Black holes in African trade data - Imani Development … · Union (SACU) trade data since 2010....

Black holes in African trade data

by Cyril Prinsloo and Ron Sandrey

tralac Working Paper

No. S15WP10/2015

July 2015

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acknowledged. All views and opinions expressed remain solely those of the authors and do not purport to reflect the views of tralac.

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Copyright © tralac, 2015.

Readers are encouraged to quote and reproduce this material for educational, non-profit purposes,

provided the source is acknowledged. All views and opinions expressed remain solely those of the

authors and do not purport to reflect the views of tralac

This publication should be cited as: Prinsloo, C. and Sandrey, R. 2015.

Black holes in African trade data. Stellenbosch: tralac.

This publication has been financed by The Swedish Embassy Nairobi. The Swedish Embassy Nairobi

does not necessarily share the views expressed in this material. Responsibility for its contents rests

entirely with the author.

www.tralac.org | [email protected] | Twitter @tradelawcentre

Readers are encouraged to quote and reproduce this material for educational, non-profit purposes,

provided the source is acknowledged. All views and opinions expressed remain solely those of the authors

and do not purport to reflect the views of tralac.


tralac Working Paper | S15WP10/2015 | Authors: Cyril Prinsloo and Ron Sandrey

3

Key points and summary

Collating and analysing African trade data is an exercise fraught with difficulties. In many instances

this data is not available from its primary source, and this leads to the use of so-called mirror data or

data assessed by examining the mirror of the trade partner data. While this is a very good proxy, it

does lend itself to some interpretational problems (such as when neither partner reports). In addition,

there are many other issues that one encounters such as transhipment of goods across boundaries, data

classification differences, whether or not the costs of transport and associated costs are included in the

import values, and the sensitivities associated with some goods that lead to a country not reporting the

full picture (South African gold trade, for example). These difficulties persist despite organisations,

such as the International Trade Centre (ITC), making great strides in improving data availability and

accuracy.

This is an important issue as politicians and trade officials and practitioners rely upon accurate data

for sound policy-making outcomes. The problem is that in examining trade data we find numerous

examples of where data seems to disappear into a ‘black hole’. Often, as in seemingly unnatural

phenomena, a logical explanation can be found but, conversely, at other times no apparently logical

explanation offers itself.

The objective of this paper is to peruse African trade data and explore some of its mysteries. We

appreciate that a case cannot be proved or disproved by offering examples, but we consider that these

examples at least help to explain some of the seemingly contradictory findings from trade data. The

emphasis is on intra-African trade data.

Overall, African trade data now reconciles relatively well at the big-picture level of aggregate country

reporting when either direct or mirror data is used. Nevertheless, some glaring gaps remain. This

applies especially to the commodity analysis, and further investigation shows that these two aspects of

intra-country and commodity problems are often interrelated. However, the situation is improving,

and examples of this include the reporting by South Africa of its intra-Southern African Customs

Union (SACU) trade data since 2010.

The South African-Chinese trading relationship is the largest African bilateral trading relationship,

and the trade data raises several questions that all suggest ‘black holes’. We examined this relationship

for ‘black holes’ and found that during 2014 the reported South African exports were valued at $8.7

billion while the reported imports into China were valued at $44.6 billion. This is a huge discrepancy.

The main export from South Africa is iron ore and when one considers that these Chinese imports



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include costs of transporting a very bulky commodity to China by ship the reconciliation values are

very good. Similar indications of reconciliation are shown for the next two lines of iron and steel

products and wood pulp.

It is the fourth commodity (precious stones and metals) where the big discrepancy in the data shows

up. South Africa reported exports of between $209 million and $333 million during 2014, with these

exports featuring platinum almost exclusively. On the other hand, China indicated imports of close to

$7 billion during this period, with these imports including some $2.4 billion for platinum and an even

larger $4.3 billion for diamonds – these are not even mentioned in South African exports to China.

The real problem associated with this HS line is HS 99, a ‘special’ classification for China. This had

an associated import value of $82.8 billion in total from the world during 2014, with $30 billion from

Switzerland and $26.7 billion from South Africa. Given that South Africa (a) does not report gold

trade data and (b) is a major gold-producing nation we can assume that the Chinese special code

relates to gold and perhaps other precious metals. Technically, if it is gold, then imports from

Switzerland are re-exports as Switzerland is not an actual gold producer.

For the reverse flow of Chinese exports and South African imports the overall reconciliation is very

good, although at the more detailed level there are some worrying differences. In recent years no trade

issue has flamed passions and trade responses in South Africa like the importation of clothing (mostly

HS 61 and 62) from China. This culminated in late 2006 with the imposition of a two-year quota

regime, a regime that only lasted for two years. Indeed, clothing from China declined, but imports

from other sources increased. Furthermore, an examination of the South African imports/Chinese

exports suggests that something akin to transhipment or other untoward practices may have been

accentuated by the quotas. We suggest a hypothesis that clothing exports from China may be entering

South Africa disguised as the product of another exporter.

Intra-African trade in HS89 (ships, boats and other floating structures) is another trade commodity

that presents data problems. This trade is focused on the south-western coastal African countries and

there are widely differing trade values between these countries. We suggest that these trading lines are

essentially re-exports that are being transferred between oil fields in the region. This distinction is

important as the HS 89 Chapter is reported as being a significant component of intra-African trade.

Tobacco trade from Zimbabwe presents an interesting case where trade data, while perhaps not falling

into a black hole, is inconsistent. For 2014, Zimbabwe reported an export of just over $840 million

globally, with $758 million of this exported to South Africa. A completely different picture is shown

from the mirror data, where the world recorded imports of tobacco valued at $944 from Zimbabwe



5

and China was the biggest reported importer of tobacco from Zimbabwe. Tobacco appears to be just

transiting through South Africa, and probably with no value-adding occurring. Perhaps the tobacco

goes up in smoke!

In 2013, Zambia’s total exports were valued at more than $10 billion, with copper making up more

than 65% of these exports. Zambia consistently reports Switzerland as the key destination for its

copper, with China as the second largest import market. However, mirror data for Zambian copper

paints a different picture, with the key exports markets being China, India and Korea. Switzerland was

not even recorded as an exporting partner. This anomaly in the data is a result of the fact that the main

exporting company has its headquarters in Switzerland.

Trade in HS 71 (precious stones and metals) is generally not reported by South Africa, and this leads

to confusion in intra-African trade data. Direct exports show that there are eight exporters reporting

trade of at least $246 million, with Ghana at the top of the list, while the mirror data is completely

dominated by the SACU members of South Africa, Botswana and Namibia while Ghana is hardly

mentioned. Ghana’s exports decline from $4,187 million for the direct data to $16 million in the

mirror data. South Africa generally does not report its gold trade profile and the gold exports from

Ghana are primarily destined for South Africa.

