Cape to Cairo - An Assessment of
the Tripartite Free Trade Area
Authors: Ron Sandrey | Hans Grinsted Jensen | Nick Vink | Taku Fundira | Willemien Viljoen
Publisher:
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Trade Law Centre for Southern Africa, National Agricultural Marketing Council. 2011
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First published 2011
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Cape to Cairo - An Assessment of the Tripartite Free Trade Area
Introduction
Regional integration features prominently in the development strategies of most African countries.
Indeed the overlapping membership of regional economic communities in Africa testifies to the
political enthusiasm of African governments for regional integration. This enthusiasm is however not
always reflected in the implementation of commitments undertaken in the various regional
integration agreements. And more fundamentally, the African paradigm of regional integration has
focused very much on a trade in goods agenda, with little attention being paid to a services agenda
and other behind-the-border issues such as investment, competition policy and government
procurement.
In October 2008 the Heads of State and Government of the 26 member states of the East African
Community (EAC), the Common Market for East and Southern Africa (COMESA) and the Southern
African Development Community (SADC) agreed to establish a Free Trade Area (FTA) integrating
the member states of these three regional economic communities.
This FTA would integrate the
economies of countries in east and southern Africa, from Cape to Cairo. Although much technical
work has been done in various task teams, and a Draft Agreement with Annexes has been prepared.
A Summit of Heads of State and Government is to be held before mid-2011, and it is at this meeting
that the Tripartite FTA negotiations will be launched. This collection of studies therefore comes at
an important time to contribute to these negotiations.
The Trade Law Centre for Southern Africa (tralac) has, with the support of the National Agricultural
Marketing Council (NAMC) of South Africa, focused in this book on several issues that will be
feature prominently in the forthcoming Tripartite FTA negotiations. Agriculture plays an important
role in all the economies of the proposed FTA, in broad economic, trade and social terms.
Agribusiness and agricultural trade opportunities are often hampered in a regional context by the
designation of products as ‘sensitive’ and their exclusion from liberalization, as well as from a range
of non-tariff barriers. Agriculture, and these related issues are therefore the core focus in this book.
In addition a broad assessment of the impact of the Tripartite FTA is included. This computer-based
analysis provides a broad-brush review of the potential gains from the establishment of the FTA.
Trudi Hartzenberg
Executive Director, tralac
Cape to Cairo - An Assessment of the Tripartite Free Trade Area
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Cape to Cairo - An Assessment of the Tripartite Free Trade Area
Acronyms
ACP
African, Caribbean and Pacific
ADA
Anti-Dumping Agreement
AGOA
African Growth and Opportunity ACT
AMAD
Agricultural Market Access Database
ASEAN
Association of South-East Asian Nations
AVEs
Ad Valorem Equivalents
BLNS
Botswana, Lesotho, Namibia and Swaziland
CET
Common External Tariff
CGE
Computer General Equilibrium
CIF
Costs of Freight and Insurance
COMESA
Common Market for East and Southern Africa
CPI
Consumer Price Index
EAC
East African Community
EC
European Commission
EPA
Economic Partnership Agreement
EU
European Union
EV
Equivalent Variation
FAO
Food and Agriculture Organisation
FDI
Foreign Direct Investment
FOB
Free on Board
FTA
Free Trade Agreement
GATT
General Agreement on Tariffs and Trade
GDP
Gross Domestic Product
GSP
Generalised System of Preferences
GTAP
Global Trade Analysis Project
HS
Harmonised System
ITAC
International Trade Administration Commission
LDC
Least Developed Country
MERCOSUR
Common Market of the South, Latin American trade organization
MFN
Most Favoured Nation
MPS
Market Price Support
NAMA
Non-Agricultural Market Access
NPC
Nominal Protection Coefficient
NRA
Nominal Rate of Assistance
Cape to Cairo - An Assessment of the Tripartite Free Trade Area
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Cape to Cairo - An Assessment of the Tripartite Free Trade Area
NTBs
Non-Tariff Barriers
NTMs
Non-Tariff Measures
PSE
Producer Support Estimate
PTA
Preferential Trade Agreement
RoO
Rules of Origin
ROW
Rest of the World
RSA
Republic of South Africa
RTA
Regional Trade Agreement
SACU
Southern African Customs Union
SADC
Southern African Development Community
SCM
Subsidies and Countervailing Measures
SPS
Sanitary and Phytosanitary
