Paul Krugman, winner of the 2009 Nobel Prize in Economics, and his collaborators (Fujita and Venables) remarked that³:

It should not, in other words, be hard to convince economists that economic geography – the study of where economic activity takes place and why – is both an interesting and important subject. Yet until a few years ago it was a subject mainstream economics largely neglected.

Fujita et al.³ added that ‘new trade’ and ‘new growth’ theories have been developed that have provided a core of useful new insights. Romer, for example, has promoted the concept that it is ideas, not objects, which poor countries and regions lack.⁴

Fujita et al.⁵ pursued two concepts, (1) in a world where increasing returns and transport costs are both important, forward and backward linkages can create a circular logic of agglomeration – producers want to be located close to their suppliers and customers; and (2) the immobility of some resources, for example, land, and in some cases labour, acts as a centrifugal force that opposes the centripetal force of agglomeration. The tension between centrifugal and centripetal forces shapes the evolution of an economy’s spatial structure. Fujita et al.⁵ suggested that their approach should be buttressed amongst others by empirical work; a suggestion acted upon in this investigation.

Florida examined the rise of the ‘creative class’ in the USA.⁶ He sketched the importance of three ‘Ts’ – technology, talent and tolerance – for urban and enterprise development. Creative people, the drivers of the ‘knowledge economy’, do not necessarily settle where jobs are but strive to settle in places that are open and tolerant, and where many different types of people feel at home. Knowledge era enterprises now have to follow the creative talents they need and not vice versa.

Beinhocker elaborated on Complexity Economics,⁷ which views economies as complex adaptive systems subject to the Second Law of Thermodynamics. Economies consist of realistically rational agents that interact dynamically with each other in essentially evolutionary systems. He stated that:

Economic wealth and biological wealth are thermodynamically the same sort of phenomena, and not just metaphorically. Both are systems of locally low entropy, patterns of order that evolved over time under the constraint of fitness functions. Both are forms of fit order.

Each individual enterprise, like each living organism, is therefore in constant competition for survival and only the fittest survive. Therefore, at any point in time, the size and composition of enterprise assemblages in an economy present a time-integrated picture of the forces that drove that economy over previous periods.

Toerien and Seaman⁸ extended the ideas of Beinhocker⁷ by examining the hypothesis that if enterprises are analogous to living organisms in the sense that they are both forms of fit order, towns must be analogous to natural ecosystems, that is, they are enterprise ecosystems. They accepted this hypothesis on the basis of a study that applied methods widely used in ecological studies to the enterprise assemblages of towns in the Great Karoo in South Africa.

Toerien and Seaman⁹ also successfully applied the Species Equilibrium Model of island biogeography¹⁰ in an examination of the enterprise dynamics of 12 Karoo towns in the Eastern Cape. The ecological model predicts that the rate of new immigrant species arriving, the rate of species extinction and the rate of speciation on an island will reach an equilibrium, where the number of species on the island will be a function of the size of the island and its isolation from a mainland (the source of new species). Toerien and Seaman⁹ found that enterprises in the selected Karoo towns behaved like species on natural islands, exhibiting statistically significant correlations (in other words proportionalities) between enterprise numbers and the size of the town. They concluded that the towns are ‘enterprise islands’, in addition to being enterprise ecosystems.

The primary purpose of this contribution was to examine proportionalities in enterprise development in a large number of South African towns. The implications of such proportionalities for local economic development (LED) planning were secondarily considered. The South African government has for more than a decade struggled with the recalcitrant issues of rural poverty and unemployment,¹¹ particularly with regard to previously disadvantaged individuals. Developing a deeper understanding of the enterprise dynamics of South African towns, particularly about enterprise proportionalities, is an imperative in LED planning.

The above implies that there must be proportionalities amongst different enterprise types. For example Van der Merwe and Nel¹ provided the following thresholds for a town: grocers = 1719, general dealers = 2129, butchers = 5770 and pharmacies = 12 661. It follows that for every pharmacy in a town there should have been approximately seven grocers, six general dealers and two butchers. They did not, however, explicitly study this kind of proportionality. If such proportionality still exists, useful norms could be developed against which the enterprise evolution of specific towns can be assessed.