Within SACU and surrounding countries the data associated with trade in vehicles becomes very

confusing, with large differences between mirror and direct data. What seems to be happening is that

Namibia and Botswana are exporting second-hand vehicles to Angola, Zambia and Zimbabwe.

Namibia, for example, is reported as exporting vehicles worth around $70 million to South Africa

whereas South Africa reports imports worth less than $1 million. An interesting ‘black hole’ in the

southern African vehicle trade is that Zimbabwe imports used vehicles in many forms from the United

States (US). The attempts of tralac trying to trace this flow through the US and Zimbabwe trade data

shed little light on the trade except that it seems to be landed in Namibia and transits to Zimbabwe

through Botswana as South Africa will not allow used vehicles to transit it territory.



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1. Background

A black hole is defined in Wikipedia as a mathematically defined region of space-time exhibiting such

a strong gravitational pull that no particle or electromagnetic radiation can escape from it. At times

African trade data seems to exhibit these same characteristics. This is because attempts to reconcile

data between bilateral partners can result in interesting situations where some of this data seems to

have plunged into one of these black holes. Several of our tralac trade data publications have

uncovered evidence of these black holes, and at our week-long trade data analysis1 we undertook a

more comprehensive investigation into these data anomalies. We believe that a strong data foundation

is essential to policy advice and therefore shining a spotlight on these anomalies makes a contribution

to better policy implementation.

Mevel et al. (2013) examine the phenomena of illicit financial flows (IFF) and trade mispricing and

their impact on African economies, but their analysis is what we would consider to be a higher level

and more comprehensive approach to assessing the costs to Africa of IFF. Certainly, tax evasion

through trade mispricing is part of the overall IFF problem. Mevel et al. explore this aspect of IFF

from African countries at the sectoral level with an indication of the flows’ destinations. They take

this analysis a step further by using a computer model to assess the impact on African economies.

They find that in general trade mispricing is highly concentrated regionally in SACU, Nigeria, Egypt

and Morocco and by commodities in copper and other nonferrous metals, crude and refined oils, and

precious metals and minerals.

Our analysis does not aim to reach the sophisticated level reached by Mevel et al. but rather to

document and examine some of the glaring examples of where African trade data does not reconcile

between exporters and importers. We explore the availability and reliability of trade data and

investigate inconsistencies, i.e. black holes or areas where data apparently disappears without a trace.

We do this by using trade data mostly sourced from the ITC, comparing their direct and mirror data.

Of course, there can be several legitimate reasons as to why trade data does not reconcile. These

include timing issues, re-exports, goods that are transiting, differences in balance of payment

treatments between selling and leasing arrangements in big-ticket items such as aircraft, trade

classification differences, the suppression of trade data by one or more partners, and the confusion that

can exist between reporting imports as cost, insurance, and freight (c.i.f.) or including freight and

related costs as most countries do – distinct from South Africa where imports are reported as not

1 An exercise that has become known as ‘Geek Week’.



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including these costs. Many of these reasons help to explain at least some of the ‘black hole’

incidents.

We start the paper by looking at the bigger picture (what data is available, where the biggest gaps are)

and then move on to specific products. In some instances we are able to offer plausible explanations

for these discrepancies, but many remain as pure black holes.

By way of background two recent tralac publications are relevant to this paper. The first is Sandrey

(2015a) where intra-African trade is examined. That study found that over the last few years aggregate

intra-African trade (imports and exports) share of total African trade has consistently been around the

12% mark (with a high of 14% in 2009) as the shares from both the European Union (EU) and the US

are dramatically declining in the face of increasing BRIC (Brazil, Russia, India and China) trade.

Emphasising the data problems Sandrey (Idem) found that by using ITC direct and mirror imports the

main intra-African exporters are South Africa, Zambia, Botswana and Namibia. At the HS 2 Chapter

level imports were headed by mineral fuels, ships and related equipment, general machinery, vehicles

and electrical machinery although, based on mirror data, trading in precious stones and metals fell into

the top bracket. By combined direct and mirror data, however, the main intra-African exporters are

South Africa, Nigeria, Côte d'Ivoire and Egypt, By HS products the main exports are fuels, ships etc.,

precious stones and metals, general machinery, vehicles and electrical equipment. These discrepancies

resulted from several data problems. Nonreporting means that mirror data is often used in place of

direct data and when neither party reports the mirror is non-existent. Examples are the penchant for

South Africa not to report gold trading and, at least until recently, poor intra-SACU reporting and the

suspected confusion of re-exports in at least vessels and related equipment and vehicles.

Secondly, Sandrey (2015a) examined the intra-SACU trade data. He explained how this analysis only

recently became feasible after in mid-November 2013 the South African Revenue Service (SARS)

announced the revision of trade statistics that covered trade data from Botswana, Lesotho, Namibia

and Swaziland (the BLNS partners). The revision published historical trade data from 2010 to 2012

and direct trade with the BLNS was included to provide an accurate reflection of South Africa’s trade.

Dominating the intra-SACU trade relationship are the South African exports to the BLNS. This is

because in direct BLNS-South African trade only the bilateral flows between Botswana and Namibia

are important in overall terms. South African imports from the BLNS are significantly lower than

exports to the BLNS, and this has direct implications for SACU tariff revenue sharing. At the sector

level a particular problem is South Africa’s trade in precious stones and metals (gold), while for

vehicles the data becomes very confusing, with large differences between mirror and direct data.



8

These and other situations are examined further in the current paper as they represent examples of

‘black holes’.

1.1 The data

We believe that recent advances in trade data from the ITC have resulted in African trade data

increasing in quality and accessibility. This paper generally uses the ITC data for the data foundation,

augmented with SACU data sourced directly from the SACU Secretariat. We must, however, start

with the ‘health warnings’ that the ITC issues about their data. It stresses that for countries that do not

report trade data to the United Nations (UN) it uses partner-country data, an approach referred to as

mirror statistics. Mirror statistics are a second-best solution, but better than having no data at all and

allow the coverage of the more than 50 primarily low-income countries (many of them African) that

do not report national trade statistics.