SSG
Special Safeguards
STE
State Trading Enterprises
TBT
Technical Barriers to Trade
TPR
Trade Policy Review
TRQ
Tariff Rate Quotas
UNCTAD
United Nations Conference on Trade and Development
US
United States (of America)
WTA
World Trade Atlas
WTO
World Trade Organisation
XSC
Rest of SACU (Lesotho, Namibia and Swaziland as used in GTAP)
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Cape to Cairo - An Assessment of the Tripartite Free Trade Area
Contents
Summary
Cape to Cairo - An Assessment of the Tripartite Free Trade Area
Ron Sandrey
1
Chapter 1
The economic and agricultural policy setting
for the tripartite Free Trade Agreement
Ron Sandrey and Nick Vink
8
Chapter 2
Agricultural production in the tripartite region
Ron Sandrey and Nick Vink
40
Chapter 3
Intra-African trade in southern and eastern Africa and the role of South Africa
Ron Sandrey and Hans Grinsted Jensen
61
Chapter 4
The tripartite Free Trade Agreement: A computer analysis of the impacts
Hans Grinsted Jensen and Ron Sandrey
108
Chapter 5
Tripartite Agribusiness Opportunities for South Africa
Ron Sandrey
152
Chapter 6
An assessment of agricultural sensitive products
within the Cape to Cairo Tripartite Region
Taku Fundira
170
Chapter 7
Non-tariff barriers affecting trade in the COMESA-EAC-SADC
Tripartite Free Trade Agreement
Willemien Viljoen
189
Editors’ and Authors’ Profiles 2011
213
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Summary
Summary
Cape to Cairo - An Assessment of the Tripartite Free Trade Area
Ron Sandrey
The objective for this book is to examine agricultural production and trade and the
agricultural regimes in East and Southern Africa to provide a background for moving towards
the proposed COMESA-EAC-SADC tripartite free trade area. This background is followed
by a computer analysis of the benefits of such an FTA, along with a look at sensitive
agricultural products and non-tariff barriers in the region.
1.
Agricultural production in the tripartite region
A special emphasis in this project is given to agriculture, and the objective for this Chapter is
to examine agricultural production trends in the region as a basis for the subsequent
analysis. This analysis makes extensive use of the United Nations Food and Agriculture
Organisation (FAO) data. The section starts by providing an analysis of agricultural
production in Africa as a whole as well as in the tripartite region countries in order to place
the tripartite countries in perspective before moving on to examine the individual countries
and their associated commodities in more detail. The generalisation is that while Africa has
done well with agricultural production per se it has not done so well on a per capita basis.
The chapter ends with an analysis of undernourishment and food aid in the region.
2.
Intra-African trade in Southern and Eastern Africa and the role of
South Africa
The purpose of this chapter is to provide a background of agricultural trade in the region. .
Any analysis of this nature needs to take into account the problem of regional data quality
and timeliness, and for this reason a wide range of data sources have been investigated and,
where possible, the best data for the specific components has been used.
First, the recently-built Global Trade Analysis Project (GTAP) African database is used to
present what is possibly the most accurate (but dated) report on intra-Africa trade data,
Note: Ron Sandrey worked on chapters 1, 2, 3 and 5 while on the Hobart Houghton Fellowship at Rhodes
University in August/September 2010.
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Summary
including agricultural trade. This is followed by an examination of the ‘mirror’ data for most
of the major countries exporting agricultural products to Africa using World Trade Atlas
(WTA) data for the December years through to and including December 2009, and then by
an analysis using the UN Food and Agricultural Organisation (FAO) data to look at a
consistent but again slightly dated analysis of African imports by product. Finally, given the
focus on the implications for South Africa, South African agricultural trade with the tripartite
region is analysed, again using WTA data.
The overall conclusion from detailed agricultural trade analysis of partner country exports to
Africa is that the EU was by far the main supplier during 2009, but that along with the US its
exports declined from the 2008 levels; that both the EU and US in particular show exports
declining in 2009 over 2008; that most other countries have seen a greater export growth
from 2000 than the EU and US; and that the main destinations are Algeria, Egypt, Nigeria and
Sudan, with wheat, rice, palm oil, sugar, tea, milk powders and wine being the main products
exported from the major sources examined.