Christaller’s central place theory has long dominated thinking about towns and their functions in South Africa.¹ For instance, Davies and Cook² employed 55 central functions to classify 601 South African urban settlements into eight levels of hierarchy. Nel and Hill¹² presented a hierarchy of Karoo towns with specific hierarchy levels defined as the functions of lower levels plus some additional ones. For instance, the level 5 functions of Karoo towns were defined as levels 6, 7 and 8 functions plus additional functions providing services with regard to roads, libraries, schools, public administration, financial management, wholesales and cinemas.¹² Implicit in the definition of these hierarchies is an acceptance of the fact that towns at higher ranks in the hierarchy would have more types of service providers than towns of lower ranks and, thus, proportionally more of the total number of services provided in all towns. The latter kind of proportionality has been neither formally quantified nor used as a concept to understand the enterprise dynamics of South African towns.

Beinhocker⁷ pointed out that the economy, and therefore all enterprises, are subject to the Second Law of Thermodynamics (the law of entropy). Products and services having a higher level of order than their constituent parts can only be produced or delivered by an increase in disorder (i.e. higher entropy, e.g. pollution) elsewhere. Energy inputs are needed to counter the ravages of entropy but energy costs money. Accordingly, the flow of money from clients who purchase the products and/or services must, (1) recompense businesses for their expenses, including the cost of energy and materials, wages, and costs in dealing with waste and pollution; (2) provide their profits; and (3) enable them to excel (or fail) in the ‘survival-of-the-fittest’ competition.

The total amount of money from whatever source (farming, wages, welfare payments, pensions and profits) that is available to be spent on products and services in a specific town, determines the number of the town’s enterprises.⁸ The composition of the needs of the buyers of products and services (clients) of the town’s enterprises determines the composition of its enterprise structure. Once the number of clients for the products or services delivered by a particular kind of enterprise exceeds the threshold numbers for such enterprises, ‘entrepreneurial space’ opens up. Conversely, if the number of clients falls below particular threshold numbers, entrepreneurial space retracts.

Toerien and Seaman⁹ showed that Eastern Cape Karoo towns can be considered to be ‘islands’ in a ‘sea’ of farms and exhibit typical ‘island effects’ recorded in island biogeography.¹⁰ Foremost in this regard were enterprise equilibriums where the rate at which new enterprises appeared, and the rate at which existing enterprises disappeared from towns, evened out. This balance resulted in enterprise numbers that were proportional to the size of the Karoo towns. Larger towns were able to house a higher number and more types of enterprises than smaller towns because their entrepreneurial spaces were larger.⁹

1. The magnitude of enterprise development in South African towns should be a function of the population sizes of these towns. The null hypothesis is that there is no statistically significant relationship between the population size of a town and the size of its enterprise assemblages.

2. Because factors other than agriculture (e.g. mining) have contributed to the development of specific towns, the size of enterprise assemblages of towns is not a function of town age. The null hypothesis is that, in general, there is no statistically significant correlation between the number of enterprises in a town and the age of a town.

3. People have proportional needs for different products and/or services (e.g. for food, liquor, furniture and clothes).¹ Therefore, if the first hypothesis is correct, there should also be proportionalities (measured as statistically significant correlations) between the number of enterprises of different business sectors and the total number of enterprises in a town. The null hypothesis is an absence of such statistically significant correlations.

4. Proportionalities, if they exist, could have far-reaching implications for rural development and job creation. The null hypothesis is that there are few, if any, useful sector proportionalities to assist in LED planning and execution.

A subgroup of towns was used because formal population numbers for towns are not readily available. The exercise was complicated by a number of issues, (1) the resident numbers for South African towns are not readily available from the most recent formal census data because the latter was focused on local authorities that often include more than one town; (2) little separation is made between urban and rural (hinterland) data; and (3) the last census was done in 2001, nearly a decade ago. Consequently, estimates of town populations for 2006 reported by Hans Fransen, a well-known town planner, in his book on old Cape towns¹⁴ were used for towns that were included both in Fransen’s book and in the database on which this contribution is based (n = 35).

There was a strong proportionality between population numbers per town and the number of enterprises per town as shown by a highly significant correlation coefficient (r = 0.92, p < 0.01; Figure 1). The first hypothesis was therefore accepted, a conclusion confirmed by an additional calculation of the correlation coefficient between the 2004 urban population numbers presented by Nel and Hill¹² for 10 Eastern Cape towns (Cradock, Aberdeen, Jansenville, Steytlerville, Graaff-Reinet, Middelburg, Willowmore, Hofmeyr, Steynsburg and Venterstad) and enterprise numbers recorded for these towns in this study (Table 1). A statistically significant (p < 0.01) relationship with r = 0.99 was recorded:

FIGURE 1: The relationship between the number of residents in a town and the total number of enterprises in the town.