Mirror reporting does unfortunately have a number of shortcomings. First and foremost, it does not

cover trade with other nonreporting countries. As a result, mirror statistics hardly cover South-South

trade and the ITC specifically warns that its data would not be a suitable source for an assessment of

intra-African trade that this paper focuses upon. In addition, there is the problem of transhipments,

which may hide the actual source of supply, and mirror statistics invert the reporting standards by

valuing exports in c.i.f. terms (that are inclusive of transport cost and insurance) and imports in free on

board (f.o.b.) terms (that exclude these items). In Africa these latter differences can be substantial, and

this can lead to differing and inconsistent data when trying to undertake an analysis such as this paper

attempts. We also find other problems that include (a) products such as drilling rigs that are perhaps

re-exports which seem to be classified as exports and as the values of these items are substantial they

again bias the data, and (b) the fact that South Africa, the industrial powerhouse of the continent, does

not fully report its trade in gold.

We start by showing the overall position for the total ITC profile for African trade for each African

country, with Graph 1 providing an overview of data availability for all 54 countries in Africa for all

years between (and including) 2001 and 2013. Note in particular that for 20% of the entries by country

the ITC uses mirror data while no data is available for a further 7% of the entries from these countries.

In particular, no data is available from Angola, Chad, Democratic Republic of the Congo (DRC),

Equatorial Guinea and Liberia throughout the entire period and from Comoros, Djibouti, Eritrea,

Guinea-Bissau and Libya over most of the period. In addition, several key players, such as Kenya, are

late reporting and only in recent years has the complete intra-SACU trading data been available. This



9

means that we are dealing with a situation that often forces us to work in ‘a hall of mirrors’ where the

situation is often distorted at best and simply not available at worst.

Graph 1: ITC data availability for African countries

Source: ITC

2. The overall data picture for intra-African trade data

A picture of the overall intra-African export trade data is given in Table 1, where the reported export

data, ranked by value, is shown on the left-hand side of the table for 2003, 2008 and the most recent

full data set for 2013. The middle section shows the comparable ITC data as reported by the importing

partner. Both sets are a mixture of direct and mirror data. On the right-hand side of the table the ratio

between exports divided by imports is shown. In theory this ratio should be around 110% to perhaps

115% as the costs associated with transportation and transaction would be expected to be in this

vicinity given the high transportation and transaction costs associated with Africa in general. An

exception to this are imports into South Africa, and given the importance of these imports in the

overall total this will bias the ratio downwards.

Overall, there are several entries on the right-hand section where the ratio for 2013 is close to our

hypothetical 110% figure. In particular, this includes the overall total for Africa, and note how this

ratio increased over the three years shown as data reporting improved. Only South Africa, the main

exporter, and Mozambique, near the bottom of the table, are below 100% while, conversely, there are

several instances where the ratio is significantly above the supposed 100% optimal ratio. Where the

ratio is exactly 100% all those countries shown in Table 1 (except Uganda) are reported as mirror data

for 2013, thus by definition the ratio would be 100%.

0 No Data

1 Reporting Data

2 Mirror Data



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Table 1: African exports for selected years, US dollars (millions) and the ratio of exports divided by

partner imports (and expressed as a %)

Source: ITC

An analysis of the reverse flows, that of intra-African import trade by country assessed against the

respective exports is given in Table 2, and here a similar pattern emerges. The overall level for Africa

is lower than it can conceivably be for 2013 as countries are reporting more exports to each other than

they report as imports. South Africa is again a consistent stand-out in this respect and, again, given

South Africa’s dominant roles in intra-African trade, this will influence the total ratio. Conversely,

given South Africa’s role as an importer that reports in c.i.f. values this will also influence the total

more than marginally. Again, the use of mirror data is apparent, as is the nonreporting of the full intra-

SACU trade prior to 2010.

TOTAL All products, US dollars (millions)

Country exports Partner imports Ratio

Exporters 2003 2008 2013 2003 2008 2013 2003 2008 2013

Africa 18,850 58,774 92,527 23,525 57,994 85,836 80% 101% 108%

South Africa 5,065 12,531 27,400 11,956 21,378 31,207 42% 59% 88%

Nigeria 2,012 9,390 7,795 2,081 7,237 7,795 97% 130% 100%

Côte d’Ivoire 1,366 3,135 5,276 935 2,029 3,038 146% 155% 174%

Egypt 503 3,051 4,362 882 2,396 4,214 57% 127% 104%

Angola 18 3,014 3,950 18 3,014 3,950 100% 100% 100%

Ghana 231 1,964 3,807 194 403 2,118 119% 488% 180%

Algeria 582 2,599 3,490 644 2,374 3,015 90% 109% 116%

Namibia 801 2,066 3,483 399 673 2,174 201% 307% 160%

Zambia 441 1,438 3,233 224 1,417 2,482 197% 101% 130%

Zimbabwe 698 1,101 3,172 698 1,137 627 100% 97% 506%

Kenya 1,087 2,363 2,256 832 2,182 2,256 131% 108% 100%

DRC 22 607 2,140 22 607 2,140 100% 100% 100%

Tunisia 619 1,984 2,041 593 1,000 1,974 104% 199% 103%

Morocco 369 1,019 1,951 272 813 1,572 135% 125% 124%

Congo 54 875 1,850 54 584 1,597 100% 150% 116%

Tanzania 191 1,026 1,789 154 649 1,086 124% 158% 165%

Swaziland 799 320 1,401 207 320 1,401 385% 100% 100%

Uganda 193 798 1,364 108 443 1,370 179% 180% 100%

Botswana 395 1,263 1,203 115 626 938 343% 202% 128%

Gabon 86 363 1,194 116 871 1,194 74% 42% 100%

Mozambique 261 428 1,164 182 1,025 1,992 144% 42% 58%

Senegal 445 1,059 1,155 350 975 955 127% 109% 121%

Libya 478 1,268 865 478 1,464 865 100% 87% 100%



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Table 2: African imports for selected years, US dollars (millions) and the ratio of imports divided by

partner exports (and expressed as %)

TOTAL All products; US$ (millions)

