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Home , Colony of Natal, Mineral Revolution, Port Elizabeth

Market integration in before unification1

WILLEM H. BOSHOFF2 AND JOHAN FOURIE3

The discovery of minerals in the South African interior caused volatile economic transformation and political upheaval across which included one of Britain’s most expensive colonial wars (1899-1902) and the unification, in 1910, of two British territories and two defeated Afrikaner republics. Using techniques borrowed from the applied business cycle literature, we use two data sources to, firstly, show market integration between South Africa and her main trading partners following the discovery of diamonds, and, secondly, within South Africa after the Second South African War. Evidence for the first hypothesis is obtained from an annual grain price series spanning 76 years, while evidence for the second hypothesis comes from a new, monthly panel dataset of 5 commodities across 21 South African towns between 1897 and 1910.

JEL CODES: F15, N17, N77 KEYWORDS: market integration, South Africa, globalization, business-cycle filters, convergence, law of one price

INTRODUCTION The process by which markets integrate is, or should be, fundamental in an explanation of the causes of industrialisation and economic progress. The rapid growth of the eighteenth- and nineteenth century Atlantic economy was, for example, to a large extent dependent on the decline in trans-Atlantic transport costs and the integration of the European and North American economies (North, 1958, O'Rourke and Williamson, 2002). When exactly this occurred, is a matter of much recent debate (Sharp and Weisdorf, 2013). Understanding when and how markets integrate also helps to explain comparative change: Shiue and Keller (2007), for example, show that markets in performed better than those in Western and the Yangzi delta on the eve of the Industrial Revolution. Using grain prices for 100 European cities, Chilosi et al. (2013) show that market integration in Europe was a ‘gradual and step-wise

1 The authors would like to thank Wynand Fourie, Matlhodi Matsai, Elana Moolman, Agrippa Stulimani and Wimpie van Lill for valuable research assistance. This paper is prepared for the Economic History Association meetings in Washington D.C. 2 Department of Economics, Stellenbosch University, South Africa. E-mail: [email protected]. 3 Department of Economics, Stellenbosch University, South Africa. E-mail: [email protected]. 1 rather than sudden process, and that early modern market structures were shaped by geography more directly than by political borders’.

We ask similar questions of South Africa. The final decade of the nineteenth century and the first of the twentieth century was, according to Charles van Onselen (1982), an ‘extraordinary period of social, political and economic change’ in South African history. The discovery of diamonds in the interior in the early 1870s followed by the discovery of gold on the Rand in the 1880s, transformed the South African interior from a sparsely populated society of Afrikaner agriculturalists to an economy dominated by the and finance – and migrant – hub of . The rapid changes in the interior affected the Cape economy too, providing an outlet for produce and support for the transport industry which connected the new centres of production with the international markets.

Yet little is known about the integration of regional South African markets during this period. Before the discovery of minerals, transport connections to the two were slow, expensive and limited to small consumer goods. The – and the need to not only transport diamonds and gold to the coast, but also the large machinery required on the mines – resulted in the rapid expansion of the railway network and a decline in trade costs. Nevertheless, geographical barriers, notably the vast and the high altitude of the interior, and political boundaries between the Boer republics and the British Cape, remained costly. Whether the new infrastructure increased market integration for those regions remains uninvestigated.

This paper answers two questions: When did South Africa globalise or, in other words, when did South African prices converge on international prices? To answer this we use an annual grain price series from 1837 to 1913 in comparison with similar series from Britain, Europe, the United States and . We find no evidence of integration before the mineral discoveries of 1870, with strong convergence before and after the Second South African War (1899-1902). Secondly, we focus on the period of rapid economic and political change: using an unbalanced, monthly panel of selected years between 1889 and 1914, we calculate the degree of integration within South Africa – notably within the Cape , but also between the Cape and and the two Boer republics, the Orange and the Transvaal. We use techniques standard to the market definition and business cycle literature to also investigate the impact significant political events, notably the Second South African War (1899-1902) and political unification in 1910.

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WHEN DID SOUTH AFRICA GLOBALISE?

Europeans arrived in 1652 at the Cape to establish a small refreshment station under the auspices of the Dutch East Indian Company. The Commander soon realised that trade with the native Khoe would not provide adequate provisions for the passing ships on their route to the East, and therefore released Company servants to become free burghers, starting a process of land acquisition and conquest that continued over several centuries. These settlers moved North and East – engulfing the pastoral Khoe, who dwindled in number due to disease and the superior military and economic power of the settlers – until reaching the agrarian isiXhosa on the banks of the Fish River. Over 143 years of Dutch rule, settler territory and numbers expanded, buttressed by the importation of slaves from the East Indies, to reach roughly 15 000 inhabitants by 1795 when the British first gained control.

After a short period of Batavian rule (1803-1806), the became part of the expanding . The Cape population continued to expand, especially in urban , but also on the frontier where pressure for farm land became intense, leading to a series of , exacerbated by the arrival, in 1820, of British settlers. While small towns dotted the country-side, production was predominantly agricultural. During most of the seventeenth, eighteenth and early-nineteenth centuries, exports were limited to grains and wine. Manufactures were mostly imported.

Unhappiness with British rule, including the emancipation of slaves in 1834, caused several thousand frontier farmers of Dutch origin to trek across the Orange River, deeper into the interior. After several years of trekking, two Republics were founded, the in 1854 and the in 1856. The , on the eastern coast, was proclaimed a British colony in 1843. By the 1860s, then, the Cape Colony was the economic hub of a vast, sparsely populated interior.

The discovery of minerals in the South African interior substantially altered the economic landscape. The diamond discoveries drew immigrants from the Cape and abroad into the interior in search of riches, founding new towns like Kimberley that, by in the 1891 census, was the second largest town in South Africa. The discovery of gold in the during the 1880s had an even greater effect. By unification in 1910, Johannesburg was the largest city and economic centre of Southern Africa (according to the 1911 South African Census, 121,857 whites versus 64,619 in Cape Town).

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Although the Cape was founded by the first multinational company, its inhabitants descended of European settlers and Malayan, Indonesian, Indian, Mauritian, Madagascan and Mozambican slaves (Baten and Fourie, 2012), it is not clear to what extent the Cape Colony was technically globalised or, in other words, whether Cape markets were integrated into the global market. Before the discovery of minerals, export volumes were small and limited to a few commodities; as Figure 1 shows, a strong rise in both exports and imports were experienced after the discovery of diamonds (after 1870), and again after the discovery of gold (after 1885). The Second South African War (1899-1902) had a profound impact on the productive capacity of the economy, reducing exports and boosting imports. By 1909, however, exports had recovered while imports had fallen, resulting in a sizeable positive trade balance.

Figure 1: Exports and Imports of goods (excluding specie) from the Cape Colony, 1850-1909 Source: CGH Blue Books (1909).

When, then, did South African markets integrate into the global economy? To answer this question, we use annual price data for the Cape Colony (from 1836) and Natal (from 1852), obtained from De Zwart (2013). We compare the South African series with a constructed annual wheat price series for the United Kingdom, the colonial ruler and South Africa’s main trading partner. The UK series was compiled from Jacks (2006) monthly wheat prices for twelve UK cities. The South African series is adjusted to the same unit of measurement, and both series are logged. Figures 2 and 3 present the results.

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Figure 2: Trends for South Africa and United Kingdom wheat prices, 1836-1913

Figure 3: Medium-term cycle trends for South African and United Kingdom wheat prices, 1836- 1913

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Figure 2 suggests a clear break around 1872, when exports of diamonds first reached more than £1 million. Before 1872, there seems to be little correlation between wheat prices in South Africa and in the United Kingdom. Yet during the early 1870s, a positive correlation seems to emerge between the two series. This visual inference is supported by measuring the correlation coefficient: between 1836 and 1871 a positive but statistically insignificant correlation of 0.05 is calculated. Between 1872 and 1913, however, a statistically significant, positive correlation of 0.86 is found, suggesting strong co-movement between South African and UK prices.