The COMTRADE data confirms the dominance of the EU in African trade, and reports from
their database that South Africa, Egypt, Morocco, Nigeria and Algeria were the biggest
traders in 2008 overall. They report total African imports of $380 billion, although there is
missing data. For the tripartite countries, COMTRADE reports imports of $206 billion, with
13.0 percent intra-African trade. Mirror data for the missing reporters adds another
$42 billion, with 7.6 percent of this intra-African trade. Tripartite agricultural imports were
$29.5 billion, 10.8 percent of the total. The main agricultural importers were Egypt,
South Africa, Angola and Libya, with wheat (Russia and the US), palm oil (Indonesia and
Malaysia) and maize (USA and Europe) the main import products.
Looking just at the African sub-set of the tripartite countries we found that the main
exporter to the region is again the EU, followed by the US and Brazil. The percentage of
African exports destined for the tripartite countries varies from a low of 28.0 percent for
Thailand to 91.1 percent for Australia. Egypt and South Africa figure prominently as major
markets, with Sudan being Australia’s main market. Examining the best-available African
import data from COMTRADE during 2007/08 we found that of the top thirteen specific
agricultural imports that accounted for 76 percent of the total imports the main two sources
in all cases were external to Africa. Intra-African agricultural trade is not significant. Earlier
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Summary
analysis of the 2001 GTAP database confirms this, and adds that where there was a degree
of intra-African trade South Africa was often the main source of this trade.
3.
The impact of agricultural policies in Africa
This chapter outlines the agricultural policy and trade settings for the individual countries of
the tripartite grouping. It does this by examining these settings in each of the three major
groupings of SADC, EAC and COMESA sequentially. In general, the major sources of
information are:
•
The WTO Trade Policy Review Mechanism (TPRM) documents that are published by
the WTO about its members every few years;
•
The seminal work on global agricultural policies undertaken by Kym Anderson and his
colleagues at the World Bank (Anderson et al, 2009) and the individual country
contributors to this study; and
•
FAO trade and production data for agricultural products during 2009 using the top-20
production, export and import commodity lines to provide additional background
information. While it is an underestimate of both production and trade in many
instances, it is a consistent source across countries and augments the policy profiles.
4.
Agribusiness Opportunities
The modern focus of trade is to move past commodity trade and try to search out more
value-added products that offer opportunities for producers, processors and traders. The
objective of this chapter is to start that examination of implications for South Africa from
regional integration. This has been done using the FAO trade database where there are 484
agricultural product lines listed for the latest complete data of 2007 to look at what the
tripartite region is importing and what South Africa is exporting in order to get a preliminary
idea of where some opportunities exist. The emphasis is on opportunities in the more
distant EAC and COMESA markets because the SACU market is already integrated and
SADC is hopefully moving in that direction with its own FTA. Conscious that the FAO
database provides trade data only for agricultural outputs, World Trade Atlas data have
been used for an indication of where opportunities may lie in the wider agribusiness input
sectors. We also extent the definitions of ‘agribusiness’ to include goods generally associated
with agricultural production, processing and marketing.
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Summary
5.
The GTAP modelling paper
In this chapter we examine the implications of the so-called tripartite countries of SADC,
EAC and COMESA entering into a genuine free trade agreement (FTA). We use the Global
Trade Analysis Project (GTAP) latest pre-release Version 8 database to assess the welfare
and trade gains from this FTA as determined by duty-free merchandise goods access and
with a small (two percent) reduction in assumed non-tariff barriers to both merchandise
goods and services barriers also factored in. Importantly, our simulation starts from the
assumption that the three regional blocs of SADC, EAC and Comesa all have their FTAs
operating in a comprehensive manner in that all three have tariff-free trade within their
blocks but not for outside of their blocks. Thus, our results relate to combining these three
blocks into one large tripartite FTA and not the adjustment process to reach this point. The
updates made and assumptions used are detailed in the main chapter.