Country imports Partner exports Ratio

Importers 2003 2008 2013 2003 2008 2013 2003 2008 2013

Africa 23,525 57,994 85,836 18,850 58,774 92,527 125% 99% 93%

South Africa 1,124 7,504 12,252 3,089 13,387 19,501 36% 56% 63%

Zambia 1,074 3,079 6,045 812 2,815 5,738 132% 109% 105%

Botswana 3,416 4,125 5,576 41 269 5,568 8263% 1534% 100%

Namibia 1,176 3,280 5,185 29 113 4,744 4015% 2910% 109%

Zimbabwe 1,140 2,162 4,480 1,140 2,092 3,047 100% 103% 147%

Côte d'Ivoire 712 2,883 4,371 595 2,315 4,332 120% 125% 101%

Congo 177 1,558 4,333 177 767 3,553 100% 203% 122%

Mozambique 638 1,291 3,570 898 1,791 3,469 71% 72% 103%

DRC 311 2,124 3,553 311 2,124 3,553 100% 100% 100%

Nigeria 758 1,868 2,753 771 3,232 2,753 98% 58% 100%

Angola 823 1,604 2,564 823 1,604 2,564 100% 100% 100%

Morocco 689 2,258 2,375 510 1,595 1,901 135% 142% 125%

Libya 526 400 2,287 526 1,786 2,287 100% 22% 100%

Algeria 382 988 2,108 281 1,122 2,065 136% 88% 102%

Tunisia 645 2,246 2,017 552 2,167 2,452 117% 104% 82%

Kenya 465 1,247 1,923 585 1,480 1,923 80% 84% 100%

Swaziland 1,248 51 1,544 22 51 1,544 5609% 100% 100%

Tanzania 444 1,522 1,429 599 1,185 1,453 74% 128% 98%

Egypt 609 1,917 1,427 602 1,940 1,579 101% 99% 90%

Lesotho 917 1,019 1,426 10 9 1,426 8881% 11036% 100%

Malawi 468 1,352 1,260 356 769 899 131% 176% 140%

Source: ITC

A breakdown of data for intra-Africa commodity trade is reported for the 2012 and 2013 years in the

same format, with African exports and importers along with the ratios shown in Table 3. Again, the

overall ratios are perhaps in the ‘acceptable’ range, but there are some flags such as precious stones

and metals.



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Table 3: African commodity trade for selected years, US dollars (millions) and the ratio of imports

divided by partner exports (and expressed as %)

African exports African imports Ratio

Code Label 2012 2013 2012 2013 2012 2013

TOTAL All products 98,680 92,527 84,602 85,836 117% 108%

27 Mineral fuels 28,046 22,864 26,127 23,849 107% 96%

89 Ships, boats, etc. 4,868 7,927 4,601 6,432 106% 123%

71 Precious stones, metals 9,638 6,653 1,043 1,340 924% 496%

84 General machinery 5,045 4,747 4,823 4,726 105% 100%

87 Vehicles 4,117 4,238 4,622 4,399 89% 96%

85 Electrical equipment 2,717 2,587 2,708 2,588 100% 100%

26 Ores 1,721 2,414 1,259 1,921 137% 126%

39 Plastics 2,213 2,217 2,019 2,077 110% 107%

72 Iron and steel 2,189 2,145 2,118 2,039 103% 105%

73 Articles of iron/steel 2,089 1,990 2,249 2,065 93% 96%

24 Tobacco 1,865 1,796 950 982 196% 183%

25 Cement, etc. 1,640 1,692 1,729 1,715 95% 99%

17 Sugars 1,334 1,532 1,141 1,191 117% 129%

33 Essential oils, etc. 1,531 1,436 1,149 1,212 133% 118%

28 Inorganic chemicals 879 1,236 999 1,154 88% 107%

22 Beverages 1,144 1,193 988 1,085 116% 110%

48 Pulp, paper, etc. 1,209 1,092 1,239 1,105 98% 99%

15 Fats and oils 1,301 1,025 1,274 1,022 102% 100%

09 Coffee, tea 1,121 1,013 991 977 113% 104%

34 Soaps, etc. 1,026 944 877 837 117% 113%

10 Cereals 1,195 921 1,119 914 107% 101%

31 Fertilisers 980 919 1,386 1,902 71% 48%

38 Miscellaneous chemical 897 863 898 827 100% 104%

03 Fish 648 833 1,086 1,163 60% 72%

07 Vegetables 719 805 718 790 100% 102%

Source: ITC



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3. Examples of black holes

We appreciate that intra-African trade is but one aspect of African trade where we may find ‘black

holes’, but we introduce it to highlight some possible areas warranting further research.

The South African-Chinese trading relationship

This relationship is one of the two largest African bilateral trading relationships; the other is the

Nigerian-US trade. The latter is hard to verify as ITC data for Nigeria is only available up to 2012: at

that time the trade mostly concerned US imports of Nigerian oil and the data reconciled reasonably

well between the two partners. On the other hand the South African-Chinese trade data raises several

questions that all suggest ‘black holes’. We will examine this relationship and the ‘black holes’ in this

current section.

We start off with the reported trade flowing from South Africa to China as shown in Table 4. On the

left-hand side we show the reported South African exports, with a total of $8.7 billion in 2014. On the

right-hand side we show the reported imports into China, with a total of $44.6 billion for 2014. This is

a huge discrepancy and a classic ‘black hole’ in the trade data. The top individual export from South

Africa is iron ore ($5,575 million in 2014), with China reporting imports of $8,487 million.

Considering that these Chinese imports include costs of transporting a very bulky commodity to China

by ship the reconciliation values are very good. Examining the ITC data for 2012 shows that the

quantities are within 1% of each other, with the 2013 data reconciliation within 3%. Only the 2014

data entails an 11% difference (79% reconciliation in the table) which is a little off balance, but even

so this is still relatively close. Similar indications of reconciliation are shown for the next two lines of

iron and steel products and wood pulp.

It is the fourth commodity (precious stones and metals) where the big discrepancy in the data shows

up: South Africa reports exports (almost exclusively platinum) of between $209 million and $333

million. On the other hand, China indicated imports of close to $7 billion in imports during 2014:

some $2.4 billion for platinum and an even larger $4.3 billion for diamonds (not mentioned in South

African exports to China). The real problem associated with this HS line is shown in the table under

HS 99, a ‘special’ classification for China. This has an associated import value of $82.8 billion in total

from the world during 2014, with $30 billion from Switzerland and $26.7 billion from South Africa

making up most of the imports. Given that South Africa (a) does not report gold trade data and (b) is a

major gold-producing nation we can assume that the Chinese special code relates to gold and perhaps

other precious metals. Technically, if it is gold, imports from Switzerland are re-exports as



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Switzerland is not an actual gold producer. All in all, however, based upon reported trade data, we can

do little more than speculate. But a situation where exports for 2014 report $8.7 billion and imports

$44.7 billion leaves a lot of details ‘in a black hole’.