Figure 3 use frequency filters to extract information relevant to specific time horizons from time-series data. A high-frequency filter, for example, would extract short-term variation, removing all other information such as long-run trends. Low-frequency filters, in turn, would remove all fluctuations to focus only on long-run information. We consider both the long-run trend and the so-called medium-term cycle, which requires the explicit definition of the time horizons associated with ‘long run’ and the ‘medium term’. We follow the literature which defines business cycles as fluctuations over a time horizon of up to eight years. Boshoff and Fourie (2010) show the importance of considering longer-term fluctuations, so-called medium- term cycles, contained in the long-run trend of 18th century South African data. Consistent with Boshoff and Fourie (2010) and Comin and Gertler (2006) (for more recent US data), we define the ‘medium run’ as variation with a horizon of between eight and fifty years. We employ the so- called Christiano and Fitzgerald (2003) (CF) filter. The CF filter outperforms the Baxter and King (1999) (BK) filter when extracting information for medium- to longer-term horizons.

The filtered series confirms the unfiltered evidence of Figure 2. There is no correlation between the medium-term cycle of South African and the United Kingdom before 1870. By the mid-1870s, however, following the discoveries of diamonds and the rapid growth of trade (Figure 1), we witness strong co-movement between two series, suggesting closer medium-run integration with the UK economy.

Apart from the cyclical co-movement, Figure 2 also shows that both series decline considerably; South African wheat prices fall more than 60% over the last five decades of the nineteenth century.4 While this would certainly have injured South African wheat farmers’ producer surplus, the – those entrepreneurs who controlled the diamond and gold mining industries – benefited from the large savings in lower wages that could be paid to white and black workers. Globalisation of the Atlantic economy after the 1860s sped the shift in economic power from agriculture to mining and from the coastal Cape under British control to the Boer republics in the interior.

4 This is calculated by comparing the average of 1858-1863 with the average of 1908-1913. 6

AGRICULTURAL MARKETS ON THE EVE OF UNIFICATION

Before the discovery of minerals, the Boer farmers of the Orange Free State and Transvaal were producing little surplus; most were engaged in subsistence cattle farming, commerce was largely carried on by barter with travelling merchants supplying those goods that could not be provided by the household and transport infrastructure to the distant Cape market was poor or non- existent. The discovery of diamonds changed the fortunes of the republics; foreigners began to migrate in large numbers to the new diamond fields, creating strong demand for stock and other agricultural produce. Kimberley, still a farm in 1869, a decade later became the second-largest town in South Africa.

But the growth of the interior also brought other changes: railways were built to carry the heavy machinery required by the mines inland and to move the diamonds and gold back to the coast for export. The railway from Cape Town to Kimberley was completed in 1885, and from , the economic hub on the rapidly-growing coast, to Kimberley in 1892, the same year that Johannesburg was connected to Kimberley. This improvement in transport infrastructure brought about by the mineral discoveries must have been significant, yet its impact on market integration in South Africa has not been quantitatively investigated.

For most of the nineteenth century, the great distances between towns and the rugged terrain between the coast and the high altitude of the interior meant that transport was rudimentary and expensive. The four-wheeled wagon, pulled by between twelve and nineteen oxen, was the mainstay of freight transport, carting South African produce such as grain, , hides, vegetables and households items such as groceries, furniture, soap, and barrels between towns (Pirie, 1993: 322). The introduction of railways had naturally affected the traditional ox- wagon routes. The long-distance wagon from Cape Town to Kimberley suffered when the railway line was completed in 1885, not only by creating a much faster and safer substitute, but also because the railway could substitute the expensive firewood transported on wagons with imported Welsh coal. On long-distance routes, railways soon surpassed road traffic.

Glanville (1911) published the freight costs of 15 agricultural products, including wheat, potatoes, eggs, fresh and tobacco, from the Cape Town, Port Elizabeth, East London, Port Natal and Lorenzo Marques harbours (see appendix). Unsurprisingly, towns further away from the coast are more expensive. But access was limited to those towns where the main railway lines to the mines passed through; towns further from the main routes had to rely on transport riders which were expensive over long distances. According to Pirie, in “certain circumstances a

7 price advantage was decisive and ensured that wagons retained a significant share of the long- distance freight transport market” (Pirie, 1993: 322). The competition between road and rail transport was especially severe in the Eastern Cape, notably the Port Elizabeth-Grahamstown and East London-King William’s Town trunk routes and affected branch lines, because of the mountainous terrain and steep topography (Pirie, 1993). Here the trunk lines that connected the main harbours of Port Elizabeth and East London with the main centres of the interior left open a vast terrain that could be serviced by wagons.

Figure 3: Map of South African railways in January 1907, with stations and towns included in the Agricultural Journal data.

Not only were railways more expensive here, but Black transport riders, owing to their lower wages, could more easily undercut railway freight. This was exacerbated by the devastation caused to the livelihoods of Black farmers on the eastern borders of the Cape Colony; the Rinderpest cattle disease (Van Onselen, 1972), the War and the gradual encroaching of white commercial farmers that caused severe losses of land and livestock (Bundy, 1972). Transport riding offered a viable alternative, which resulted in fierce competition between rail and road on the Eastern frontier.

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The result of this competition can be seen from the several attempts by the Cape to protect its expensive rail infrastructure from private competition. In general, the government followed two strategies: to cut rail tariffs, and to tax road transport. The first strategy was implemented with little success: over shorter distances, road haulage continued to be cheaper than rail carriage because of the “more direct routing of ox wagons, the use of roads and pasturage free of charge and the narrower profit margins at which transport riders operated” (Pirie, 1993: 325). The second strategy – to tax road transport – was only successfully passed through parliament in September 1908. The proposal, finally gazetted in December 1908, imposed an additional duty on inbound road traffic at the coast and was to be a success, as 1909 was the first year that all sections of the three branch lines returned a profit.

The impact of these railway expansions on Cape markets has not received much attention, at least not if compared to the burgeoning literature on railways in other regions. Railways improved the integration of markets in Europe (Schwartz et al., 2011, Federico, 2012) and the U.S. (Donaldson and Hornbeck, 2012, Liu and Meissner, 2013), and in places as diverse as Uruguay (Herranz-Loncán, 2011), Indonesia (Marks, 2010) and (Jedwab and Moradi, 2013). The neglect of railway infrastructure can also cause disintegration. Federico and Sharp (2013) show how U.S. railroad construction resulted in rapid integration until the 1920s. But following the First World War, agricultural markets began to disintegrate (Hynes et al., 2012), resulting in large welfare losses for farmers and final consumers. It is tempting to ascribe all market integration during the first era of globalization to the effect of railways. Andrabi and Kuehlwein (2010) warn, however, that not all integration is necessarily a consequence of railway construction. They show sharp price convergence in British Indian grain markets between 1861 and 1920, but find that railways explain only 20 percent of the decline in price dispersion.

We undertake similar analyses for markets in South Africa before unification. Given South Africa’s closer international integration shown above, the aim here is to test the extent of market integration within the Colony in the period before unification in 1910, and to identify possible causes of such integration.

