QUANTITATIVE DATA
5.3 General SME and Biographical Information
This section discusses biographical and demographic information of the participants and informal manufacturing SMEs with respect to gender, age, marital status, education, the category of the informal manufacturing sector, number of employees, years of operation, designation in the business, whether the business is an exporter or a non-exporter and level/value of annual sales since some studies on informal SMEs have shown these demographic characteristics have a significant influence on some of the challenges faced by these informal manufacturing SMEs.
5.3.1 Gender of the Respondents
In this study, respondents were given a question to indicate their gender. This was done for a dual purpose, firstly, in the spirit of finding out whether there is gender balance in the informal manufacturing categories being investigated and secondly to establish, the relationship between gender and informal manufacturing SMEs challenges. Figure 5-2 shows the gender of the respective respondents in the study.
Figure 5-2: Gender of the Respondents
Male Female
44.5%
The figure shows that 457 (55.5%) male informal manufacturing SMEs owners were involved in the study while 366 (44.5%) were female. This sample confirms that there are more males involved in the informal manufacturing SMEs than females. This sample finding is further supported by Mahadea (2001:197), who based on economic dimensions, concluded that males tend to engage in more value addition manufacturing sectors and have more employees than their female counterparts.
5.3.2 Categories of the Age Group of Respondents
The respondents were asked a question to determine their specific personal age group. This was required to establish the influence of age, on the challenges faced by the informal SMEs. The age distribution of the respondents was as follows: 16 - 25 (176 or 21.4%), 26 - 35 (252 or 30.6%), 36 - 45 (245 or 29.8%), 46 - 55 (110 or 13.4%), 56 - 65 (32 or 3.9%) and 66 + (8 or 1%) as shown in Figure 5-3.
Figure 5-3: Categories of the Age Group of Respondents.
Source: Own, 2017
The sample of the respondents revealed that the largest age group category is the 36-45 (245) accounting for 29.8% of the sample and the smallest age group was the 66+ accounting for 1% of the sample. In this sample, it is evident that the 26-45 years indicates a great participation of the economically active population in the informal manufacturing sector with 29.8%. This is similar to a study carried out by Ndiweni, Mashonganyika, Ncube and Dube (2014:5), that also showed
,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0
16-25 26-35 36-45 46-55 56-65 66+
Age 21,4
30,6 29,8
13,4
3,9
1,0
Percentage
40% of the participants and this is, the case in the results above with the 26 to 35 and 36-45 constituting 60.4%.
5.3.3 Marital Status of Respondents
The respondents were asked about their marital status. Five (5) categories of marital status were established. The five (5) categories were single, married, in partnership, divorced and widowed.
The information was important to make a comparison of married respondents, with divorced and unmarried.
Figure 5-4: Marital Status of Respondents
Source: Own, 2017
Figure 5-4 above shows, that the majority of the respondents (438) 53.2% were married while (181) 22% were single, (124) 15.1% were in partnership, (50) 6.1% were divorced and (30) 3.6%
Single 22%
Married 53.2%
In partnership 15.1%
Divorced 6.1%
Widowed 3.6%
5.3.4 Educational Level of the Respondents
The respondents were asked a question to indicate their highest educational qualification to assess the informal SMEs operators’ education the relationship to the challenges facing those SMEs as well. Eight (8) broad categories of educational qualification, were used to determine the educational levels. These were high school, certificate, diploma, degree, honours, masters and doctorate. The observation for the sample was that the largest sample respondents had diplomas (256) 31.1%, followed by certificates (231) 28.1%, high school (201) 24.4%, degree (114) 13.9%, honours (15) 1.8% and masters (6) 0.7% as shown in Figure 5-5.
Figure 5-5: Educational Level of the Respondents
Source: Own, 2017
The findings for this sample, confirm that there was no doctorate holder involved in the informal manufacturing sector and the majority of the respondents had diplomas 31.1%. Contrary to the sample results above, a study by Gemini (1998), confirms that most informal SMEs owners in Zimbabwe had some secondary education with the majority having completed their high school education while post- high school education accounted for a relatively smaller proportion of the respondents.
,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0
High school Certificate Diplom Degree Honours Masters Education
24,4
28,1
31,1
13,9
1,8 ,7
Percentage
5.3.5 Categories of the Informal Manufacturing SMEs
All sample respondents were asked a question to indicate the category of the informal manufacturing their business was operating in. This was necessary to establish a relationship between gender, educational level and the informal manufacturing SMEs sub-sectors in the study.
In this sample, results indicate that the largest participants (228) 27.7% are in the food, bakery and confectionery processing followed by engineering/foundry and metal fabrication (169) 20.5%, textile and garment making 146 (17.7%), toiletry making (131) 15.9%, leather and rubber making 76 (9.2%) and timber and furniture making (73) 8.9% as shown in Figure 5-6.