Table 4: South African exports, Chinese imports, $ (millions) and % ratio export/import

RSA exports $ (millions) RSA exports/China imports China imports $ (millions)

2012 2013 2014 2012 2013 2014 2012 2013 2014

HS Total 10,321 12,059 8,680 23% 25% 19% 44,654 48,388

26 Ores 6,840 8,023 5,575 92% 95% 79% 7,450 8,487 7,073

72 Iron/steel 736 1,094 1,157 80% 107% 88% 920 1,023 1,322

47 Pulp wood 160 210 343 89% 117% 89% 179 179 387

71 Precious metals 209 333 216 6% 6% 3% 3,655 5,430 6,906

51 Wool 191 212 209 104% 103% 101% 185 206 207

99 Special

0% 0% 0% 28,958 30,281 26,676

27 Fuels 1,089 997 203 56% 90% 44% 1,947 1,110 464

74 Copper 206 238 170 68% 73% 58% 301 326 296

Source: ITC

The comparable data for Chinese exports and South African imports is shown in Table 5. Here the

overall reconciliation is very good (keeping in mind that South Africa does not include transportation

and associated costs in its import valuations). For the individual HS 4 lines there are, however, some

worrying differences. We explore HS 61 and 62 below as case studies.

Table 5: China exports, South African imports, $ (millions) and % ratio export/import

China exports $ (millions) China export/RSA import RSA imports $ (millions)

2012 2013 2014 2012 2013 2014 2012 2013 2014

HS TOTAL 15,323 16,831 15,704 105% 105% 102% 14,610 16,011 15,449

85 Electrical 2,076 2,990 2,856 78% 78% 76% 2,645 3,816 3,780

84 Machinery 2,304 2,459 2,242 64% 68% 68% 3,587 3,595 3,307

62 Clothing 814 874 868 174% 176% 186% 467 497 467

61 Clothing 1,001 952 844 211% 188% 176% 474 508 478

94 Furniture 1,077 1,166 838 233% 261% 202% 462 447 415

64 Footwear 1,038 854 766 149% 126% 123% 698 676 621

39 Plastics 570 609 567 139% 133% 114% 410 457 495

87 Vehicles 615 611 576 127% 147% 147% 484 415 393

62% 62% 61% 63% 65% 64%

Source: ITC



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South African imports of clothing from China

Over the last 15 years or so, no trade issue has flamed passions and trade responses in South Africa

like the importation of clothing (mostly HS 61 and 62) from China. This culminated in late 2006 with

the imposition of a two-year quota regime on the importation of selected fabric and clothing lines

from China at the behest of South African Trade Unions but against the objections of the apparel

manufacturers and retailers. This quota regime only lasted for two years (2007 and 2008) with the

mixed result that, indeed, clothing from China declined, but imports from other sources increased.

During this period South African border officials monitored these imports very closely, and we would

have expected that reconciliation between the respective data for Chinese exports and South African

imports would have been acceptable. Sandrey et al. (2013) examined this data and found that these

trade flows did not reconcile well and that this was especially (and worryingly) so in the closely

monitored clothing imports.

Given the importance of this trade we will re-examine and update the reconciliation between South

African clothing imports and Chinese exports as defined by HS 61 and 62, the main clothing lines.

The data for HS 62 (articles of apparel, accessories, not knit or crochet) is shown in Table 6 for the

years 2004 to 2014 inclusive. The top two lines show the reported imports into South Africa and

exports from China in US dollars (millions). We would expect these to be very close as South Africa

values imports on f.o.b. Below these two lines the table shows the import data expressed as a

percentage of the export data. This is done firstly by total HS 62 and then by the associated

disaggregated HS 4 codes to see if patterns and possible explanations emerge in the detail. There is a

degree of consistency throughout the table. By value, both imports from China and exports to South

Africa declined during the quota period but recovered, while the total reconciliation declined during

these same quota years and basically remained at these lower levels since. That suggests that

something akin to transhipment or other untoward practices may have been accentuated by the quotas.

For the disaggregated HS 4 codes there is a consistency across the lines but not between the lines,

with very few consistently around the 100% reconciliation level.2

This analysis is extended further in Table 7 where the South African import data by source is

compared with partner mirror export data over the same period. This is presented to offer the

hypothesis that clothing exports from China may be entering South Africa disguised as products of an-

2 We would not of course expect perfect reconciliation but we feel that we are entitled to an approximation to 100% given that South Africa uses f.o.b. for import valuations. The use of c.i.f. would perhaps add 10 to 15 percentage points depending on shipping and associated costs. Note also that despite the supposed global consistency in the HS 4 and even the more detailed HS 6 codes there may be some classification differences, but if so they seem to be consistent.



16

Table 6: Reconciliation of South African HS 61 imports/Chinese exports

2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Imports ($m) 246 321 462 284 266 352 487 500 467 497 467

Exports ($m) 297 389 571 411 472 504 681 828 814 874 868

South Africa’s imports from China as % of Chinese exports to South Africa: a reconciliation

HS 4 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

Total 83% 82% 81% 69% 56% 70% 71% 60% 57% 57% 54%

6201 49% 56% 60% 77% 64% 74% 63% 46% 51% 51% 63%

6202 53% 79% 56% 60% 51% 68% 54% 22% 36% 38% 57%

6203 98% 92% 92% 73% 54% 65% 68% 61% 50% 53% 50%

6204 85% 74% 78% 57% 43% 56% 63% 56% 50% 52% 39%

6205 79% 84% 92% 75% 55% 78% 76% 59% 59% 58% 67%

6206 154% 142% 158% 89% 70% 124% 121% 106% 139% 103% 116%

6207 138% 94% 80% 115% 83% 94% 148% 105% 103% 96% 137%

6208 106% 76% 136% 130% 101% 109% 131% 118% 101% 101% 62%

6209 104% 98% 59% 148% 172% 128% 122% 131% 100% 77% 84%

6210 24% 7% 10% 15% 27% 23% 24% 15% 19% 20% 30%

6211 49% 84% 43% 33% 24% 67% 53% 55% 65% 79% 60%

6212 500% 267% 113% 137% 119% 269% 179% 286% 178% 141% 123%

6213 94% 119% 104% 83% 91% 77% 68% 86% 86% 64% 85%

6214 45% 59% 50% 51% 37% 40% 47% 52% 71% 80% 75%

6215 58% 60% 81% 90% 84% 102% 89% 80% 88% 80% 93%

6216 125% 122% 124% 124% 73% 88% 97% 115% 113% 85% 100%

6217 168% 331% 226% 294% 235% 312% 259% 194% 214% 184% 206%

Source: ITC

other exporter. The top line (below the years) shows that, overall, the world reports more exports to

South Africa than the official South African data acknowledges, with a percentage value that was

around 70% pre-quota but dropped to 60% during the quota before rising to nearer a consistent 80%

post-quota. Immediately below this line we have the Chinese reconciliation figure reproduced from

the above table, and given the dominance of Chinese imports (shown in line 4) this figure will have a

major overall impact. Lines 5 and 6 show the second most important clothing import source for 2014,

Swaziland and Lesotho, and here the most recent data reconciles well.3

3 This intra-SACU trade data was sourced directly from the SACU Secretariat and only become available in a consistent format since 2010. While we have heard unconfirmed rumours of transhipment of Chinese clothing through Swaziland to South Africa there is no obvious economic incentive as the common SACU 45% tariff applies to both countries. The actual entry into South Africa from Swaziland is duty-free, as is access for all Southern African Development Community (SADC) partners.