PRICE CONVERGENCE

To do this, we turn to a newly digitised source of monthly prices from the Agricultural Journal, published by the Department of Agriculture of the Cape Colony (Agricultural Journal 1889-

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1914).5 We construct two series from several editions of the Journal: the first includes monthly prices of eight products across twenty Cape Colony towns between 1897 and 19106, and the second includes monthly prices of twelve major South African towns (including Johannesburg and , the main economic centres of the Boer Republics) for 1889 to 1890 and again after unification, from 1912 to 1914.7 We first investigate average price levels, then to turn different measures of market integration.

A wheat price series is typically used to identify market integration (Shiue and Keller, 2007, Federico and Persson, 2007, Sharp and Weisdorf, 2013), a trend we followed above when we calculated the timing of South Africa’s first integration into world markets. The richness of the Agricultural Journal series, however, allows not only a comparison of price levels by town, but also allows for a reassessment of the usefulness of using wheat prices as a proxy for all trade. Figure 4 below, for example, shows an index of prices for the eight products, averaged over the twenty Cape Colony towns. A decline in prices is observed over the period, with six of the eight products less expensive at the end of the period. The South African War evidently has a large effect on prices – especially tobacco – and the effect of the war seem to linger for several seasons as production slowly returns to pre-war levels.

5 Prices are reported as the current (wholesale) rates of agricultural produce telegraphed by the civil commissioners, or the , of the towns included, and each price is printed in pounds, and pennies. 6 The eight products are wheat, mealies (corn), potatoes, fresh butter, eggs, mutton, beef and tobacco. The twenty Cape Colony towns are: Aliwal North, Beaufort West, Burghersdorp, Cape Town, Clanwilliam, Cradock, Dordrecht, East London, Graaff-Reinet, Graham’s Town, Kimberley, King William’s Town, Malmesbury, , , Port Elizabeth, Queen’s Town, , Vryburg and Worcester. No data is available for the following months: Jan97-Jun97, Sep197, Jul98-Dec98, Jan00-Jun00, Jul02- Aug02, Apr05-May05, Aug05, Jul06-Jun07, Nov08, Jul09-Dec09 and Apr10-May10. 7 The twelve South African towns are: Beaufort West, Bloemfontein (Orange Free State), Cape Town, (Natal), East London, Graham’s Town, Johannesburg (South African Republic), Kimberley, King William’s Town, (Natal), Port Elizabeth and Queen’s Town. No data is available for Jan89-Sep89 and Dec 90. 10

Figure 4: Index of five-month centred average for eight products, averaged over twenty Cape Colony towns, July 1897-December 1910 (July 1897 = 100).

What should be striking is the relatively low and stable price of wheat throughout the series; in fact, wheat prices suggest a highly integrated market. Even the war and the scorched earth tactics of much of the interior seem to cause little variation in wheat prices. This is probably due to the geography of production: the interior was predominantly a mealie-growing region, while the south-west Cape, unaffected by the war, was the primarily a wheat-growing region.8 Much wheat was also imported. Using wheat to measure market integration may thus result in a downward bias: by considering only wheat, a market may appear more integrated than what was indeed the case.

8 The South African Census of 1911 reports, for example, that 61% of all wheat was produced in the Cape Colony, while 79% of all mealies were produced in the two interior provinces of the Transvaal and Orange Free State. 11

Figure 5: Box plots of coefficients of variation for eight products over twenty towns

To investigate this bias more systematically, we first turn to a simple but popular (Federico, 2011) measure of market integration: the coefficient of variation, calculated by dividing the standard deviation with the mean. Figure 5 reports a box plot of the monthly CVs for each product. The box plots support our hypothesis informed by visual inspection: wheat prices suggest strong integration (median of 0.17). So, too, do mealie, mutton, and beef prices (0.19, 0.19 and 0.18). But fresh butter (0.25), eggs (0.26), potatoes (0.32) and especially tobacco (0.52) seem far less integrated. We therefore construct a basket of the eight products. The basket is based on the structure of a standard barebones-basket, adjusted for South African consumers (de Zwart, 2013, De Zwart, 2011, Allen et al., 2011).9 Figure 6 report the basket price series for each town in our dataset.

9 Wheat and mealies (corn) are weighted 32.93% each, potatoes 16.47%, fresh butter 1.86%, eggs 2.79%, mutton and beef 5.58% each and tobacco 1.86%. See also the appendix. 12

Figure 4: Prices for a basket of goods by town (centred moving-average), June 1897-December 1910. Source: Various volumes of the Agricultural Journal; own calculations.

The graph is cluttered (which is why we’ve removed the legend) but already provides a visual clue of the extent of price convergence in the series. Prices in pre- and immediately post-war towns seem volatile and uncorrelated; however, by 1907 price changes seem to be highly correlated and aligned in a tight band. We test this using the coefficients of variation, splitting the sample into three periods: 1897-1902, 1903 to 1906, and 1907 to 1910. The CV for the basket declines in each period, from a high of 0.16 before and during the war, to 0.12 between 1903 and 1906, to 0.10 for 1907 and after. The declining CV supports the visual suggestion of greater market interaction.

The coefficient of variation is useful as an aggregate measure of market integration, but says little about the extent of regional market integration. Price ratios offer an alternative at a more disaggregated level. Consider a commodity price series for towns and , and . The

price ratio between the two towns in month ( ) is then defined as . If the

two towns share the same market, one may require absolute convergence of prices, i.e. . This approach would be consistent with the analysis of CVs presented above. Nevertheless, much of the price ratio literature focuses on the looser concept of relative convergence, where prices in two towns sharing the same market may diverge even while ‘constraining’ one another. 13

Econometrically, we measure constraint by the presence of a stochastic trend in the data. If

contains a unit root, we conclude that the towns are not market-integrated: commodity prices in two towns sharing a market will not move arbitrarily far away from one another. Alternatively, towns in the same market will share a common trend in their prices so that the price ratio will be stationary. We investigate price ratios for the basket over all possible town pairs. This richer investigation helps us to identify towns where integration is stronger.

The price ratio confirms the aggregate results based on the coefficient of variation: while the mean remains constant across the three time periods (at 1.01), the standard deviation declines significantly, from 0.18 between 1897 and 1902, to 0.13 from 1903 to 1906, to 0.10 from 1907 to 1910. Another way to report this decline is to count the number of ‘integrated’ relationships as a proportion of the total number of relationships. Table 1 show the steady increase over the full sample in the number of pairwise observations between 0.95 and 1.0510, which we arbitrarily regard as the cut-off for an integrated market.11 Whereas only 18% of town pairs are integrated in the first period, by 1907-1910 the number of such integrated links had nearly doubled.

But price ratios also allow more disaggregated view, showing the regional shifts in market integration. We therefore split the sample into three geographic regions: towns in western part of the Cape Colony, towns in the eastern part of the Cape Colony, and the two towns of Kimberley and Vryburg in the interior beyond the Orange River. We report the pairwise relationships across all towns in a region and elsewhere, and only the relationships within a region.

Table 1: Integrated town pairs by region, 1897-1910 All (19 Eastern Cape Interior region (2 towns) region (6 towns) region (12 towns) towns) n all ratio n all ratio n all ratio n all ratio 1897 -1902 30 167 18.0% 14 91 15.4% 27 139 19.4% 5 35 14.3% Across 1903-1906 56 188 29.8% 27 99 27.3% 49 160 30.6% 12 37 32.4% all 1907-1910 60 171 35.1% 28 80 35.0% 52 150 34.7% 15 35 42.9% 1897-1902 3 15 20.0% 12 53 22.6% 0 1 0.0%

Within 1903-1906 3 15 20.0% 20 64 31.3% 0 1 0.0% region 1907-1910 2 10 20.0% 21 66 31.8% 1 1 100.0% Notes: n represents the number of pairwise relationships that fall between 0.95 and 1.052632 of all pairwise town relationships. Source: Various volumes of the Agricultural Journal; own calculations.

