SPATIAL DYNAMICS IN KWAZULU NATAL 1

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1 Province of KwaZulu-Natal Provincial Treasury IMES Unit SPATIAL DYNAMICS IN KWAZULU NATAL 1 Clive Coetzee General Manager: IMES Unit Economist clive.coetzee@kzntreasury.gov.za Working Paper 9.1: dd November The views expressed in this working paper are the views of the author and might not necessarily reflect the views of the Provincial Treasury. All rights reserved

2 Introduction The province of KwaZulu-Natal is located in the southeast of South Africa; it borders three other provinces and the countries of Mozambique, Swaziland, and Lesotho, along with a long shoreline on the Indian Ocean. The province covers a land area of 93,378km² or 7.65 per cent of the total land area of South Africa. There are at present about 10.5 million people residing in the province or about per cent of the total national population. The province has a population density of almost 109 people per km², significantly less than the 602 people per km² recorded in the Gauteng province, but significantly greater than the 48 people per km² recorded in the Mpumalanga province (data supplied by Global Insight, Coetzee unpublished report, own calculations). In 2012, KwaZulu-Natal GDP was estimated at about R322 billion. Provincial output increased from R56 billion during the 3rd quarter of 2001 to R79 billion during the 1st quarter of 2013 in real terms (constant 2005 prices). Economic activity picked up robustly from 2000 to 2008 (4.42 percent average annual growth), thereafter growth moderated and ultimately decreased (-1.78 percent) in 2009 due to the global financial crisis. Positive growth resumed during 2010 albeit at a very modest pace with the provincial economy recording a very modest 3.76 percent, 3.69 percent and 2.95 percentage growth during 2010, 2011 and 2012, respectively. The province contributes on average between 16 per cent and 17 per cent to the national gross domestic product, significantly less than the 35 per cent of the province of Gauteng but slightly more than the 15 per cent of the province of the Western Cape (data supplied by Stats SA, Coetzee unpublished report, own calculations). In terms of per capita GDP, the province of KwaZulu-Natal only ranks seventh out of the nine provinces in 2011 suggesting a fairly poor or poverty stricken province. This is mainly because of the political uncertainties that the province faced up to The province of KwaZulu-Natal in general only started on its development and growth path post 2001 and not post 1996, where as the other provinces started post 1996 and thus the province is behind the development and growth curve, i.e., relatively underdeveloped and poor in 2001 compared to the other provinces. The province of KwaZulu-Natal consists of 52 local economic regions. These local economic regions are demarcated by the Municipal Demarcation board ( In the majority of cases (44 of the 52) each local economic region consist of one (1) major urban centre (city or town), which is surrounded by residential suburbs and agricultural land. This is to

3 a large degree in support of the static monocentric model. However, it is especially the five (5) major municipal regions that lean more towards the polycentric city model. Thus the majority of the local economic regions conform to the static monocentric model whilst only a small number conform to the polycentric city model. The province of KwaZulu-Natal is also a fairly concentrated province, for example: About 50 percent of the provincial population resides in the five main local economies. Almost 80 per cent of the provincial GDP is produced in the five main local economies. Personal per capita income is more than double in the five main local economies compared to the rest of the province. Poverty levels are almost half in the five main local economies compared to the rest of the province. The five main local economies cover only about 9.5 per cent of the total provincial land cover. Population density levels are more than 12 times higher in the five local economies compared to the rest of the province. The five local economies account for about 93 per cent, 86 per cent and 78 per cent of all new Office & Banking Space, Shopping Space and Industrial & Warehouse Space from 2001 to (data supplied by Stats SA and Global Insight, Coetzee unpublished report, own calculations). The five local economies which are also the major municipal regions are: Ethekwini Municipality (Durban). It is the economic hub of KwaZulu-Natal and the major import/export center in South Africa. Msunduzi Municipality (Pietermaritzburg). It is the second largest city within KwaZulu-Natal and is the capital city of the province of KwaZulu-Natal. Umhlathuze Municipality (Richards Bay, Empangeni). It is the home of manufacturing in the province, boasting two world class aluminium smelters and the world s largest export coal terminal. Hibiscus Coast Municipality (Port Shepstone). It covers an area of approximately 90 km of coastline, comprising of 21 beaches, not surprisingly the premier tourism destination in the South Africa.

