Plate 9.5: Gas Welders Working along Madzindadzi Road
5.5 Reflections on Natural Advantage: An Insight on Sharing of Tools
A total of 642 manufacturers participated in the sample survey, with a distribution of 29%, 38% and 33% in Gazaland, Siyaso and the Complex home industries respectively. A chi−square independence of association test was collectively used for the three sites to ascertain whether nature of products (products that are an end in themselves or those that can be used as inputs in other processes) produced in home industries depend on whether operators have full toolkits. A collective chi−square calculated value of 2.5432 was compared with a chi−square prescribed value of 3.180 (see Table 5.2). The outcomes suggest that nature of products does not rely on whether an operator boasts a full toolkit or not. It can, therefore, be assumed that even operators without full toolkits can successfully produce goods that serve as an end in themselves. This is a true reflection of spatial interdependence as manufacturers share tools to successfully manufacture goods of different types, finished and semi−finished.
Manufacturers in a similar line of trade use similar tools. As such, it is easy for them to share tools when in adjacent locations.
Interestingly, 65% of manufacturers operate without full toolkits. This percentage is significant because it outweighs that of those with full toolkits, yet they produce both finished and semi−finished goods. Figure 5.5a shows the percentage distribution of manufacturers who own full toolkits and those without full toolkits. More than half (65%) of the doughnut represents the grand percentage of manufacturers without full toolkits. Most of the manufacturers narrated that their manufacturing processes are job−based, that is, they manufacture as per order (just in time) and only stock a few products for display. This enables easy sharing of tools and
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optimises their usage since one manufacturer does not normally use all of them simultaneously.
In the long−run, sharing of tools reduces capital expenditure on individual manufacturers, and thereby dropping the price of production. At the same time, this promotes interaction and networking since they are interdependent.
Do You Have a Full Toolkit? Type of Tools
5.5a
A combination of Hand & Power Tools Hand Tools Power Tools
5.5b
Figure 5.5: Aggregate Facts on Tools Ownership and Type of Tools (Study Findings, 2017)
Regardless of if operators own full toolkits, an investigation into the sort of tools used in manufacturing was conducted. 50% of manufacturers use a mixture of power and hand tools, whereas 42% and 8% use hand tools and power tools only respectively. The doughnut in Figure 5.5b shows this distribution. In an inquisitive attempt to know the association between tool ownership and type of tools used, and whether tool ownership depends on geographic location of study sites, data was collated, analysed and briefed in Table 5.1. These relationships are instrumental in explaining the level of spatial interdependence among the manufacturers.
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Table 5.2: Results on Analysis of Data on Tool Ownership (Study Findings, 2017)
Do you own a full
toolkit ? Nature and type of tools Products
Yes No Combination Hand
tools
Power tools
End products
Input to other processes
Gazaland 107 (26.1%)
82 (35.3%)
108 (33.5%)
58 (21.8%)
23 (42.6%)
135 (26.3%)
54 (42.2%)
Complex
147 (35.9%)
64 (27.6%)
89 (27.6%)
107 (40.2%)
15 (27.8%)
196 (38.1%)
15 (11.7%)
Siyaso 156
(38.0%)
86 (37.1%)
125 (38.8%)
101 (38.0%)
16 (29.6%)
183 (35.6%)
59 (46.1%)
Totals 410
(63.9%)
232 (36.1%)
322 (50.2%)
266 (41.4%)
54 (8.4%)
514 (80.1%)
128 (19.9%) Pearson's Chi-squared test (with Yate’s
continuity correction)
data: location vs toolkit
X-squared = 7.4227, df = 2, p-value = 0.02444
Pearson's Chi-squared test data: type of tools vs toolkit X-squared = 1.1693, df = 2, p-value
= 0.5573
Pearson's Chi-squared test (with Yates' continuity correction)
data: Type of products vs tool Kit
X-squared = 2.5432, df = 1, p- value = 0.1108
It must be observed that for the relationships between geographical location and toolkit ownership, and type of products and toolkit ownership Pearson’s chi−square test was performed for both aggregate and site−specific combinations using 2 × 2 contingency tables.
As such, the Yates continuity correction was applied to account for the inherent upward bias caused by use of 2 × 2 contingency tables in Pearson’s chi−square tests (Thompson, 1988;
Hitchcock, 2009). Since these two tests are based on 2 × 2 contingency tables, they gave one degree of freedom and chi square threshold of 3.841 at 0.05 level of significance. For the association between sort of products and toolkit ownership, any chi square calculated value above 3.841 led to the rejection of the null hypothesis (there is no relationship between the proximity of operators and sharing of toolkits) and vice versa. As indicated in Figure 5.6, only
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Siyaso gave a chi square value of 5.455 that lie in the rejection (blue) region. This means that, for Siyaso, type of products produced rely on whether one owns a full toolkit, whereas for other sites (including aggregate), types of products produced do not depend on whether a manufacturer has a full toolkit. Being an odd result, an inquiry was then made into what explains this anomaly.
Figure 5.6: Chi Square Rejection Criteria (Study Findings, 2017)
It was found out that independence of association test for Siyaso gave results that are contrary to the null hypothesis. Figure 5.7a indicates that manufacturers without full toolkits form clusters of between 2 and 5 manufacturers, and in some instances conglomerate around manufacturers with full toolkits. These outcomes suggest that operators without full toolkits can only manufacture goods to some magnitude and then contribute into production processes of other manufacturers without full toolkits or those that have full toolkits. So the presentation of spatial dependence of manufacturers working in Siyaso is different from that of manufacturers working in Gazaland and Complex. In Siyaso, manufacturers are not reliant on tool sharing, but on feeding one’s output as input in someone’s production process. On the
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contrary, in Gazaland and Complex manufacturers rely on each other’s tools to produce either finished or semi−finished goods. So in both instances there is clear evidence of spatial dependence, irrespective of the variance in its nature.
Do you have a full toolkit? Nature and type of tools used
Yes No 5.7a
Hand Tools Power Tools A Combination of Hand & Power Tools 5.7b
Figure 5.7: Siyaso’s Spatial Distribution of Tool−Ownership and Type of Tools (Study Findings, 2017)
Concerning the sort of tools that manufacturers working in Siyaso use, a significant percentage (52%) make use of a combination of hand and power tools, followed by 42% and 6% who use hand only and power only tools respectively. A test on whether type of tools used depend on whether one owns a full toolkit gave an aggregate chi−square value of 1.1693 which fell below a prescribed chi square value of 2.920 at 0.05 level of significance and 2 degrees of freedom.
This reveals that type of tools used hinge on whether one owns a full toolkit. Half (50.2%) of
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the manufacturers in the three sites use a combination of power and hand tools since most products require use of combined tools when manufacturing. Because of undercapitalisation, 41.4% of the manufacturers do not afford to buy power tools so they use hand tools only.
Figure 5.7 (both a and b) provides evidence of successful growth of Siyaso home industry. This is described by the fact that Siyaso has attracted new manufacturers working outside the sampling window, but dotted around the administrative boundary. This is not peculiar to Siyaso, but common to both the Complex and Gazaland. As such, it might be concluded that the three clusters are organically growing as evidenced by manufacturers dotted around administrative boundaries of clusters. Spatial interdependence of manufacturers within the sampling window and those in and out of the sampling window mainly revolves around tool−use economies of scale. Other than sharing tools, manufacturers also share knowledge on manufacturing at different fronts.