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Table 7: Clothing in HS 62, South African imports/reported exports to South Africa

Exporters 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014

World 64% 68% 69% 68% 59% 59% 80% 75% 73% 79% 81%

China 83% 82% 81% 69% 56% 70% 71% 60% 57% 57% 54%

China % total 74% 73% 80% 59% 58% 69% 59% 52% 56% 56% 53%

Swaziland

119% 89% 99% 99% 119%

Lesotho

81% 93% 96% 108%

Madagascar 767% 56% 200% 343% 164% 230% 224% 280% 349% 266% 292%

Mauritius 57% 84% 76% 81% 78% 74% 91% 74% 74% 95% 89%

India 50% 82% 75% 82% 82% 71% 88% 78% 79% 58% 72%

Bangladesh 71% 86% 115% 108%

115% 108% 114%

Vietnam 99% 135% 68% 120% 162% 147% 116% 81% 126%

Turkey 85% 98% 148% 128% 169% 116% 86% 99% 102% 120% 166%

Italy 80% 49% 65% 66% 67% 76% 84% 72% 74% 62% 87%

Indonesia 53% 79% 99% 236% 104% 66% 69% 119% 168% 201%

Romania 3061% 11973% 11113% 1375% 3837% 8118% 6239% 2925% 2860% 16011% 5951%

Morocco 1041% 1023% 620% 971% 78000% 4036% 9040% 367% 3196% 10082% 41644%

US 112% 123% 172% 146% 115% 119% 139% 98% 110% 66% 82%

Pakistan 16% 39% 63% 56% 111% 69% 69% 64% 86% 127% 147%

% RSA imports 85% 87% 90% 80% 82% 90% 82% 79% 92% 95% 95%

% partner exports 79% 80% 83% 75% 72% 76% 84% 85% 95% 104% 101%

Source: ITC and SACU Secretariat



18

Intra-African trade in HS 89 (ships, boats and other floating structures)

This particular commodity presents data problems. The left-hand half of Table 8 shows intra-African

exports, with this segment in turn divided into the direct and mirror data (exports as reported by

exporters) on the left-hand side and the direct and mirror data of exports as summed from importing

country reporting. The totals for the exports each year are the same, but there are major differences in

the sources (although the underlined mirror data is shown to be prominent). Note that Angola is a

major trading partner here, and in general Angolan trade data is sourced from mirror data. The right-

hand half of the table shows the two sets of reported data for imports, and again the total are the

same. The first set of this data on the left-hand side pertains to imports by importing countries, or

direct and mirror data. The far right segment pertains to imports by exporting countries, again as a

mixture of direct and mirror data. There is little reconciliation between these two sets of importing

data. The right-hand side is somewhat more consistent but still has some glaring problems. For

example, Ghana is listed as being the mirror import source of some $737 million for African imports

based on African exports, yet it is not shown as a direct importer. Indeed, Ghana’s direct import data

reports only $3 million in exports during 2013, yet the mirror data for Ghana reports exports of $737

million and all of this trade as exports to (imports by) Congo. Scrutiny of the intra-African trade at the

HS 4 level reveals that during 2013 intra-African exports were valued at $5,024 million for HS 8905

(light vessel, dredger; floating dock; floating/submersible drill platform) and some $2,079 million for

HS 8901 (cruise ship, cargo ship, barges). We suggest that these trading lines are essentially re-

exports that are being transferred between oil fields in the region. This distinction is important as the

HS 89 Chapter is reported as being a significant component of intra-African trade.



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Table 8: Intra-African trade in HS 89 (ships, boats and other floating structures), direct and mirror data, 2012 and 2013 by $ (millions)

Intra-African exports Intra-African imports

Exports by exporter Exports by importer Imports by importer Imports by exporter

Exporters 2012 2013 Importers 2012 2013 Importers 2012 2013 Exporters 2012 2013

Africa 4,868 7,927 Africa 4,868 7,927 Africa 4,601 6,432 Africa 4,601 6,432

Angola 1,839 1,856 Congo 2,817 2,997 Congo 3,651 4,035 Angola 1,839 1,856

Côte d’Ivoire 5 1,731 Ghana 88 1,543 Angola 406 874 Congo 539 1,430

Congo 1,150 1,662 Angola 406 874 Côte d’Ivoire 0 654 Gabon 855 985

Gabon 855 985 South Africa 72 822 Gabon 206 321 Ghana 41 737

Namibia 22 723 Côte d’Ivoire 40 710 Nigeria 7 253 Côte d’Ivoire 138 246

Equatorial Guinea 53 219 Gabon 206 321 Cameroon 0 153 Equatorial Guinea 53 219

Nigeria 615 213 Nigeria 269 253 Liberia 2 45 Nigeria 289 213

DRC 17 190 Cameroon 46 153 Sierra Leone 0 27 DRC 17 190

Cameroon 0 148 Namibia 145 134 Namibia 112 25 Cameroon 176 148

Mozambique 32 71 Liberia 2 45 Tanzania 141 8 Namibia 159 129

Liberia 46 45 Sierra Leone 0 27 DRC 15 5 South Africa 233 69

Togo 47 41 Mauritius 1 13 Benin 0 5 Senegal 16 54

South Africa 26 32 DRC 15 5 South Africa 2 5 Cabo Verde 0 52

Tunisia 1 4 Benin 0 5 Kenya 2 4 Liberia 46 45

Subtotal % 97% 100% 84% 100% Subtotal % 99% 100% 96% 99%

Source: ITC and SACU Secretariat



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Similarly, according to data (direct data) reported by Côte d’Ivoire, Ghana was one of its key trading

partners in Africa, with 35% of total exports from Côte d'Ivoire to Africa destined for Ghana in 2013.

Both countries report data directly for the period between 2003 and 2013 (with one exception: not data

reported by Ghana for 2004). However, when comparing data of products traded between these two

countries, it is evident that Côte d'Ivoire’s exports are recorded significantly higher than what was

recorded as imported by Ghana. The most significant of these discrepancies for 2013 includes the

reported export from Côte d'Ivoire to Ghana of HS89 (ships, boats and other floating structures4) for

more than $1,54 billion, while Ghana records nothing.