10 The latter is shortened for 1.052632 which is equal to the inverse of 0.95. 11 We also used 9.98 and 1.02 as cut-off. The results follow similar trends. 14

All three regions reveal greater integration over the period; from less than 20% integration across the Colony, integration increases to 35% for towns in the Western Cape and Eastern Cape regions. Surprisingly, there seems to be little regional integration: only 20% of Western Cape towns are integrated with each other, and this share remains constant over all three periods. While it is tempting to ascribe this to those towns not connected to the Cape railway, like Clanwilliam, or those connected via a privately-operated rail, like Mossel Bay, the pairwise relationships suggest that even towns on the main trunk route from Cape Town to Kimberley (Worcester and Beaufort West) remained poorly integrated.

Integration in the Eastern Cape did improve, at least until 1906. Integration on the trunk lines increase markedly as the railway was extended and fees fell; for the period 1903-1906, a high 39% of towns on the trunk lines are integrated with others in the Eastern Cape, although this falls to 33% in the following period, possibly because of the decline in competition from road transport reported above.

Rail transport seemed to predominantly favour the interior regions. Although we only have two towns in our sample, their integration with the rest of the Cape economy is evident from Table 1: whereas only 14% of pairwise relationships are strongly integrated before or during the War, this figure increases to 43% in the final period. By 1907, Kimberley and Vryburg was served by multiple lines running from the coast which explains the integration with towns in both the Western Cape and Eastern Cape regions. Rail transport clearly substituted expensive road transport to the interior, creating a single market for a number of goods transported along the rail network.

To further explore the extent of integration in a single market, we next turn to dynamic factor models (DFMs), which aim to identify common trends in a large set of time series. These models approximate the variance among a set of time series with a set of random walk processes, with much smaller than (Harvey, 1989). These unobservable random walks are called factors. Conventionally, factor models consider only contemporaneous relationships and DFMs were developed to also account for leading and lagging correlation. DFMs have been particularly popular in business cycle analysis, where it allows analysts to identify common trends across a range of regions or variables. It has also been introduced in the context of studying co- movement in economic history (Uebele, 2011).

We use a dynamic factor model to test the size of the common component in the Cape Colony market. In the context of agricultural products, seasonal adjustment of price data is necessary to prevent spurious inferences regarding co-movement. The data is therefore seasonally-adjusted

15 using time-series regression of individual town basket prices on period dummy variables. Alternative statistical methods for seasonal adjustment, such as the X12 census method, are difficult to apply given the existence of missing values.

We apply the DFMs to the seasonally-adjusted basket prices for the sample of Cape Colony towns. In particular, we first fit a DFM allowing for a single common factor across all towns. The results suggest that a common trend is present in each of the three periods 1897-1902, 1903- 1906 or 1907-1910. Figure 5 shows that the common trend increases in importance from the first to the third period, with some stagnation in the middle period.

100% 90% 80% 70% 60% 50% Local 40% Colony-wide & regional 30% 20% 10% 0% 1897-1902 1903-1906 1907-1910

Figure 5: National and provincial versus local variance shares in the Cape Colony, 1897-1910

We next split the effects by Cape Colony region. The results, reported in Figure 6, remain the same, although the interior region (Kimberley and Vryburg) appears to have greater co- movement with the rest of the Cape Colony than towns in the other regions.

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100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Western Western Western Eastern Eastern Eastern Interior Interior Interior Cape: Cape: Cape: Cape: Cape: Cape: region: region: region: 1897-19021903-19061907-19101897-19021903-19061907-19101897-19021903-19061907-1910

Colony-wide & regional Local

Figure 6: National and provincial versus local variance shares for three regions in the Cape Colony, 1897-1910

On average, our results suggest that co-movement across the Cape Colony became more important. It is not clear, however, to what extent regional integration was more important than Colony-wide integration. It seems that the integration was driven more strongly by overall co- movement across the Colony, than by regional integration. This supports the anecdotal evidence that railroads had a greater effect on long-distance road transport, compared to the shorter distances where transport riders remained competitive until legislation was required to protect the rail investment by the government.

100% 90% 80% 70% 60% Local 50% 40% Regional 30% Colony 20% 10% 0% Eastern Cape region Western Cape Interior region region

Figure 7: National and provincial versus local variance shares in the Cape Colony, 1907-1910

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Nevertheless, the common factors explain only about 20% of the variance by 1907-1910. This suggests that while co-movement may have become more important, prices were certainly not becoming tightly integrated.

The four unified under the banner of the in 1910. The Agricultural Journal continues to be published after unification, but the towns and products included, and even the units of measured, change. We therefore construct a new basket of goods for the period March 1912 to August 1914 for 12 towns that include all the major centres of production.12 The coefficient of variation for the basket is a low 0.093, suggesting that the market is highly integrated. It should be noted that all the towns included in the new list were either main centres of mining in the interior or located on the trunk lines serving the interior markets (see Figure 3).

The new data allows us to compare the same basket to a sample constructed from the 1907- 1910 series. These results provide evidence that co-movement became more important, although the order of magnitude remains around 20%. A more interesting result, however, emerges from the sample period 1912-1914, which includes data for towns in the interior. We fit a single common factor DFM on this time-period as well, and find much stronger evidence of co- movement than using only Cape Colony-data (Figure 8). The results are also robust across the different regions within the Cape Colony. It indicates that the slow but steady integration suggested by the 1897-1910 data above reflected integration at a national rather than at a regional level.

12 The twelve towns included are Beaufort West, Bloemfontein, Cape Town, Durban, East London, Graham’s Town, Johannesburg, Kimberley, Kingwilliamstown, Pietermaritzburg, Port Elizabeth and Queen’s Town, and the five commodities used to construct the basket are mealies (60%), potatoes (20%), fresh butter (10%) and eggs (10%). 18

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Cape Colony: Cape Colony: Cape Colony Eastern Cape Eastern Cape Eastern Cape 1907-1910 1912-1914 (expanded towns: 1907- towns: 1912- towns dataset): 1912- 1910 1914 (expanded 1914 dataset): 1912- 1914

National & provincial Local

Figure 8: National versus local variance shares in the Cape Colony, 1907-1914

We also fit a four-factor DFM, where we allow for a national common trend as well as specific trends for towns in the Cape Colony, in Natal (Durban and Pietermaritzburg) and in the former Boer Republics (Bloemfontein and Johannesburg). This model suggests a negligible role for the regional factors, with the common national factor explaining most of the variance (about 32%).

100% 90% 80% 70% 60% Local 50% 40% Provincial 30% National 20% 10% 0% Cape Colony Natal Former Boer Republics

Figure 9: National, provincial and local variance shares in the Union of South Africa, 1912-1914

The integration identified earlier in the Cape Colony data for 1897-1910 was therefore indicating national rather than regional integration. The evidence, however, is more nuanced when considering each province separately. Figure 9 shows the national, provincial and local variance shares for the former British colonies, the Cape Colony and Natal, and the former Boer

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Republics for the small basket prices in the period 1912-1914. Note how relatively unimportant provincial integration is in the case of the Cape Colony compared to the role of the national common factor. Provincial common factors are far more important in the case of Natal (which consists of the two major towns Pietermaritzburg and Durban) and the former Boer Republics (represented by major towns Bloemfontein and Johannesburg).

CONCLUSION

Before the discoveries of minerals in the South African interior, the Cape economy remained largely isolated from movements in global markets. From the 1870s, however, we show that wheat prices in the UK and in Cape Town are highly correlated, suggesting that the South African economy followed the international trend of increasing globalisation.