For the final tripartite agreement only the results show that there are significant gains to
South Africa, but only for South Africa and Mozambique. South Africa welfare increases by
US$1,321 million or 0.22 percent of the real Gross Domestic Product (GDP). The gains to
South Africa derive from a better use of land, labour and capital (enhanced allocative
efficiency), increased net investment increasing the amount of capital employed in the
economy, and some contribution from increased labour employment and terms of trade
gains. Although these gains cannot be taken as exact figures but rather as indicative, they
suggest that an FTA covering the entire Eastern part of Africa is a policy objective that
warrants every consideration. They are much more than the gains from either an FTA with
China (with its associated impacts on South African manufacturing) or with Mercosur (with
its associated impacts on the agricultural sector) from earlier tralac research.
Results for the rest of SACU (Lesotho, Namibia and Swaziland) are disappointing with a
welfare loss of $84 million, while Botswana similarly loses $16 million. Most other tripartite
partners gain or lose very marginally, excepting Mozambique which gains $57 million. This is
because most countries other than South Africa and Mozambique have access to other FTAs
through their multiple membership of overlapping FTAs. All non-African countries outside
of the agreement lose.
For agriculture, the tripartite FTA is only beneficial in sugar and then only for the
South African and Mozambique agricultural sectors.
Manufacturing is the big gainer for
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Summary
South Africa, but again really only for South Africa with lesser gains for Egypt and rest of
eastern Africa (Kenya). Revenue for the SACU tariff actually increases by US$49 million as a
result of South African manufacturing imports from non-African countries to replace
increased exports to rest of east Africa and employment and real wage outcomes are both
positive for South Africa
Alternative scenarios
Negotiating and implementing an FTA is a complex and drawn out process, and this is
especially the case in Africa where many nations have limited technical capacity to undertake
these negotiations and inadequate institutional frameworks to base agreements upon – to
say nothing of political obstacles. Recognising this we use the GTAP model to explore an
alternative option called “Proudly Africa” that is simply a continent-wide campaign to “Buy
Africa”. It is not driven by tariff preferences but merely a cooperative understanding to
increase the share of trade sourced from within Africa.
Gains to Africa are a significant $3,871 million. These gains accrue to all African
countries/regions except Botswana, and Angola/DRC. The big winner is manufacturing in the
continents’ powerhouse South Africa although this time we are seeing solid gains also from
the rest of West Africa aggregation and Nigeria. All non-African countries lose and world
welfare declines as Africa marginally turns away from the global low-cost producers. Thus,
Proudly Africa is good for Africa but not the world.
Proudly Africa presents an interesting study that shows large gains can be made in the
absence of a formal negotiation that undoubtedly would require difficult and protracted
negotiations. It is simply a non-binding agreement to encourage the purchase African
merchandise goods, and is modelled by increasing imports of African products by
ten percent as applicable. It clearly shows that the way forward for Africa that is largely in
African hands. Proudly Africa capitalises on World Bank data for GDP growth over the
current decade that is reinforcing the concept that this century may belong to Africa. Its
beauty lies in its simplicity and in the final analysis the welfare gains are significant and
relatively evenly spread.
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Summary
6.
Sensitive Products
We note that the issue of sensitive products for exemption from tariff liberalisation in the
different countries/regional groupings may become an area of contention in the tripartite
FTA negotiations, simply because much of the basis for this exemption designation is likely
to be arbitrary, and the sensitive products are more likely to reflect protectionist interests
or rent-seeking behaviour, both of which will perpetuate inefficiencies. There have been
discussions around the issue of sensitive products within the individual RECs and within the
tripartite FTA itself thus emphasising the significance of this issue and therefore we urge
countries to base their selection on genuine public policy objectives.
This chapter provides an overview on what motivates countries to seek for flexibilities for
certain products from full liberalisation. We start with a review of how the issue of sensitive
products is being handled at the multilateral and regional level before becoming focussed on
agricultural trade liberalisation within the tripartite FTA and to what extent agricultural
products form part of the sensitive products list.
Sensitive products can undermine the process of deeper integration, and furthermore the
lack of resources and analytical capacity to undertake detailed analysis and the lack of
properly defined guidelines and benchmarks also hinders the process. We presume that this
may partly explain the delays by some countries in COMESA and SADC to identify and
notify to the respective Secretariats, their lists of sensitive products.