Zimbabwe: Trade in HS24 (Tobacco and manufactured tobacco substitutes)

Tobacco trade from Zimbabwe presents an interesting case where trade data, while perhaps not falling

into a black hole, is inconsistent. For 2014, Zimbabwe reported an export of just over $840 million

globally, with $758 million of this exported to South Africa. A completely different picture is shown

by looking at the mirror data, where the world recorded imports of tobacco from Zimbabwe for the

same year valued at $944 million. However, unlike trade reported by Zimbabwe, the biggest reported

importer of tobacco from Zimbabwe was China, making up around 60% of total exports in the last

three years.

Zambia: Trade in HS74 (copper and articles thereof)

In 2013, Zambia’s total exports were worth more than $10 billion. Copper is a key commodity export

for Zambia, making up more than 65% of exports, totalling $6.89 billion in 2013 (Table 10). Zambia

consistently reports Switzerland as the key destination for Zambian copper, totalling $3.8 billion in

2013. The second biggest import market for Zambian copper for the same year was China, receiving

30% of copper exports, with a value of $2.1 billion. However, mirror data for Zambian copper paints a

different picture. While the sum of global copper imports from Zambia is reported as $4.6 billion, key

exports markets are China, India and Korea, with China importing more than 60% of these and the

other two countries 5% each. Looking at Swiss imports of copper, Zambia is not even recorded as an

exporting partner. This anomaly suggests that there is a misrepresentation of the trade data to the tune

of nearly $4 billion. The more recent 2014 data shows little change for the Switzerland-China

conundrum. In discussion with Zambian trade officials we were told that they were aware of the

anomaly in the data and that it was a result of the fact that the main exporting company had its

4 HS890520 (floating or submersible drilling or production platforms)



21

headquarters in Switzerland. An analysis of balance of payment data would shed light on this

problem.

Table 9: Zambian exports of copper, direct and mirror data

Importers 2004 2006 2008 2010 2012 2013 2014

Direct data from Zambia

World 684 2,613 3,279 5,418 6,373 6,890 7,243

Switzerland 158 1,427 1,972 3,332 3,882 3,856 4,248

China 0 81 206 1,297 1,646 2,110 1,733

Australia 0 0 0 0 0 0 349

Singapore 0 0 0 0 0 14 264

South Africa 158 163 169 267 177 238 196

UAE 0 0 20 176 217 246 134

Japan 0 3 0 7 1 56 78

UK 240 65 89 105 298 107 65

% total 81% 67% 75% 96% 98% 96% 98%

Mirror data from importing countries

World 817 2,029 2,752 4,176 4,664 4,595 4,029

China 125 152 334 2,415 2,478 2,808 2,767

Korea 142 432 292 375 336 263 360

Italy 9 237 250 27 207 242 191

India 1 18 6 10 257 264 181

South Africa 59 153 145 168 245 198 133

Egypt 46 23 599 214 305 159 99

Thailand 112 278 132 44 45 50 90

Taipei 46 146 73 163 91 55 80

% total 66% 71% 67% 82% 85% 88% 97%

Source: ITC

Ghana: HS71 (pearls, precious stones, metals, coins, etc.)

Table 19 contains the same fourfold split for intra-African trade in HS 71 (precious stones and

metals). Looking at the far left-hand segment (exports by exporting country), we see that there are

eight exporters reporting trade of at least $246 million, with Ghana at the top of the list with exports of

$2,491 million. Yet next to this where exports by importing countries (the mirror data) are reported

the trade is completely dominated by the SACU members of South Africa, Botswana and Namibia and

Ghana is hardly mentioned. There are two interrelated problems here. One is that South Africa

generally does not report its gold trade profile and South African exports are therefore more

accurately reflected in the mirror importing data of partners. This is also reflected in the gold exports



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from Ghana which are primarily destined for South Africa but not reported there. The second is that,

at least until recently, intra-SACU trade data was not comprehensively reported. These problems are

obvious when the direct exports from South Africa are reported as being some $496 million while the

mirror data reports them to be worth $5,148 million! We have no way of answering the question of

‘Mirror mirror on the wall, who is the most truthful of all?’ Similarly, the import data shows that it

features almost inclusively reported imports (both direct and mirror) into Botswana, South Africa and

Namibia, albeit with conflicting values. Note also that Ghana’s exports decline from $4,187 million

for the direct data to $16 million in the mirror. This is explained by examining Ghana’s exports, and

there the direct data reports that they exclusively go to South Africa. The mirror export data does not

report on Ghanaian exports to South Africa. Furthermore, all of this trade is HS 7108 (gold). Thus,

South Africa is not reporting either exports or imports of gold, and this goes a long way in explaining

the large discrepancy between aggregate intra-African exports and imports of this HS Chapter.



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Table 19: Intra-African trade in HS 71 (pearls, precious stones, metals, coins, etc.), 2012 and 2013 by $ (millions)

Intra-African Exports Intra-African Imports

Exports by exporter Exports by importer Imports by importer Imports by exporter

Exporters 2012 2013 Importers 2012 2013 Importers 2012 2013 Exporters 2012 2013

Africa 9,638 6,653 Africa 9,638 6,653 Africa 1,043 1,340 Africa 1,043 1,340

Ghana 4,187 2,491 South Africa 8,817 5,148 Botswana 696 854 Namibia 403 499

Namibia 438 886 Botswana 618 1,279 South Africa 209 295 South Africa 359 452

Tanzania 941 726 Namibia 137 189 Namibia 80 159 Botswana 146 322

Zimbabwe 788 644 Ghana 16 14 Mauritius 14 24 Angola 31 21

South Africa 321 496 Mauritius 5 11 Swaziland 37 1 Sierra Leone 3 17

Botswana 287 447 Mauritania 0 3 Lesotho 0 1 Tanzania 3 8

Côte d’Ivoire 364 323 Swaziland 37 1 Kenya 1 1 Lesotho 18 8

Egypt 470 246 Lesotho 0 1 Zambia 1 1 Zimbabwe 37 7

Zambia 192 191 Kenya 1 1 Seychelles 1 1 Guinea 0 2

Burkina Faso 222 135 Zambia 1 1 Burkina Faso 0 1 DRC 1 1

Subtotal % 85% 99% 100% 100% Subtotal % 100% 100% 96% 100%

Source: ITC



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Regional trade in HS87 (vehicles other than railway, tramway)5

Within SACU and surrounding countries the data associated with trade in vehicles becomes very

confusing, with large differences between mirror and direct data. We find an extreme discrepancy for

standard passenger motor vehicles: 2013 BLNS data reports that $75.8 of these are destined for South

Africa (and $179 million to the world) while South African imports are only valued at $5.7 million

from BLNS. Namibia is reported as exporting vehicles worth $70 million to South Africa whereas

South Africa reports imports of less than $1 million. What seems to be happening is that Namibia and

Botswana are exporting second-hand vehicles to Angola, Zambia and Zimbabwe.