Figure 5-6: Categories of the Informal Manufacturing SMEs
Source: Own, 2017
5.3.6 Number of Employees in the Informal SMEs
,0 5,0 10,0 15,0 20,0 25,0 30,0
Food, Bakery and confectionary
processing
Toiletry Making
Textile and garment
making
Leather and rubber making
Engineering /Foundry and
metal fabrication
Timber and furniture
making Sector
27,7
15,9 17,7
9,2
20,5
Percentage 8,9
out that the number of workers employed by the SMEs signifies the important role of the small- to-medium enterprises in the economy.
Figure 5-7: Number of Employees in the Informal SMEs
Source: Own, 2017
5.3.7 Number of Years of Operation in the Informal SMEs
All sample participants were asked questions about the number of years there have been operating.
Sample findings, indicates that 33.4% of the informal manufacturing SMEs were between 1-5 years old, 33.9% were between 6-10 years, 19.9% were between 11-15 years, 8.4% were between 16-20 years, 1.7% were between 21-25 years, 1.5% were between 26-30 years, 0.6% were between 31-35 years, 0.4% were between 36-40 years, 0.1% were between 41-45 years, 0.1%
were between 46-50 years. The results are presented in Figure 5-8.
,0 10,0 20,0 30,0 40,0 50,0 60,0
1-10 11-20 21-30 31-40 41-50 51-60 61+
Number of employees 52,4
25,9
13,4
4,4 2,3 1,0 ,7
Percentage
Figure 5-8 Number of years of Operation in the Informal Manufacturing SMEs
Source: Own, 2017
5.3.8 Designation of the Respondents of Informal SMEs Represented
The respondents were asked a question to ascertain the designation or position within the business.
This was important to establish whether there is a relationship between position and the challenges faced by the informal manufacturing SMEs. The sample results confirm that the designation of the sample respondents were owners (371) 45.1%, management (242) 29.4%, non-management (60) 7.3%, both owner and manager (150) 18.2%. The sample results revealed that the greatest respondents were owners 371 (45.1%) and the smallest percentage of respondents was non- management (60) 7.3% as shown in Figure 5-9.
,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0
1-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 Years operating
33,4 33,9
19,9
8,4
1,7 1,5 ,6 ,4 ,1 ,1
Percentage
Figure 5-9: Designation of the Respondents of the Informal SMEs Represented
Source: Own, 2017
In this sample, it is evident that there is great involvement of the owners of the informal manufacturing with 45.1%. This is an indication that once the informal manufacturing SMEs has been established they have the limited drive to expand and their growth would be constrained by the imperative to accommodate more employees over and above the owners / founders.
5.3.9 Status of the Informal SMEs in Terms of Exporting
Sample interviewees were asked a question on whether there are exporters of their products or not. This question sought to establish the number of informal manufacturing SMEs that are exporting their commodities to other countries. Sample findings indicate that (396) 48.1% of the respondents are exporters while (427) 51.9% are not exporters as shown in Figure 5-10.
Figure 5-10: Status of the Informal SMEs in Terms of Exporting ,0
5,0 10,0 15,0 20,0 25,0 30,0 35,0 40,0 45,0 50,0
Owner Management Non-management Both owner and manager Designation in the business
45,1
29,4
7,3
18,2
Percentage
Yes - 48.1%
No - 51.9%
In this sample, 48.1% of all the firms are exporting their commodities as individuals to other countries. This shows that the informal manufacturing SMEs in Zimbabwe, are starting to see the incentive to export their products to other countries to boost their total revenue.
5.3.10 Value of the Informal SMEs’ Annual Sales/Revenue
Respondents were asked a question about the value of the informal SMEs’ annual sales revenue.
The rationale for that question was to ascertain the value of annual sales levels of the informal manufacturing SMEs in Zimbabwe. The respective sample outcomes for the administered sample are summarised in Figure 5-11.
Figure 5-11: Value of the Informal SMEs’ Annual Sales / Revenue
Source: Own, 2017 ,0 5,0 10,0 15,0 20,0 25,0 30,0 35,0
Up to US$10000 US$10001 –US$20000 US$20001 –US$30000 US$30001 –US$40000 US$40001 –US$50000 US$50001 –US$60000 US$60001 –US$70000 US$70001 –US$80000 US$80001 –US$90000 US$90001 –US$100 000 >US$100 000
Annual revenue 27,3
31,0 26,7
7,9
3,8 1,5 ,7 ,1 ,2 ,4 ,4
Percentage
revenue value between US$90 001.00-US$100 000.00 and 3 (0.4%) have US$100 000+ annual revenue value.
Bowale and Ilesanmi (2014:140) in a similar study, concluded that business size, the category of business, sources of seed capital are significant variables that determines both employment generation and revenue generation potential of the SMEs while the age of business and level of education were important factors determining the capacity of SMEs to create employment.