The chapter concludes by highlighting the need for policy makers to find ways to develop a
systematic approach to determining sensitive products and to ensure that all stakeholders
are aware of the purpose of a sensitive products list. The current list of sensitive products
reflects poorly on the desire to establish a CU with a CET, to which all the RECs have or
aspire to attain.
7.
Non-tariff barriers in the EAC-COMESA-SADC Tripartite FTA
Successive round of Multilateral Trade Negotiations have led to a decrease in the use of
tariffs as barriers to trade. However, the reduction in tariffs has been substituted by the
utilisation of non-tariff barriers, which are the basis of this Chapter. NTBs are defined as
being any barrier to trade other than import and export duties. This includes export taxes,
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Summary
import bans, government monopolies, cumbersome documentation requirements and a lack
of physical infrastructure.
The chapter highlights the most prevalent NTBs hindering regional trade in the tripartite
territory. These include customs procedures and administrative requirements, technical
standards, government participation in trade and the lack of physical infrastructure. We note
that this is of particular importance to agricultural trade within the region. Cumbersome
documentation requirements, stringent standards and inefficient road and rail networks
cause time delays and increase the cost of intra-regional trade. This has a direct and indirect
impact on the quality and price of agricultural products available in the regional market.
In the analysis we also note that the member states of the EAC, COMESA and SADC have
realised that NTBs are a major impediment to the expansion of intra-regional trade and
identified the elimination of NTBs as one of the key objectives of the Draft Tripartite
Agreement. In order to reach this objective a Web-based NTB Monitoring Mechanism has
been put into place. Although the Mechanism has been successful in resolving various NTB
complaints among trading partners, there are still a range of NTBs prevalent in the region.
The chapter concludes by highlighting that in order to enhance regional development and
promote intra-regional trade the tripartite member states need to intensify efforts to
address NTBs on a regional basis.
April 2011
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
Chapter 1
The economic and agricultural policy setting
for the tripartite Free Trade Agreement
Ron Sandrey and Nick Vink
In this chapter the economic fundamentals of the tripartite countries are explained in order
to place these countries’ economies in perspective before continuing to examine the
agricultural policy settings of the countries in order to similarly place their agricultural
sectors in perspective. Data on the economic settings is sourced from the United Nations
World Development Indicators, the Food and Agricultural Organisation (FAO) and the
Central Intelligence Agency (CIA) websites, with the most consistent recently available data
used.
1.
The economic setting
The objective for this section is to set the scene for a ‘Cape to Cairo’ tripartite Free Trade
Agreement (FTA) by outlining the relative economic facts of the 26 countries that make up
this region. This is shown in Table 1. The first point to note is the concentration of the
Gross Domestic Product (GDP): South Africa, Egypt, Angola, Libya and Sudan account for
79.2 percent of the total regional GDP. Conversely, the smallest 12 countries represent only
4.5 percent of the total with a combined GDP of little more than Kenya’s. South Africa alone
accounts for 38.7 percent of the GDP (and an even greater 40.6 percent of the regional
Gross National Income (GNI), while Egypt accounts for another 17.8 percent of the GDP.
Table 1 also shows the wide dispersion in GNI per capita. This ranges from $9,010 (Libya) to
Burundi with $110. A great numbers of impoverished people live in the region – 15 of the 26
countries have an average GNI per capita below $1,000 annually. Note that this data reflects
the actual GNI per capita figures, and that when adjusted for purchasing power parity (PPP)
they often increase by as much as three times at the poorest end of the spectrum. For
example, the American CIA (2010) reports that the Democratic Republic of Congo (DRC)
ranked 227th on its listing of GDP per capita using purchasing power parity (last in the CIA
2009 listings) but with a value per capita of $300 as opposed to the $140 shown in Table 1.
Using this 2009 CIA data for PPP GDP the region can be placed in perspective globally, as 12
of the 26 tripartite countries are placed in the bottom 28 places on their listing: from
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
Tanzania’s 200th place to the DRC’s number 227. These countries are Zambia, Tanzania,
Uganda, Comoros, Madagascar, Ethiopia, Rwanda, Malawi, Mozambique, Eritrea, Burundi and
the DRC. It is no coincidence that the CIA’s listing of birth rates also shows that eight of the
top thirteen in the world are in the region, as in order for per capita GDP to increase GDP
growth must exceed the rate of population growth.