The extreme discrepancy, however, is to be found in the exports of HS 8703 (standard passenger

motor vehicles). BLNS data that reports these exports to be valued at $75.8 million to South Africa

(and $179 million to the world) is completely at odds with South African imports of $5.7 million from

BLNS. Namibia, for example, is reported as exporting vehicles worth around $70 million to South

Africa depending upon which process is used on the ITC site to obtain the data whereas South Africa

reports imports of less than one million. The $179 million in global exports in this line obviously

entails used vehicles, with around $100 million being sent from particularly Namibia and Botswana to

Angola, Zambia and Zimbabwe. There is also an interesting ‘black hole’ in southern African vehicle

trade in that Zimbabwe imports used vehicles in many forms from the United States. Our experiences

at tralac trying to trace this flow through US and Zimbabwe trade data sheds little light on the trade

except that it seems to be landed in Namibia and transits to Zimbabwe through Botswana as South

Africa will not allow used vehicles to transit it territory.

5 This section uses both ITC data and data from the SACU Secretariat.



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References

Mevel, S., Ofa, S. and Karingi, S. 2013. Quantifying illicit financial flows from Africa through trade

mispricing and assessing their incidence on African economies. Selected paper for presentation at the

African Economic Conference 2013, 28-30 October Johannesburg, South Africa.

Sandrey, R. 2015a. Intra-SACU trade relationships and related issues. Trade Brief T15TB01/2015.

Stellenbosch: tralac.

Sandrey, R. 2015b. Intra-African trade – an analysis. Working Paper S15WP08/2015. Stellenbosch:

tralac.

Sandrey, R., Mpitsa, E.M., Vermaak, J. and de Beer, M. 2013. Assessing South Africa’s trading

relationship with China. Working Paper D13WP03/2013. Stellenbosch: tralac.



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Annex: Trade data availability on ITC TradeMap

The following table shows ITC data availability for African countries. While it reports only up to the

year 2013 an examination of the ITC site as of July 2015 shows little change for 2014 data.

Data availability

0 No Data

1 Reporting Data

2 Mirror data

3 Mix mirror & direct data

Countries and

Territories 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013

Algeria 1 1 1 1 1 1 1 1 1 1 1 1 1

Angola 2 2 2 2 2 2 2 2 2 2 2 2 2

Benin 1 1 1 1 1 1 1 1 1 1 1 1 1

Botswana 1 1 1 1 1 1 1 1 1 1 1 1 1

Burkina Faso 1 1 1 1 1 0 1 1 1 1 1 1 1

Burundi 1 1 1 1 1 1 1 1 1 1 1 1 1

Cabo Verde 1 1 1 1 1 1 1 0 1 1 1 1 1

Cameroon 1 1 1 1 1 1 1 1 1 1 1 1 2

Central African Republic 1 1 1 0 0 1 1 1 1 1 1 1 1

Chad 2 2 2 2 2 2 2 2 2 2 2 2 2

Comoros 1 1 1 1 1 1 1 1 1 2 2 2 2

Congo 0 0 0 0 0 0 1 1 1 1 1 1 1

Côte d’Ivoire 1 1 1 1 1 1 1 1 1 1 1 1 1

DRC 2 2 2 2 2 2 2 2 2 2 2 2 2

Djibouti 0 0 0 0 0 0 0 0 1 2 2 2 2

Egypt 1 1 1 1 1 1 1 1 1 1 1 1 1

Equatorial Guinea 2 2 2 2 2 2 2 2 2 2 2 2 2

Eritrea 0 0 1 2 2 2 2 2 2 2 2 2 2

Ethiopia 1 1 1 1 1 1 1 1 1 1 1 1 1

Gabon 1 1 1 1 1 1 1 1 1 2 2 2 2

Gambia 1 1 1 1 1 1 1 1 1 1 1 1 1

Ghana 1 0 1 0 1 1 1 1 1 1 1 1 1

Guinea 1 1 0 1 1 1 1 1 1 1 1 1 1

Guinea-Bissau 0 0 1 1 1 2 2 2 2 2 2 2 2

Kenya 1 1 1 1 1 1 1 1 1 1 1 2 2

Lesotho 1 1 1 0 0 0 0 1 1 1 1 1 2

Liberia 2 2 2 2 2 2 2 2 2 2 2 2 2



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Libya 0 0 0 0 0 0 1 1 1 1 2 2 2

Madagascar 1 1 1 1 1 1 1 1 1 1 1 1 1

Malawi 1 1 1 1 1 1 1 1 1 1 1 1 1

Mali 1 1 1 1 1 1 1 1 0 1 1 1 2

Mauritania 1 1 1 1 1 0 1 1 1 1 1 1 1

Mauritius 1 1 1 1 1 1 1 1 1 1 1 1 1

Morocco 1 1 1 1 1 1 1 1 1 1 1 1 1

Mozambique 1 1 1 1 1 1 1 1 1 1 1 1 1

Namibia 1 1 1 1 1 1 1 1 1 1 1 1 1

Niger 1 1 1 1 1 1 1 1 1 1 1 1 1

Nigeria 1 1 1 0 0 1 1 1 1 1 1 1 2

Rwanda 1 1 1 1 1 1 1 1 1 0 1 1 1

São Tomé and Principe 1 1 1 1 1 1 1 1 1 1 1 1 1

Senegal 1 1 1 1 1 1 1 1 1 1 1 1 1

Seychelles 1 1 1 0 1 1 0 1 2 2 2 2 2

Sierra Leone 0 1 2 2 2 2 2 2 2 2 2 2 2

Somalia 2 2 2 2 2 2 2 2 2 2 2 2 2

South Africa 1 1 1 1 1 1 1 1 1 1 1 1 1

Sudan (North & South) 1 1 1 1 1 1 0 1 1 1 1 2 2

Swaziland 1 1 1 1 1 1 1 2 2 2 2 2 2

Tanzania, 1 1 1 1 1 1 1 1 1 1 1 1 1

Togo 1 1 1 1 1 0 1 1 1 1 1 1 1

Tunisia 1 1 1 1 1 1 1 1 1 1 1 1 1

Uganda 1 1 1 1 1 1 1 1 1 1 1 1 1

Zambia 1 1 1 1 1 1 1 1 1 1 1 1 1

Zimbabwe 1 1 0 1 1 1 1 1 1 1 1 1 1

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