The correlation coefficient (r = 0.057, n = 124) was not statistically significant (p > 0.05); consequently the null hypothesis was accepted. Town age is therefore not an important general factor in determining the size of enterprise assemblages of South African towns, except for towns with similar origins.⁹

Because the magnitude of the enterprise assemblages of towns varied from 8 for a small village such as Norvalspont, to more than 1800 for a town such as Welkom, care had to be exercised to prevent the use of spurious correlations (and spurious interpretations) resulting from the size differentials of enterprise structures of towns. Knowing that smaller towns have fewer service functions¹ it was also necessary to ask if observed proportionalities extended over the whole range of enterprise assemblages in the selected town group. To examine these questions the selected group of towns was divided into three approximately equally sized subgroups: 40 large (> 130 enterprises per town), 38 medium (between 50 and 130 enterprises per town) and 47 small (fewer than 50 enterprises per town) towns. Just as for the total group of towns, Pearson correlation coefficients and regression equations were calculated and tested for each of the subgroups.

Significant correlations (p < 0.05) were registered between the total number of enterprises per town (independent variable) and the number of sector enterprises of all 19 different business sectors (dependent variables) for the total town group (Tables 3, 4 and 5). Some of the regression equations explained almost all of the variance in sector enterprise numbers and others only a limited amount. Strong proportionalities were observed within the business sectors of large towns whilst fewer proportionalities were observed in smaller towns.

Many South African towns were founded to meet the spiritual needs of farmers and their families who had settled in inland areas (the so-called ‘church towns’¹⁴) but soon also catered for the commercial needs of farmers. The majority of South African towns, therefore, started with strong initial links to agriculture. Thus, one would expect that the enterprise structures of especially smaller rural towns should be strongly influenced by the business sector that caters for the agricultural client (i.e. the agricultural products and services sector). Although the data reveal statistically significant correlations and thus proportionality (Table 3), only limited amounts of the variances in enterprise numbers of this sector are explained by the regression equations (Table 3). Some small towns are very strong in this sector, others are very weak and the historically strong link to agriculture now appears to be tenuous. These findings support the contention that hinterlands are now less important than they were in the past for most rural towns.¹² Agriculture should not be readily positioned as the ‘saviour economic activity’ for rural local authorities struggling with LED; yet, this approach is precisely the chosen rural development strategy of the provincial government of the Western Cape.¹⁶

Tourism has been promoted as an economic activity that can contribute significantly to rural economic development in South Africa.¹⁷ The number of tourism and hospitality sector enterprises in towns was significantly correlated with the total number of enterprises per town, but only 58% of the variance in the enterprise numbers of this sector was explained by the regression equation (Table 6). Larger towns are nevertheless likely to have reasonably strong tourism and hospitality sectors. In the medium and small town groups much less of the variance in the sector was explained by the regression equations (Table 6). Some towns in these groups have strong tourism and hospitality sectors but others have very weak tourism and hospitality sectors. Successful positioning of towns in the tourism and hospitality sector is neither an automatic nor an easy process.

Value addition has been promoted as a means of adding economic value in South African towns.¹⁸ Two business sectors that add value to either local and/or external primary and/or intermediary materials are the processing and factory sectors.⁸ Success in these sectors had little association with the size of the enterprise assemblages of South African towns (Tables 3 and 6) and smaller towns often had more processors and/or factories than did larger towns. Success in these sectors depends on aspects such as ‘special’ entrepreneurship, talented individuals and management according to the key success factors of a specific business model.¹⁹ In this study, relatively small towns were quite successful in the processing sector, suggesting that it is not beyond the capabilities of small towns to achieve success.

The mining sector needs special consideration. The statistically significant correlations (and hence proportionalities) registered between the numbers of mining-related enterprises and the total number of enterprises for the total town group, as well as for the large town groups (Table 3), could be spurious and might have been caused by the impact on the calculations of the large mining sector of Welkom. However, a large mining sector is not a precondition for significant business development, because some other large towns in this study did not have significant mining sectors. The mining sector also did not have a major impact on the enterprise assemblages of medium and small towns and there were no proportionalities (Table 3).