4 Newcastle Municipality (Newcastle). Situated in the northern corner of the province, it is has significant coal deposits and agricultural land. The location and economic dominance of the five local economies are clearly displayed in the below night satellite map of the province. It is evident that the economic activity of the province is located (with the exception of Newcastle) along the N2 from Port Shepstone to Richards Bay and along the N3 from Pietermaritzburg to Durban. In-land or rural KwaZulu-Natal seems relatively starved of economic activity. Figure 1: Night Satellite Map of the Province City Size and Distribution Analysis The table below (table 1.1) indicates the road distance between the different local economies. The Ethekwini and Msunduzi local economies are the closest to each other whilst the Newcastle and Hibiscus Coast local economies are the farthest from each other. The two largest local

5 economies are the closest to each other whilst the two smallest local economies are the farthest from each other as indicated in table 1.2. Table 1.1: Road Distance Matrix km Ethekwini Msunduzi Umhlathuze Hibiscus Coast Newcastle Ethekwini Msunduzi Umhlathuze Hibiscus Coast Newcastle (own calculations) Table 1.2: Population per Road Distance Matrix Ethekwini Msunduzi Umhlathuze Hibiscus Coast Newcastle Ethekwini Msunduzi Umhlathuze Hibiscus Coast Newcastle (data supplied by Global Insight, own calculations) The Lorenz curves for the five local economies are displayed in the graph below (graph 1.1). The vertical axis represents the cumulative population distribution of the total population of the five local economies and the horizontal axis represents the five local economies. The red linear line represents equality in the population distribution and the non-linear lines represent the level of inequality in the population distribution for the stated periods. The data demonstrate starkly how the size distributions of the local economies have not changed noticeably over the period, even during the 2003 to 2007 economic boom and subsequent economic crisis period. This suggests that the relative population distributions for

6 the five local economies have stayed fairly constant over the period. However it is also very clear that the size distributions are very unequal, i.e., there is a significant concentration of the population in one of the local economies (about 70 percent) where as the other four have similar but very smaller population sizes (cumulatively about 30 percent). Graph 1.1: Lorenz Curve for the Five Local Economies (data supplied by Global Insight, own calculations) Graph 1.2 displays the average annual population growth rates of the five local economies from 1996 to 2012 and the initial population size of each local economy in The vertical axis and the red points represent the average annual population growth rate for the period whilst the horizontal axis represents the population size (in thousands) of each of the five local economies in It is possible to estimate the regression line or function using the following equation: where: Y t = α + βx t + ε t (eq 1.1) Y t = average annual population growth rate (1996 to 2012)

7 X t = population in 1996 ( 000) ε t = error term The coefficients will be estimated using Ordinary Least Squares (OLS) and regression makes use of cross-sectional data. It must be stated that because only five local economies are included, there will only be five observations, which suggests the regression will suffer serious limitations. It becomes then very difficult to say anything about the usefulness of the estimated coefficients. Graph 1.2: Population Growth Rates and 1996 Cities Size (data supplied by Global Insight, own calculations) The descriptive statistics of the cross section is displayed in the table below. The normality test (Jargue-Bera) indicates that the variables are normally distributed (p>0.05). Table 1.4: Descriptive Statistics of the Cross-Section Y Mean Median X

8 Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq. Dev Observations 5 5 The results of the regression are displayed in the table below. The test statistics (t-statistics and adjusted R-squared) shows that the α and β coefficients and the regression function is not statistical significant. However it is not possible to draw any inference from the regression given the limitations as mentioned earlier. Table 1.5: Regression Function Dependent Variable: Y Method: Least Squares Sample: 1 5 Variable Coefficient Std. Error t-statistic Prob. X C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) However let us assume that it was indeed possible to infer from the regression. Then the regression line implies or suggests that there is a very modest negative relationship between initial size of the local economy and the population growth rate during that period. However the regression line is not statistically significant as per the statistical test and thus it is not possible to argue, as expected, that the smaller the initial population size the faster the population increases and vice-versa. However the graph does suggest that the average annual population growth rates of four of the local economies have not been that dissimilar from one another. This supports the findings of a

9 constant level of inequality in the population distributions amongst the five local economies for the period. "Zipf's Law" is the name of a remarkable regularity in the distribution of city sizes all over the world, also known as the "Rank-Size Distribution", according to Brakman et al (2009). They argue further that the remarkable log-linear relationship of the city-size distribution holds for virtually all countries. The process involves assigning ranks based on population size to each of the local economies, for example the local economy with the largest population size is given a rank number of 1 and so forth. If the natural logarithm of the rank and of the city size (measured in terms of the number of people) are calculated and then plotted in a diagram, it will show a remarkable log-linear pattern, i.e., the Rank-Size Distribution. If the slope of the line equals minus 1 the relationship is known as Zipf's Law. The graph below (vertical axis = log of population and horizontal axis = log of rank) indicates the distribution of the five local economic sizes and the regression line. The regression equation is as follows: Y t = α + βx t + ε t (eq 1.2) where: Y t = log of the rank X t = log of population size in 2012 ε t = error term The coefficients will be estimated using Ordinary Least Squares (OLS) and the regression makes use of cross-sectional data. However the situation here will be exactly similar to the previous situation in that only five observations are included. Nevertheless the regression function will be estimated and applied for illustrative purposes. The descriptive statistics of the cross section is displayed in the table below. The normality test (Jargue-Bera) indicates that the variables are normally distributed (p>0.05). Table 1.6: Descriptive Statistics of the Cross-Section Mean X Y