Yet the focus on the integration of wheat prices between global markets and that of Cape Town, we posit, may offer a misguided idea of the extent of integration in the rest of the Cape Colony and, later, South Africa. We show using a monthly price series of a basket of eight commodities that the Cape Colony market was highly fractionalised before and during the Second South African War, and in its immediate aftermath. Only during the middle of the first decade of the twentieth century did prices begin to exhibit the characteristics of an integrated market: a decline in prices or, in other words, a convergence to the law of one price, and co-movement after adjusting for seasonal variation. We find support for these results by considering the coefficients of variation, the price ratios and a dynamic factor model for a basket of goods.

An important determinant of the greater integration was the contribution of the railways. We show that the main effect of the railways was, predictably, to connect the interior centres of production with those on the coast, creating a larger national component. The railways mattered little for regional integration, possibly because, as we see from the anecdotal evidence, it entered an already highly competitive market.

The greater integration of the South African market associated with the introduction of railways, and the decline of long-distance road transport, also affected the distribution of income between Black, for whom transport riding was an important source of employment, and White, who benefited from the lower long-distance transport costs of the rails. In fact, Colin Bundy argues that “perhaps the most important variable introduced into structural relations after the mineral discoveries was the relative ease of access of capitalist white farmers and peasant farmers to markets” (Bundy, 1972: 387).

20

Yet, as with the impact of railways in , the extensive rail network did not immediately create a fully integrated market and its impact should therefore not be overemphasised. We show that when considering a basket of goods instead of relying on only one commodity, our coefficients of variation remain quite high, at least given similar coefficients calculated elsewhere. We also show that our factor model finds at most 40% of a national component over the entire period, suggesting that more than half of the variation is explained by local circumstances.

21

Appendix

Our first DFM allows for a single common factor across the various towns in the Cape Colony. We use a state-space framework to estimate the factor model. This requires the specification of a measurement equation, which deals with observable data, and a state equation, which specifies the behaviour of the unobserved common factor. The measurement equation decomposes a commodity’s price in town ( ) in month ( ) as follows:

(1)

where is the common factor, identical across all towns, and the so-called factor loading, determining how strongly price in town co-moves with the common factor. In the empirical analysis, we standardize the data, which implies that for all . represents an idiosyncratic element unique to every town.

As the factor is unobservable, we estimate equation (1) using a state-space model where we describe the behaviour of the factor as a simple AR(1)13 process:

(2)

We have considered higher order AR processes, but these do not significantly increase the explanatory power of the DFM (measured by the Akaike information criterion). Furthermore, note that we already use five-month centred moving averages in our data to reduce noise in the price series and to reduce the number of missing observations.

The original dynamic factor models rely on direct numerical optimization to obtain maximum likelihood parameter estimates. This approach limits analysis to a small number of long time series (Harvey, 1989, Molenaar, 1985, Molenaar et al., 1992). Zuur et al (2003) suggest an alternative EM algorithm for optimization, allowing for a larger number of time series. We follow the latter approach.

Factor analysis decomposes the variance of price in town ( ) as follows:

(3)

13 22

Equivalently, given the use of standardised data, (3) can be re-expressed in terms of correlation

:

(4)

One can then use the estimated factor loadings to calculate what proportion of overall variance or correlation is explained by the common factor. In particular, the proportion of total sample (standardised) variance due to the common factor can be calculated as:

∑ (5)

Table 1: Market sizes

1865 1875 1891 1904 1911 1921 Total 1276242 1519488 Cape of Good Hope 582377 650609 Natal 98114 136838 Transvaal 420562 543485 Orange Free State 175189 188556

Aliwal North 3953 3543 5280 6520 6795 6386 Beaufort West 2623 3738 7580 10007 5198 6011 Burghersdorp Cape Town 25861 30730 27928 52212 64619 81983 Clanwilliam 2231 3018 7393 10576 5203 5847 Colesberg 3485 4521 8122 9824 3728 3716 Cradock 5924 5967 8093 9188 6128 5957 Dordrecht East London 3773 7987 20545 18146 24091 Graaff-Reinet 6013 7356 10872 13880 7255 7402 Graham's Town Kimberley 20187 20400 20876 21607 King William's Town 9012 9256 11216 10333 10812 Malmesbury 6514 7862 10077 13607 13975 14807 Mossel Bay 2158 2664 5965 6653 Port Alfred Port Elizabeth 7131 9309 13939 23892 20755 27236 Queen's Town 3650 6228 8116 10859 7285 8743 Tarkastad 3141 2648 2259 Vryburg 5537 4296 5614 Worcester 3159 4093 10883 15703 7381 8837

Bloemfontein 26147 30034 Durban, Natal 38317 57993

23

Johannesburg 121857 153878 Pietermaritzburg, Natal 15895 18889

Table: Freight rates from five Southern African harbours to towns, 1911

CT Rel PE Rel EL Rel D Rel LM Rel Aliwal North 48 96 30 100 25 89 Beaufort West 24 48 33 110 35 125 Bloemfontein 50 100 30 100 28 100 33 100 69 100 Burghersdorp 44 88 26 87 21 75 Cape Town 50 167 53 189 Clanwilliam Colesberg 41.5 83 26.5 88 28.5 102 Cradock 41.25 83 17.25 58 20.25 72 Dordrecht 50 100 32 107 24 86 Durban, Natal East London 53 106 24 80 Graaff-Reinet 43.25 87 17.25 58 28.25 101 Graham's Town 51 102 12 40 Johannesburg 54.5 109 38.5 128 35.5 108 34 49 Kimberley 41.25 83 33.25 111 39.25 119 King William's Town 51 102 21 70 Malmesbury 7 14 Mossel Bay Pietermaritzburg 8 24 Port Alfred Port Elizabeth 50 100 50 152 63 126 44 147 32.25 47 Queen's Town 46 92 28 93 Tarkastad 49.25 99 32.25 108 Vryburg 47.5 95 39.5 132 Worcester 12 24 Source: Glanville (1911); own calculations. Prices are pence per 100 lb of product per rail. Rel measures the relative cost to Bloemfontein.

Table: Coefficients of correlation for eight products over twenty towns

Fresh CVs Wheat Mealies Potatoes butter Eggs Mutton Beef Tobacco N 123 123 123 123 123 123 123 123 Mean 0.17 0.22 0.32 0.25 0.25 0.19 0.19 0.57 Median 0.17 0.19 0.32 0.25 0.26 0.19 0.18 0.52

Table 2: Average price by product (5-month centred average for twenty towns)