The only economic driver sector that showed significant proportionality throughout is the construction industry (Table 3). This proportionality only really applied to large towns, because although there was a positive correlation between the number of construction enterprises and the total number of enterprises in the medium and small towns, the degree to which the variance could be explained was low and towns of equal size can differ widely in the number of their construction enterprises. The fact that large towns have proportionally larger construction sectors and small towns sometimes have few, if any, suggests that some, if not all, construction activities in smaller towns might be provided by suppliers from the informal sector.

The engineering and technical services, telecommunication services, news and advertising services, vehicle, professional services, and real estate services sectors showed strong proportionalities as far as the large towns were concerned (Tables 4 and 5). These proportionalities apparently broke down for smaller towns. Just as in the construction industry, shifts across the interface between the formal and informal economies might be the cause.

In summary, whilst the economic driver sectors, with the exception of the construction industry, exhibited limited proportionalities (Table 3), the service sectors had more pronounced proportionalities, especially for large towns (Tables 4 and 5).

Some of the towns in this study were towns for which Fransen¹³ provided population estimates (Table 7), which allowed investigation of the relationship for sector enterprises. Regression equations were calculated for town population numbers (independent variable) and sector enterprise numbers for those business sectors with extended proportionalities (Table 8). Because some of these business sectors did not have strong proportionalities in the medium and small town groups, distortions of the regression equations could have resulted. To check if this had happened the above calculations were repeated (Table 9) for those large towns (more than 130 enterprises) included in Table 7. Comparison of Tables 8 and 9 indicates that, if distortion was introduced, it was not severe.

This study augmented the original observations about threshold populations¹ by revealing that there are currently still strong proportionalities between the total number of enterprises per town and the number of sector enterprises per town for most of the services sectors presented in Table 2. Regression equations quantified these relationships (Tables 3, 4 and 5) and much of the variances of particular sector enterprise numbers were explained (Table 6). Proportionalities were also linked to the population numbers of towns in general (Table 8) and especially to those of the large towns (Table 9).

A comprehensive literature search revealed that little has been reported, internationally as well as locally, on the reasons why proportionalities are present and could be expected in the enterprise structures of towns. For instance, a recent report on the regeneration of small towns in South Africa paid little attention to this issue.²¹ The exception is provided by Toerien and Seaman⁹ who likened enterprise development in South African rural towns to islands rising above the sea and over time being colonised by all kinds of living organisms. As new space becomes available on a natural island, some immigrant organisms will use it.¹⁰ Toerien and Seaman⁹ showed that the Species Equilibrium Model of MacArthur and Wilson¹⁰ can be used to describe the dynamics of enterprise development in South African towns. They suggested that when a new town is founded, entrepreneurial space becomes available and is utilised by entrepreneurs who, similar to their organism counterparts, have to move across a ‘sea’ to reach the opportunities. Not all entrepreneurs are successful in the competitive business environment and some enterprises go under. Over time equilibrium is reached between the rate of development of new enterprises and the rate of disappearance of existing enterprises and this phenomenon plays a major role in defining enterprise proportionalities. In addition, issues such as town size, population size and town age also played a role in determining the proportionalities of the 12 Karoo towns.⁹

The question as to why there are such marked differences in proportionalities between most of the economic driver sectors and the service sectors deserves attention. In particular, the difference(s) between the utilisation of entrepreneurial space in the trade sector (Table 4) in contrast to, for example, the processing sector (Table 3), needs clarification. A specific question can be posed: why is there such a strong correlation in the trade sector over 125 towns of different sizes, ages, physical localities and origins, and why is it not the case for the processing sector?

Considering Beinhocker’s²² discussion of competition between business plans, and hence between enterprises, different proportionalities in the two sectors probably suggest that entrepreneurial space is used differently by the entrepreneurs of the two sectors. In the trade sector, entrepreneurs are able to ‘see’ business opportunities easily and exploit them. As a consequence, when one business disappears as a result of competition it is rapidly replaced, usually by the actions of a different entrepreneur who is willing to take on the risks involved. This phenomenon represents ‘run-of-the-mill’ entrepreneurship with entrepreneurs that can ‘see’ and react to ‘standard’ business opportunities with perceived limited risks.