10 Median Maximum Minimum Std. Dev Skewness Kurtosis Jarque-Bera Probability Sum Sum Sq. Dev Observations 5 5 The results of the regression are displayed in the table below. The test statistics (t-statistics and adjusted R-squared) show that the α and β coefficients and the regression function are indeed statistical significant. However it is not possible to draw any inference from the regression given the limitations as mentioned earlier. Table 1.7: Regression Function Dependent Variable: Y Method: Least Squares Sample: 1 5 Variable Coefficient Std. Error t-statistic Prob. X C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) However let us assume that it was indeed possible to infer from the regression. Then the regression line implies or suggests that the Rank-Size Distribution is indeed statistically significant. The result is a slope that is statistically significant equal to 1, indicating that the local economies are as evenly distributed as per the prediction. The local economies are thus characterized by a largest local economy, with other local economies decreasing in size relative to it, initially at a rapid rate and then more slowly.

11 Graph 1.3: Zipf's Law, y = x R² = SE = (data supplied by Global Insight, own calculations) Economic Size and Distribution Analysis These five cities also differ significantly in terms of their economic structure. Table 1.8 displays the annual average (2006 to 2012) contribution rates for each economic sector for each of the five cities compared to the national and provincial economies. The structural differences are fairly evident, for example Richards Bay and Newcastle are production economies whilst Pietermaritzburg and Port Shepstone are consumer economies. Durban has a much more diversified economy which is fairly similar to the national economy, except for the transport sector. Table 1.8: Annual Average Contribution Rates (%) SA KZN Durban Port Shepstone PMB Newcastle Richards Bay Agriculture, forestry and fishing Mining and quarrying Manufacturing Electricity, gas

12 and water Construction Wholesale & retail trade; hotels & restaurants Transport, storage and communication Finance, real estate and business services Personal and General Government Services Stats SA, Global Insight, Own Calculations) The GDP and economic growth rates of the five the cities has also been fairly varied. Table 1.9 displays the per annual GDP and average economic growth rate of each of the cities and the national and provincial economies. It seems evident that the differences in total economic output are very big and substantial although the economic growth rate disparities are marginal. It is also interesting to note that the city economic growth rates are much more volatile than the national and provincial growth rates (except for Pietermaritzburg). Coetzee (2012) indicates that the GDP distributions of the five cities have not changed noticeably over the period. This suggests that the relative GDP distributions for the five cities have stayed fairly constant over the period Table 1.9: Annual Gross Domestic Product (R m 2005 constant prices) SA KZN Durban Port Shepstone PMB Newcastle Richards Bay

13 Average St Dev (Stats SA, Global Insight, Own Calculations) The economic divergence of the five cities is also apparent when looking at the per capita income statistics. Table 1.20 displays the annual per capita income for each of the five cities and the national and provincial economies. The differences between the annual per capita income of the five cities suggest that there has been no or very little convergence between the five cities. Table 1.20: Annual per capita income (Rand, current prices) SA KZN Durban Port Shepstone PMB Newcastle Richards Bay Average St Dev (Stats SA, Global Insight, Own Calculations) The urbanization or concentration of economic activity suggests or implies that the production of goods in services in the province is increasingly becoming concentrated in the five cities. To support the above notion of the urbanization of economic activity, this study will make use of

