Metric	Tertiary	Other
Education status	106 (27.18%)	199 (51.03)	70 (17.95%)	15 (3.85%)
	Sole proprietor	Partnership	Family business	No response
Business type	210 (53.85%)	72 (18.46%)	105 (26.92%)	3 (0.77%)
	Manufacturing	Retail and wholesale	Transport	Other services
Sector of operation	159 (40.77%)	86 (22.05%)	82 (21.03%)	63 (16.15%)
	R30 000 and above	R10–30 000	R10 000 and below
Income per month	20 (5.12%)	105 (26.92%)	262 (67.18%)
	Acquired assets	Rent assets	Use backyard
Assets ownership	93 (23.85%)	118 (30.26%)	178 (45.64%)
	Bank	Family	Savings	Other
Capital Source	52 (13.33%)	153 (39.23)	168 (43.08)	17 (4.36%)
	Very high	Good	Very little knowledge
Financial literacy	57 (14.62%)	145 (37.18%)	188 (48.21%)
	Always	Sometimes	Never
Advertising budget	52 (13.33)	128 (32.82%)	210 (53.85%)
	Increased	Remained the same	Decreased
Employee growth	79 (20.26%)	48 (12.31%)	263 (67.44%)
	Yes	Otherwise
Business experience	179 (45.90%)	211 (54.10%)

Source: Survey data (August–September 2018).

Table 4. Ordered logit regression output.

Iteration 0:	log likelihood = −343.91746
Iteration 1:	log likelihood = −262.08585
Iteration 2:	log likelihood = −260.52801
Iteration 3:	log likelihood = −260.52244
Iteration 4:	log likelihood = −260.52244
Ordered logistic regression	Number of obs – 323
	LR χ^2 (14) – 166.79
	Prob > χ^2 – 0.0000
Log likelihood – −260.5224	Pseudo R^2 – 0.2425
Assets ownership (AO)	Odds ratio	Std. Err	Z	P>|z|	95% Conf.	Interval
Edu	0.6612	0.1245	2.20	0.028**	0.4573	0.9559
Bustyp	0.8217	0.1217	−1.33	0.185	0.6147	1.0984
Sect	0.9571	0.1182	−0.36	0.723	0.7513	1.2192
Income	0.4374	0.1106	3.27	0.001***	0.2664	0.7181
Emplgrow	1.5708	0.3219	2.20	0.028**	1.0512	2.3471
Profgrow	0.8418	0.1593	−0.91	0.363	0.5810	1.2197
Bizbranch	1.1086	0.2306	0.50	0.620	0.7374	1.6667
Opcentre	0.6912	0.1528	1.66	0.097*	0.4509	1.0685
Capital source	0.8375	0.1379	−1.08	0.281	0.6067	1.1561
Financial	0.3661	0.0923	−3.98	0.000***	0.2234	0.6002
Experience	0.4785	0.1223	2.88	0.004***	0.2900	0.7898
Finlit	3.7111	0.8664	5.62	0.000***	2.3485	5.8646
Advert	1.9500	0.3340	3.90	0.000***	1.3442	2.7274
Bisnet	0.8156	0.2151	−0.77	0.440	0.4864	1.3675
/cut1	0.1420	1.2787			−2.3641	2.6481
/cut2	1.9899	1.2815			−0.5218	4.5017

Source: Estimation output: * 10%, ** 5% and *** 1% significance level.

Table 5. Ramsey RESET test using powers of fitted values.

H0: model has no omitted variables
F(3,312)	= 2.63
Prob > F	= 0.0604

Table 6. Goodness of fit for multivariate.

	χ^2	df	p-value
Pearson	224.543	212	0.264
Deviance	130.022	212	1.000

Table 7. Wald tests for multivariate case.

χ^2 (15)	= 115.98
Prob > χ^2	= 0.0000

GCRO, 2015. Quality of life survey IV (2015): Health. GCRO data brief No. 9. December 2018.

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Statistics South African, 2015b. The statistics bulletin. Pretoria, South Africa. http://www.statsa.co.za.

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