Fresh Wheat Mealies Potatoes butter Eggs Mutton Beef Tobacco

Jul1897 144.3 97.8 159.0 22.3 17.4 6.0 5.1 6.3

24

Aug1897 145.6 100.7 163.4 22.9 15.7 6.3 5.2 5.7 Sep1897 145.8 103.7 169.6 23.5 15.5 7.4 6.2 5.7 Oct1897 147.5 107.4 175.9 24.9 15.0 7.8 6.5 5.5 Nov1897 150.9 111.6 166.8 26.2 15.8 7.7 6.5 5.3 Dec1897 148.7 113.7 163.3 25.0 16.3 7.7 6.5 5.8 Jan1898 149.7 115.0 157.0 23.8 17.6 7.7 6.6 6.2 Feb1898 150.7 114.4 154.0 22.8 19.0 7.5 6.6 6.4 Mar1898 151.7 113.9 149.4 21.6 21.2 7.3 6.6 6.4 Apr1898 153.6 115.0 150.7 20.1 23.0 7.1 6.6 6.5 May1898 156.2 112.8 149.0 19.8 24.2 7.0 6.4 6.2 Jun1898 157.6 112.7 149.8 20.3 25.7 6.9 6.4 6.0 Jul1898 162.3 116.3 148.9 20.6 27.3 6.7 6.3 5.9 Aug1898 166.5 124.7 150.0 21.7 27.9 6.6 6.2 6.0 Sep1898 Oct1898 Nov1898 146.0 124.3 167.8 19.9 17.6 8.3 7.9 7.8 Dec1898 142.1 122.7 155.5 19.6 18.1 8.1 7.9 7.7 Jan1899 138.7 119.9 149.7 19.3 18.1 8.1 7.8 7.8 Feb1899 136.6 119.2 145.1 18.6 19.4 8.0 7.8 7.5 Mar1899 135.0 119.7 143.9 18.1 20.8 8.0 7.8 7.6 Apr1899 131.5 114.5 136.8 17.3 22.7 8.0 8.0 7.5 May1899 130.5 117.3 141.0 16.9 23.7 7.8 7.8 7.7 Jun1899 130.7 116.4 153.9 17.3 23.2 7.9 7.9 7.7 Jul1899 130.3 115.9 162.6 18.0 20.9 7.9 7.7 7.8 Aug1899 131.2 116.5 167.2 18.8 18.5 7.9 7.7 7.7 Sep1899 134.8 118.5 172.4 19.8 16.6 8.1 7.9 7.9 Oct1899 138.4 117.2 177.1 21.0 15.5 8.3 8.0 7.9 Nov1899 139.6 115.9 176.7 21.6 15.4 8.3 8.1 8.0 Dec1899 145.6 119.1 177.6 22.0 16.0 8.5 8.3 7.9 Jan1900 158.0 122.4 188.5 24.3 18.1 8.7 8.8 8.4 Feb1900 157.2 120.6 202.0 26.2 19.2 9.2 8.7 9.4 Mar1900 Apr1900 May1900 137.0 119.0 220.7 23.0 25.0 9.2 9.0 12.5 Jun1900 139.0 128.6 213.2 24.6 22.6 8.5 8.4 15.4 Jul1900 140.1 135.7 221.1 25.5 21.5 8.5 8.4 15.1 Aug1900 142.0 136.1 233.4 25.9 20.3 8.6 8.4 15.0 Sep1900 142.8 140.0 240.7 26.6 20.1 8.7 8.4 14.2 Oct1900 146.0 142.5 248.1 27.1 20.9 8.6 8.4 14.4 Nov1900 147.0 145.3 247.8 26.5 20.8 8.8 8.7 14.5 Dec1900 149.4 144.2 236.6 25.2 21.7 8.8 8.7 14.3 Jan1901 150.0 139.3 229.5 22.8 23.1 8.5 8.7 14.4 Feb1901 153.5 139.3 220.3 21.6 26.0 8.4 8.8 14.8 Mar1901 151.4 140.7 210.9 20.0 28.1 8.4 8.8 15.8 Apr1901 150.2 133.5 191.3 21.1 31.2 8.3 8.9 16.1 May1901 147.7 129.2 195.3 22.1 30.7 8.3 8.8 16.9 Jun1901 146.4 125.6 194.6 24.4 29.9 8.1 8.8 18.2

25

Jul1901 144.6 122.5 208.0 25.8 27.7 8.0 8.9 17.8 Aug1901 144.2 120.6 219.2 27.3 25.4 8.2 8.8 17.8 Sep1901 143.3 125.0 227.5 27.9 23.6 8.3 8.9 17.3 Oct1901 142.3 126.9 225.1 27.4 23.7 8.3 8.8 16.6 Nov1901 140.2 129.7 227.7 27.2 24.0 8.6 8.8 15.2 Dec1901 139.6 130.7 213.0 25.9 24.8 8.7 8.7 17.0 Jan1902 140.0 130.4 185.6 24.4 25.7 8.5 8.7 18.8 Feb1902 138.9 130.0 172.6 23.3 27.1 8.4 8.6 19.2 Mar1902 137.9 133.3 175.6 23.4 28.7 8.4 8.6 18.4 Apr1902 137.1 147.4 181.7 23.2 30.0 8.4 8.7 17.2 May1902 137.3 149.9 186.7 22.8 30.4 8.3 8.6 16.6 Jun1902 133.5 153.7 195.8 22.1 31.7 8.3 8.6 16.3 Jul1902 144.3 156.3 212.7 25.1 29.8 8.7 9.0 15.4 Aug1902 145.7 155.9 228.7 27.5 26.0 8.8 9.2 15.1 Sep1902 148.5 157.3 243.4 27.1 22.3 9.1 9.6 14.9 Oct1902 149.0 160.3 249.1 26.0 22.5 9.3 9.6 14.8 Nov1902 150.5 160.8 247.4 25.2 23.0 9.3 9.6 14.6 Dec1902 151.5 163.6 244.5 23.3 22.7 9.3 9.7 14.6 Jan1903 149.6 162.1 236.1 22.5 23.6 9.3 9.7 15.2 Feb1903 148.7 145.2 225.1 21.5 24.9 9.3 9.8 15.2 Mar1903 147.7 143.2 215.6 22.0 26.6 9.3 9.8 15.1 Apr1903 144.8 149.6 212.4 22.2 28.5 9.3 9.8 15.0 May1903 143.9 149.3 215.8 22.9 30.4 9.3 9.9 15.2 Jun1903 144.0 155.3 219.5 23.8 31.2 9.4 10.0 14.6 Jul1903 142.8 151.0 222.8 24.9 30.8 9.3 10.2 15.1 Aug1903 139.8 149.7 225.3 25.8 29.0 9.5 10.3 15.2 Sep1903 144.0 142.7 229.0 26.9 26.5 9.7 10.5 15.2 Oct1903 143.2 138.2 236.7 27.8 24.3 10.0 10.7 14.8 Nov1903 143.6 134.9 219.4 26.5 22.7 10.4 10.9 13.8 Dec1903 139.7 131.7 199.3 25.3 22.4 10.5 10.6 13.8 Jan1904 141.6 131.5 177.8 23.2 23.0 10.4 10.6 13.5 Feb1904 134.9 132.8 151.9 20.8 23.7 10.3 10.4 13.5 Mar1904 133.1 130.5 122.4 18.7 24.8 10.0 10.2 13.1 Apr1904 130.4 127.5 110.1 18.3 26.3 9.7 9.8 12.9 May1904 131.6 128.3 106.2 17.8 26.2 9.7 9.9 12.6 Jun1904 129.1 117.3 102.6 18.9 24.7 9.3 9.4 12.4 Jul1904 128.5 110.4 102.5 20.7 22.8 8.8 9.2 12.0 Aug1904 126.9 106.8 105.5 22.7 20.4 8.6 8.9 11.9 Sep1904 126.4 102.0 110.1 23.6 18.1 8.5 8.8 11.7 Oct1904 127.2 100.3 111.9 23.8 17.2 8.5 8.8 12.2 Nov1904 131.4 99.3 114.1 24.4 17.4 8.5 8.7 12.8 Dec1904 131.6 96.8 112.1 24.5 18.4 8.6 8.6 12.8 Jan1905 131.1 96.6 113.0 23.7 20.0 8.6 8.7 12.7 Feb1905 130.0 96.6 116.7 22.3 20.9 8.6 8.7 13.7 Mar1905 129.1 97.5 116.5 21.7 21.4 8.7 8.8 13.9 Apr1905 127.5 98.7 116.5 20.2 24.1 8.6 8.6 13.2 May1905 128.0 99.8 123.5 19.0 26.3 8.4 8.6 13.1