In contrast to the above, value addition in the processing and factory sectors is not pursued effectively in South Africa and small towns may have more of these enterprises than very large towns (Table 3). Obviously, available entrepreneurial space is not being utilised fully, perhaps because it is much more difficult to ‘see’ these business opportunities and they probably are more risky and require significantly higher capital inputs. Success in these sectors is dependent on ‘special’ entrepreneurs.

The basic differences between enterprises founded upon ‘run-of-the-mill’ and ‘special’ entrepreneurship are fundamental. ‘Run-of-the-mill’ enterprises are dependent on, and limited by, local demand and if there is only a small demand, the entrepreneurial space is small. In contrast, ’special’ enterprises have much larger markets because their products and/or services are exportable, or in other words, they are potential global players and have an unlimited scope.

The implications for rural development and regeneration of these two broad entrepreneurial types are considerable. For instance, the results (Table 4) suggest very strongly that the trade sector offers little scope to expand local economies because the entrepreneurial space in the trade sector is already fully exploited and there is little chance to create additional opportunities for emergent entrepreneurs. When a new enterprise is started in a sector that is already congested and in balance with the market demand of this sector, it will be subject to strong competition. If it manages to scrape out a place in the sector, it will do so at the expense of another enterprise that already operates in that sector. A system of ‘musical chairs’ will ensue where one enterprise is merely replaced by another but the total ‘game’ has not grown.

To expand the economies of South African towns, the money spent in the economies of these towns must be grown. This challenge is huge. Nel and Hill¹² outlined some of the complicating factors, for example, agriculture being under pressure resulting in a loss of farmers and farm workers as clients of towns’ enterprises; better roads and transport infrastructure resulting in rural and town residents making their purchases in the larger towns of a region; the loss of personnel (and their wages) of government departments and organisations such as the South African Railways, and the loss of schools. Sharp contrasts exist in LED practice between large cities with their focus on competitiveness, secondary cities with a focus on economic readjustment and small towns in which LED practice is limited or laggard.¹⁷ Given these challenges, it is clear that development strategies must focus on how the flow of money into towns can be enhanced. Success in the mining sector depends on the presence of mineral resources, which cannot be planned. Therefore, growing the processing, factory, tourism and hospitality, and construction sectors must be a serious consideration in most strategic LED plans for towns because these sectors offer opportunities to increase the flow of money into towns.

Several studies have indicated that many municipalities lack an understanding of their economic spaces.^17,21 The hypothesis outlined here, of two broad kinds of entrepreneurship in South African towns, offers a new way of thinking about rural development and job creation. It needs to be investigated further and a better understanding of the proportionality phenomena is a key to such an endeavour.

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11. Zuma J. We remain committed to taking the economic transformation forward. Letter from the President. ANC Today. 2009 Nov 13. Available from: http://www.anc.org.za/docs/anctoday/2009/at45.htm

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13. Fransen H. Old towns and villages of the Cape. Johannesburg: Jonathan Ball Publishers, 2006; p. 27–28.

14. Fransen H. Old towns and villages of the Cape. Johannesburg: Jonathan Ball Publishers, 2006; p. 361–363.

15. Erasmus BPJ. [On the road in South Africa]. 2nd ed. Johannesburg: Jonathan Ball; 2004. Afrikaans.

16. Van Rensburg G. [Rural areas are not no-man’s-land]. Die Burger 2010 Nov 13;13. Afrikaans.

17. Rogerson CM. Strategic review of local economic development in South Africa: Final report submitted to minister Sicelo Shiceka (dplg). Bonn: GTZ, 2009; p. 1–31. Available from: http://www2.gtz.de/wbf/4tDx9kw63gma/LED_Strategic_ReviewFinal.pdf

18. Toerien DF. Taming Janus: Technology, business strategy and local economic development. Stilbaai: DTK, 2005; p.157–170.

19. Toerien DF. Taming Janus: Technology, business strategy and local economic development. Stilbaai: DTK, 2005; p.140–156.

20. Van der Merwe IJ, Nel A. [The city and its environment: A study in settlement geography]. Stellenbosch: Universiteit-Uitgewers, 1975; p. 104. Afrikaans.

21. Wessels J. Development of a small town regeneration strategy: Final Report. Pretoria: ARS Progretti, 2010; p. 1–148.

22. Beinhocker ED. The origin of wealth: Evolution, complexity, and the radical remaking of economics. Boston: Harvard Business School Press, 2006; p. 287–297.