The right-hand side of the table shows the agriculture value-added (contribution to GDP),
again reflecting considerable variation. The resource-rich countries, particularly Botswana,
Libya and South Africa (the only country in Africa with significant industrial capacity) are in
stark contrast to many of the poorer countries such as DRC, Tanzania, Ethiopia and Rwanda
where this contribution is 40 percent or more. Next is the import trade data, with the
percentage of imports designated as ‘food’ and the derived value of the food imports. Many
countries have food imports above the average 12.6 percent of total imports, as that average
is reduced by South Africa’s 6.1 percent. Not shown is that the average food import value is
4.5 percent of GDP, and that several significant but very poor countries have a percentage
value above ten percent of their GDP (e.g. Eritrea and Zimbabwe at 18.4% and 18.3%
respectively and DRC at 10.2%). The oil-rich countries, Libya and Sudan, have food imports
expressed as a percentage of GDP of only 3.1 and 2.8 percent respectively (below the
tripartite average) while Angola’s 4.8 percent is only marginally above the average. Note at
the onset that the trade values for the so-called BLNS (Botswana, Lesotho, Namibia and
Swaziland) Southern African Customs Union (SACU) countries are significantly underreported in this and subsequent analysis as South Africa and the BLNS often do not report
their bilateral trade data.
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
Table 1: Economic settings for the Southern African Development Community
(SADC), the East African Community (EAC) and the Common Market for
Eastern and Southern Africa (COMESA), 2008
Reporter
SADC
South Africa
Angola
Botswana
Zambia
DRC
Namibia
Mozambique
Mauritius
Madagascar
Zimbabwe
Malawi
Swaziland
Lesotho
EAC
Tanzania
Kenya
Uganda
Rwanda
Burundi
COMESA
Egypt
Libya
Sudan
Ethiopia
Eritrea
Djibouti
Seychelles
Comoros
Total
Population
million
215.4
48
17
1.9
12
62
2.1
21
1.3
20
13
14
1.1
2
127.2
40
38
31
9.7
8.5
205.55
75
6.2
39
79
4.8
0.83
0.90
0.63
548
GNI
$bn
403.3
274
43
11.5
9.2
8.6
7.2
7.1
7
6.4
4.5
3.5
2.9
2.1
55.6
16.3
24
11.3
3.1
0.9
232.69
119.5
55.5
36.7
17.6
1.3
0.9
0.76
0.43
675.29
Per
capita $
1,872
5,720
2,540
6,120
770
140
3,450
330
5,580
320
340
250
2,560
1,030
437
410
640
370
320
110
1,132
1,580
9,010
950
220
270
1,090
8,960
680
1,232
GDP
$bn
434.8
284
61.4
12.3
11.4
9
7
7.8
6.8
7.4
3.4
3.6
2.9
1.6
56.5
16.2
24.2
11.8
3.3
1
258.02
130.5
58.3
46.2
19.4
1.34
1.1
0.73
0.45
733.12
Agriculture
share
of GDP
%
3
9
2
22
42
11
28
5
26
19
34
7
12
45
26
24
40
35
29.5%
14
2
28
46
24
4
3
47
Total
imports
$m
167,359
99,500
20,982
5,212
5,060
4,400
4,340
3,804
4,651
3,980
2,950
1,650
1,700
2,005
17,149
7,125
11,074
4,526
1,146
403
77,245
48,382
9,150
9,352
8,036
530
574
1,041
180
261,753
Food imports
%
Imports
8.9%
6.1
14
12.7
6
20.8
17.1
16.2
23.6
10.7
21.1
17.2
12.1
3.2
13.9
13
14
12.5
10
19.1%
19.7
19.9
13.8
13.9
46.5
77.7
22.4
32.1
12.6%
$m
14,946
6,070
2,937
662
304
915
742
616
1,098
426
622
284
206
64
2,257
990
1,440
634
143
40
14,743
9,531
1,821
1,291
1,117
246
446
233
58
32,936
Source: UN World Development Indicators and FAO
A salient point that emerges from this analysis is that the current SADC membership makes
a contribution of nearly two-thirds of the total GDP, and if the nascent drive to more fully
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
integrate the SADC economies becomes meaningful economic reality for Cape to Cairo, we
are really looking at integration with Kenya, Uganda and Egypt and then Libya, Sudan and
Ethiopia to realise the potential of the Tripartite Free Trade Area (referred to here as the
Cape to Cairo project. Conversely, without SADC integration it is doubtful that Cape to
Cairo will become a reality, and, importantly, within SADC the SACU grouping must stay
together, as current rumblings of discontent may undermine SACU’s cohesion.