14 data obtained in the building plans reported as completed publication by Statistics South Africa. Stats SA publish on an annual basis the mentioned publication that reports the number of building permits, square meters and rand value of building plans reported as completed per municipality. The categories of building plans completed include amongst others dwellings more than 80m², office & banking space, shopping space and industrial & warehouse space. Therefore, it becomes possible to spatially profile the province in terms of the locations that are expanding or not in terms of the addition of total and per category of space. The data obtained (2001 to 2010) from the mentioned publication was used to calculate various location and space related statistics. The statistics derived indicates, amongst others, the following: The five cities received 67 per cent of the total square meters of building plans completed, i.e., 67 per cent of total provincial space expansion. An additional 9 per cent of the total square meters of building plans completed occurred in municipalities that are adjacent to the urban municipalities. 38 of the total number of municipalities in the province (75 per cent) received only 11 per cent of the total square meters of building plans completed. The above findings are also displayed in the table below and suggest significant levels of concentration of economic activity in the province and are supported by the following table, i.e., table 6. Table 1.21 indicates that, except for dwellings less than 80m², urban KZN received the bulk of the total square meters of building plans completed for the indicated categories of building plans completed for the period, for example the five cities received 90 per cent of the total office and banking square meters of building plans completed, etc Table 1.21: City Economic Concentration 1996 to 2010 Total Space Expansion Urban Space Expansion, m² Rural Space Expansion, m² Total Provincial Space Expansion, m² Urban (As a % of Total Space) Rural (As a % of Total Space) Dwellings less than 80m² Dwellings equal and more than 80m²

15 Flats Townhouses Other residential buildings Schools, nursery schools, crèches, hospitals Churches, Sport and Recreation Office & Banking Space Shopping Space Industrial & Warehouse Space Average (Stats SA, Own Calculations) Provincial Spatial Analysis The population and economic spatial dynamics suggest that the population and economy of the province is concentrated in the five cities, that the concentration levels are increasing with economic concentration increasing at a faster pace than the population concentration and that the five cities are not converging. These five cities are thus growing or expanding (both from a population and economic perspective) at very similar rates, but for very different reasons. The average international urbanization rate seems to be around the 70 percent. In KZN the urbanization rate is about 55 percent (assuming the five cities = to urban). The economy is already operating at about the 70 percent urbanization rate so there seems to be a disequilibrium position in the province, i.e., 55 percent of the population resides in the five cities, but 80 percent of the provincial gdp is generated in these five cities and 70 percent of all new economic space is located in the five cities. This is displayed in graphs 14, 15 and 16 below.

16 Graph 14, 15 and 16: Urbanization and Concentration in KZN Population Ethekwini Hibiscus CoastThe Msunduzi Newcastle umhlathuze Rural Urban GDP 0.00 Durban Port Shepstone PMB Newcastle Richards Bay Urban Rural Urban (As a % of Total Space) Building Plans Approved

17 The location and specifically the relative location of the five local economies are displayed in the below Google Earth map. Map 2: Satellite Map of the Five Cities Port Shepstone Richards Bay (Google Earth) Arribas, et al (2010) state that the gravity model of bilateral trade is of primary importance in empirical analyses of trade patterns. Its simplest version states that trade interactions between two geographically defined economic entities (either countries or regions) are proportional to the size of these entities and inversely related to the distance between them. Some authors such as Leamer and Levinsohn (1995) state that the gravity model provides some of the clearest and most robust empirical findings in economics (Leamer and Levinsohn, 1995, p.1384), whereas others such as Rose (2000) note that the gravity model provides a framework with a long track record of success (Rose, 2000). The traditional gravity model drew on analogy with Newton's Law of Gravitation. A mass of goods or labour or other factors of production supplied at origin i, Yi, is attracted to a mass of

18 demand for goods or labour at destination j, Ej, but the potential flow is reduced by the distance between them, dij. Strictly applying the analogy, X ij = Y i E j /d 2 ij gives the predicted movement of goods or labour between i and j, Xij. The results of the gravity model application are presented in the table below. The value itself does not really mean anything. It only means something on a comparative basis; for example trade between Durban and Pietermaritzburg should be far greater than trade between any of the other cities, especially between Newcastle and Port Shepstone. Table 1.22: Gravity Model (GDP per city and distance between cities) Pietermaritzburg Durban Richards Bay Newcastle Port Shepstone Pietermaritzburg Durban Richards Bay Newcastle Port Shepstone (Global Insight, Own calculations) In its most general formulation, it explains a flow Fij (of goods, people etc.) from an area i to an area j as a function of characteristics of the origin (Oi), characteristics of the destination (Dj) and some separation measurement (Sij): F ij =O i D j S ij, i=1,..i; j=1,..j Customarily the model is estimated in log-linear form. When applied to flows of goods between countries, by analogy, the model stresses that trade increases with size and proximity of the trading partners. Rewriting the above equation in log form, a vector of bilateral trade flows (exports, imports, total trade) Fij is modelled as:

19 F ij =Xβ + Ɛ, Ɛ N(O,σ²) where X is a vector of (logs of) explanatory variables, and ε a white noise error term. In the simplest specification, X contains proxies for the size of the two economies (GDP, population and/or GDP per capita) and the distance between them (as proxy for transportation costs and other obstacles to trade). The panel specification for the gravity model is as follows: X it = α +β 1 Y 1,it + β 2 Y 1,it + βd 1,it + ε it for 1 = i,...n for t = 1,...T and cross section identifiers = 20 The gravity value is an estimated value since no inter-city sales (trade) data exists. It is calculated by multiplying a sales percentage of the host city to the trading city with the GDP of the trading city. The sales percentage is derived from the 2011 business confidence survey where businesses of each city were asked to indicate the percentage sales of their total sales to the other four cities. However this question was only included in the 2011 survey. The 1996 to 2010 values were calculated by deflating the 2011 value with the provincial per annum economic growth rate from 1997 to 2011 The panel is displayed in the below exhibit.

20 Exhibit 1.2: Gravity Model X Y Y2 13 D The correlation matrix of the model (panel data of the five cities from 1996 to 2011 in log format) are displayed in the table below. The results support the notion of positive attraction and negative distance, where, X 1it = gravity value (sales from city a to city b), Y 1it = GDP of the host city, Y 2it = GDP of the trading city and d 1it = distance between host and trading city. Table 1.23: Correlation matrix (i=1..5, t= , cross section identifiers = 20) X1 Y1 Y2 d1 X Y

21 Y d The results of the simple OLS Gravity Model is displayed in the below table. Table 1.24: Simple OLS Regression Equation (i=1..5, t= , cross section identifiers = 20) Variable Coefficient Std. Error t-statistic Prob.?Y ?Y ?D C R-squared Mean dependent var Adjusted R-squared S.D. dependent var S.E. of regression Akaike info criterion Sum squared resid Schwarz criterion Log likelihood Hannan-Quinn criter F-statistic Durbin-Watson stat Prob(F-statistic) The coefficients indicate that a 1 percent increase in the size of the host economy will be associated with a 1.7 percent per annum increase in trade flows and a 1 percent increase in the size of the trading economy will be associated with a 11.5 percent per annum increase in trade flows, whilst a 1 percent increase in distance will be associated with a 2.5 percent per annum decrease in trade flows. Testing the model indicates that the coefficients are statistically significant and that the residuals stationary. It must however be cautioned that because of lack of accurate time series inter-city sales data, the model might suffer from statistical problems and therefore further econometric analysis of the panel will not be pursued.

22 Given the dominance of the five local economies and the results of the gravity model it is possible to argue the there will be a steady inflow of people into these five local economies as people seek employment and higher wage/income opportunities, etc. This seems also to be supported by a number of theories postulated in recent times wrt urbanization. For example Ernest Ravenstein, according to Corbett (2001), is widely regarded as the earliest migration theorist. Ravenstein, an English geographer, used census data from England and Wales to develop his "Laws of Migration" (1889). Ravenstein concluded that migration was governed by a "push-pull" process; that is, unfavorable conditions in one place (oppressive laws, heavy taxation, etc.) "push" people out, and favorable conditions in an external location "pull" them out. Ravenstein's laws stated that the primary cause for migration was better external economic opportunities; the volume of migration decreases as distance increases; migration occurs in stages instead of one long move; population movements are bilateral; and migration differentials (e.g., gender, social class, age) influence a person's mobility (Corbett, 2001). Map 3: Potential Flow of People Port Shepstone Richards Bay

23 The theory of urbanization and the economic and spatial realities suggests that there will be a steady but continuous flow of people and economic activity from the rural areas of the province (the non-five local economies) to the five local economies as displayed in map 4. The spatial economic interdependence between the five local economies will therefore continue to increase and the regional spillover effects through the interregional interaction could be expected over time and the increasing interregional or intercity trade will continue to enhance each local economies complimentarily as well change the nature of its competitiveness. Map 4: Growth of the Five Local Economies Port Shepstone Richards Bay The five local economies will therefore continue to experience an inflow or people and economic activity so that at some point in time the current equation, i.e., 55% population = 78% gdp = 73% new economic space will look as follows, 70% population = 80% gdp = 90% new economic space

24 This will imply that the four local economies (not Newcastle) will continue to grow to each other so that at some stage these four local economies will be one continuous agglomeration (continuous urbanized area) as displayed in the map below. The big question is the role of Newcastle in this agglomeration. Map 5: Major Agglomeration Port Shepstone Richards Bay It is therefore very possible to argue that the process of conurbation has already started and are well under way. A "conurbation" is a region comprising a number of cities, large towns, and other urban areas that, through population growth and physical expansion, have merged to form one continuous urban and industrially developed area. This is displayed as the solid blue area in the map above.