26

Jun1905 127.1 100.1 126.0 18.4 27.5 8.3 8.4 13.0 Jul1905 126.8 98.8 125.0 19.1 23.5 8.0 8.1 12.5 Aug1905 127.4 97.7 128.2 19.5 21.0 7.9 8.0 12.5 Sep1905 128.8 94.9 141.0 19.5 18.0 7.6 7.8 12.3 Oct1905 129.7 93.2 152.4 18.6 14.8 7.5 7.7 11.6 Nov1905 129.9 94.0 152.4 18.6 15.0 7.5 7.7 11.4 Dec1905 128.4 94.8 150.3 18.0 15.3 7.4 7.6 10.8 Jan1906 124.4 96.5 144.4 17.2 16.4 7.4 7.6 11.6 Feb1906 120.4 98.5 133.0 16.9 17.6 7.3 7.5 11.2 Mar1906 117.9 99.6 122.6 16.9 19.7 7.2 7.4 11.2 Apr1906 115.4 100.5 114.9 16.5 21.9 7.1 7.3 11.2 May1906 114.7 101.0 113.6 16.5 23.1 7.1 7.2 11.2 Jun1906 113.4 101.0 117.3 16.7 24.4 7.0 7.1 10.1 Jul1906 114.8 101.4 118.5 17.0 25.9 7.0 7.2 10.1 Aug1906 116.1 101.6 112.3 17.3 26.6 7.1 7.1 10.5 Sep1906 Oct1906 Nov1906 Dec1906 Jan1907 Feb1907 Mar1907 Apr1907 May1907 98.4 73.3 98.6 17.9 21.4 6.7 7.0 9.4 Jun1907 103.8 73.8 100.2 17.9 19.1 6.4 6.9 11.8 Jul1907 105.3 70.6 96.0 18.9 16.5 6.3 6.7 11.0 Aug1907 107.4 70.3 95.4 19.5 15.0 6.3 6.6 10.7 Sep1907 111.2 71.1 94.0 19.4 14.2 6.3 6.7 10.1 Oct1907 120.3 72.6 93.7 19.7 12.5 6.2 6.6 9.7 Nov1907 125.2 74.5 95.4 19.5 11.8 6.2 6.6 8.6 Dec1907 130.9 77.6 94.8 18.2 12.5 6.2 6.6 8.8 Jan1908 132.9 80.4 93.6 17.3 13.6 6.1 6.6 9.0 Feb1908 132.9 84.3 96.5 17.3 15.6 6.1 6.5 8.9 Mar1908 132.9 86.0 97.0 17.0 17.9 6.0 6.4 9.0 Apr1908 132.1 87.3 96.3 17.3 20.1 5.8 6.2 9.0 May1908 131.7 88.4 99.6 18.5 21.3 5.6 5.8 8.8 Jun1908 133.7 89.1 107.0 19.2 21.2 5.5 5.7 9.3 Jul1908 135.8 88.8 115.4 19.4 19.3 5.4 5.6 9.4 Aug1908 135.4 88.2 129.5 20.1 16.5 5.5 5.5 9.6 Sep1908 136.3 88.3 136.3 20.5 14.7 5.5 5.4 10.0 Oct1908 136.9 94.6 140.6 19.5 12.4 5.7 5.6 10.4 Nov1908 137.0 104.9 134.5 18.8 11.8 5.7 5.8 10.1 Dec1908 135.5 110.6 124.5 17.2 12.3 5.8 5.9 10.0 Jan1909 130.8 116.3 103.1 15.1 13.8 5.6 5.9 10.0 Feb1909 128.0 113.8 101.4 14.6 14.7 5.5 5.8 10.0 Mar1909 126.2 109.4 92.9 14.3 16.4 5.4 5.7 10.3 Apr1909 127.4 98.4 93.5 14.2 18.2 5.1 5.5 9.3

27

May1909 127.2 95.9 93.0 14.3 19.0 5.0 5.4 9.3 Jun1909 129.8 91.3 94.6 14.2 19.8 5.0 5.3 9.4 Jul1909 134.8 83.4 94.9 14.6 20.9 4.9 5.3 9.0 Aug1909 139.3 80.1 100.3 15.9 22.1 4.8 5.2 8.8 Sep1909 Oct1909 Nov1909 139.8 71.8 96.5 18.0 13.9 4.3 5.0 7.4 Dec1909 132.6 73.1 89.0 15.4 14.0 4.3 5.0 7.9 Jan1910 130.5 73.2 84.4 14.2 14.2 4.3 5.0 7.9 Feb1910 130.5 73.2 84.4 14.2 14.2 4.3 5.0 7.9 Mar1910 130.5 73.2 84.4 14.2 14.2 4.3 5.0 7.9 Apr1910 124.9 71.3 78.8 13.5 16.9 4.3 5.0 8.0 May1910 123.3 69.6 79.5 14.7 18.2 4.5 5.0 7.8 Jun1910 121.4 68.8 80.5 16.3 18.8 4.4 4.9 8.1 Jul1910 121.1 68.4 80.3 16.7 17.4 4.5 5.0 8.2 Aug1910 121.1 68.2 80.6 17.9 15.8 4.6 5.2 8.2 Sep1910 121.4 68.7 83.0 18.7 13.7 4.8 5.4 8.3 Oct1910 121.3 68.6 85.5 18.6 12.3 4.9 5.5 8.5 Nov1910 121.5 67.8 87.8 19.0 11.3 5.0 5.7 8.5 Dec1910 122.1 68.1 89.8 19.4 10.7 5.2 5.7 8.4 Source: Various editions of the Agricultural Journal (Department of Agriculture, 1889-1914); own calculations

Table: Creating a basket of goods

Fresh Wheat Mealies Potatoes butter Eggs Mutton Beef Tobacco

Basket weights 70.8 70.8 35.4 4 6 12 12 4 Percentage of basket 32.93 32.93 16.47 1.86 2.79 5.58 5.58 1.86 per 100 per 100 per bag of per Units lbs lbs 165 lbs per lbs dozen per lbs per lbs per lbs Convert to 100 lbs 1 1 0.61 100 156 100 100 100 Source: De Zwart (2011; 2013), own adjustments

Table: International comparisons

Region Date Markets CV Source All European markets 1868-1870 113 0.13 Federico (2011), p. 102 Austria-Hungary 1868-1870 5 0.07 Federico (2011), p. 102 Belgium 1868-1870 4 0.12 Federico (2011), p. 102 France 1868-1870 35 0.06 Federico (2011), p. 102 Germany 1868-1870 20 0.06 Federico (2011), p. 102 Italy 1868-1870 13 0.20 Federico (2011), p. 102 Netherlands 1868-1870 6 0.07 Federico (2011), p. 102 Spain 1868-1870 8 0.08 Federico (2011), p. 102 Sweden 1868-1870 12 0.07 Federico (2011), p. 102 Switzerland 1868-1870 2 0.07 Federico (2011), p. 102 United Kingdom 1868-1870 5 0.04 Federico (2011), p. 102 28

Federico and Persson (2007), p. 94; Jacks (2005), United States 1911-1913 12 0.02 p. 389 Pune (India) 1870-1914 Unknown 0.19 Studer (2008), p. 417 Calcutta (India) 1870-1914 Unknown 0.14 Studer (2008), p. 417 Delhi (India) 1870-1914 Unknown 0.18 Studer (2008), p. 417 Cape Colony 1897-1910 20 0.17 Own South Africa 1889-1892 10 0.25 Own South Africa 1912-1914 12 0.18 Own Sources: (Federico, 2011, Federico and Persson, 2007, Jacks, 2005, Studer, 2008)

Table: Dynamic factor model results (basket of goods) 1897-1906 1907-1910 Aliwal North 0.222 0.384 Burghersdorp 0.248 0.452 Clanwilliam 0.175 0.262 Cradock 0.248 0.416 Dordrecht 0.201 0.360 East London 0.275 0.380 Graaff Reinet 0.254 0.438 Grahams Town 0.270 0.382 Kimberley 0.283 0.296 Kingwilliamstown 0.272 0.373 Beaufort West 0.268 0.402 Malmesbury 0.260 0.426 Mossel Bay 0.004 0.380 Port Alfred 0.179 0.292 Port Elizabeth 0.293 0.408 Queens Town 0.290 0.337 Worcester 0.271 0.314 Cape Town 0.289 1.000 Tarkastad 0.232 0.322 Vryburg 0.213 0.377 Sum 4.749 8.000 Sum without Cape Town 4.459 7.000 Proportion of variance explained by common factor 0.237 0.400 Proportion of variance explained by common factor (ex CT) 0.235 0.368

29

Table: Pair-wise price ratios, 1897-1902 BW BU CT CW CR DO EL GR GT KI KW ML MB PA PE QT TS VB WO Aliwal North 1.11 0.97 0.99 0.87 1.14 0.83 1.21 0.99 1.13 1.11 0.92 0.89 0.79 1.05 0.97 0.98 1.21 0.89 Beaufort West 0.94 0.91 0.81 1.10 0.81 1.13 0.94 1.09 1.05 0.87 0.84 0.78 0.92 0.93 1.21 0.84 Burghersdorp 0.99 0.90 1.11 0.90 1.21 1.03 1.15 1.18 0.94 0.91 0.80 1.16 1.00 0.99 1.25 0.92 Cape Town 0.86 1.13 0.88 1.23 1.03 1.13 1.16 0.96 0.91 0.83 1.15 1.01 1.01 1.32 0.93 Clanwilliam 1.42 1.08 1.48 1.26 1.29 1.36 1.15 1.07 1.01 1.22 1.18 1.46 1.05 Cradock 0.84 1.12 0.95 0.94 1.04 0.84 0.78 0.72 1.12 0.91 0.91 1.09 0.85 Dordrecht 1.40 1.18 1.32 1.31 1.11 1.03 0.88 1.28 1.13 1.13 1.34 1.05 East London 0.83 0.92 0.94 0.79 0.73 0.65 0.94 0.81 0.81 1.08 0.76 Graaff Reinet 1.15 1.15 0.94 0.88 0.79 1.18 0.99 0.99 1.28 0.90 Grahams Town 1.01 0.84 0.79 0.75 0.88 0.90 1.06 0.79 Kimberley 0.82 0.79 0.70 1.02 0.87 0.87 1.09 0.78 Kingwilliamstown 0.96 0.87 1.06 1.08 1.40 0.96 Malmesbury 0.94 1.42 1.13 1.12 1.43 0.99 Mossel Bay 1.62 1.25 1.25 1.62 1.14 Port Alfred Port Elizabeth 0.84 0.86 1.00 0.80 Queens Town 1.01 1.29 0.92 Tarkastad 1.29 0.92 Vryburg 0.74

Table: Pair-wise price ratios, 1903-1906 BW BU CT CW CR DO EL GR GT KI KW ML MB PA PE QT TS VB WO Aliwal North 0.95 0.97 0.85 0.84 0.88 0.88 1.05 0.85 0.95 0.91 0.81 0.77 0.98 0.81 0.87 0.86 0.94 0.80 0.78 Beaufort West 0.96 0.89 0.88 0.89 0.95 1.12 0.90 1.01 0.96 0.85 0.82 1.04 0.87 0.96 0.91 1.00 0.85 0.82 Burghersdorp 0.95 1.02 1.39 1.01 1.20 0.93 1.11 1.04 0.93 0.89 0.90 0.98 1.01 0.92 0.86 Cape Town 0.98 1.03 1.08 1.25 1.02 1.11 1.08 0.95 0.91 1.16 0.98 1.08 1.02 1.12 0.94 0.92 Clanwilliam 1.05 1.12 1.28 1.04 1.16 1.10 0.98 0.94 1.21 1.03 1.14 1.05 1.16 0.97 0.94 Cradock 1.01 1.19 0.99 1.01 1.03 0.90 0.90 1.18 0.94 1.09 0.96 1.11 0.92 0.92

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Dordrecht 1.18 0.95 1.06 1.01 0.90 0.85 1.13 0.93 1.01 0.96 1.06 0.88 0.87 East London 0.81 0.89 0.86 0.77 0.73 0.94 0.79 0.85 0.81 0.90 0.76 0.74 Graaff Reinet 1.12 1.07 0.95 0.91 1.16 0.96 1.05 1.01 1.11 0.94 0.91 Grahams Town 0.98 0.86 0.83 1.05 0.92 1.03 0.92 0.99 0.86 0.82 Kimberley 0.89 0.85 1.09 0.92 0.98 0.95 1.05 0.88 0.86 Kingwilliamstown 0.96 1.23 1.04 1.16 1.07 1.18 0.99 0.96 Malmesbury 1.27 1.09 1.21 1.12 1.24 1.03 1.01 Mossel Bay 0.84 0.91 0.89 0.98 0.83 0.80 Port Alfred 1.08 1.03 1.17 0.96 0.95 Port Elizabeth 0.99 1.10 0.93 0.87 Queens Town 1.11 0.93 0.91 Tarkastad 0.85 0.83 Vryburg 0.98

Table: Pair-wise price ratios, 1907-1910 BW BU CT CW CR DO EL GR GT KI KW ML MB PA PE QT TS VB WO Aliwal North 1.13 1.01 0.92 1.07 0.90 1.08 1.01 1.05 0.94 0.91 1.01 1.03 0.92 1.10 1.00 1.09 1.00 0.97 Beaufort West 0.87 0.81 0.92 0.81 0.97 0.88 0.94 0.85 0.81 0.90 0.92 0.82 0.95 0.89 0.97 0.89 0.87 Burghersdorp 0.95 1.09 0.92 1.10 1.01 1.08 0.97 0.93 1.04 1.06 0.94 1.11 1.01 1.10 1.03 0.97 Cape Town Clanwilliam 1.12 1.01 1.22 1.08 1.18 1.08 1.02 1.12 1.11 1.03 1.17 1.13 1.21 1. 11 1.08 Cradock 0.85 1.02 0.95 1.01 0.94 0.88 0.97 1.00 0.87 1.03 0.95 1.06 0.96 0.92 Dordrecht 1.20 1.12 1.17 1.06 1.01 1.11 1.15 1.02 1.23 1.11 1.21 1.10 1.08 East London 0.93 0.97 0.87 0.84 0.93 0.96 0.85 1.03 0.93 1.01 0.92 0.90 Graaff Reinet 1.06 0.96 0.92 1.02 1.04 0.91 1.09 1.00 1.11 1.01 0.96 Grahams Town 0.90 0.86 0.96 0.97 0.87 1.05 0.95 1.04 0.95 0.93 Kimberley 0.96 1.06 1.07 0.98 1.15 1.07 1.14 1.05 1.05 Kingwilliamstown 1.11 1.12 1.01 1.21 1.11 1.20 1.10 1.08 Malmesbury 1.02 0.91 1.08 1.00 1.09 0.99 0.97 Mossel Bay 0.89 1.07 0.98 1.09 0.99 0.95

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Port Alfred 1.23 1.10 1.20 1.09 1.07 Port Elizabeth 0.89 0.98 0.92 0.87 Queens Town 1.08 1.00 0.97 Tarkastad 0.92 0.90 Vryburg 0.98

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