2.
The impact of economic policies in Africa
The objective of Sections 3-5 is to outline the agricultural policy and trade settings for the
individual countries of the tripartite grouping. It will do this by sequentially examining these
settings in each of the three major groupings of SADC, EAC and COMESA. In general, the
major sources of information are:
•
The World Trade Organisation (WTO) Trade Policy Review Mechanism (TPRM)
documents that are published by the WTO about its members every few years;
•
The seminal work on global agricultural policies undertaken by Kym Anderson and his
colleagues at the World Bank (Anderson and Masters, 2009) and the individual country
contributors to this study; and
•
FAO trade and production data for agricultural products during 2009 using the top 20
production, export and import commodity lines to provide additional background
information. While it is an underestimate of both production and trade in many
instances, it is a consistent source across countries and augments the policy profiles.
In this section, a bird’s-eye view is taken of the impact of agricultural policies on producers
and consumers in the tripartite region, based on work of the World Bank. This extends the
work of the International Food Policy Research Institute (IFPRI) and the Organisation of
Economic Cooperation and Development (OECD) that has measured agricultural policy
distortions globally for several years by assessing the nominal rates of assistance (NRA) for
some 75 countries that account for between 90 to 96 percent of global agricultural
production. This included 21 countries in Africa, of which 11 are tripartite countries
(Table 2). A Nominal Rate of Assistance (NRA) for each major farm product is computed as
the percentage by which policies change the gross returns to farmers for that product
relative to what they would have received in the absence of these policies. A positive NRA
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
means that the country subsidises that particular product while, conversely, a negative NRA
represents a tax on the product. The weighted value of these individual products gives the
overall single NRA figure for the agricultural sector in that country.
Anderson (2010) provides an excellent review of this global research of agricultural
protection, both by region and through a time-series summary. He concludes that the
African NRA of -7 for the 2000-04 period represents the only global region that taxes
agriculture, as Asia, Latin America, the developing country average and high-income
countries all subsidise their agriculture, with the latter subsiding to the tune of an average of
32 percent. The global average is 18 percent, and this sets the African effective taxation of
agriculture in stark perspective. Anderson and Masters (2009) emphasise just how important
this is by citing World Bank data showing how Sub-Saharan Africa accounts for 12 percent of
the world’s farmers, 16 percent of the agricultural land and 28 percent of those living on less
than a $1 a day, while also having the world’s slowest agricultural growth in an environment
that is heavily interventionist and slow to reform.
Estimates of the effects of agricultural and related policies were assessed on eleven tripartite
countries as part of the World Bank project as reported by Anderson and Masters (Table 2).
These countries accounted for 84.6 percent of the region’s agricultural production in 2007
according to FAO data, the major omissions being DRC (4.2% of the agricultural production
in the region) and Malawi, Angola and Rwanda (another combined 7.4%). There is
considerable variation shown by country in the table; overall, for the 2000-04 period, South
Africa, Madagascar and Uganda are neutral, Mozambique and Kenya give moderate support,
Tanzania, Egypt, Sudan and Ethiopia tax quite heavily while both Zambia and especially
Zimbabwe are taxing severely. The averages at 2000-04 compared with those of the earlier
1995-99 are a mixed bag for Africa. While most of the developing world had shown a
reduction in agricultural taxation, this was not universally the case in the tripartite region.
Similarly, Column 4 shows that the NRA for the subsets of agricultural products deemed to
be ‘exportables’ from each country is well below the average NRA in all cases except
South Africa. This indicates a strong anti-trade bias in the region, and as such should
potentially benefit South African producers over their regional counterparts if the FTA
comes to fruition. The importance of agriculture to these economies is again emphasised in
the right-hand column that shows the percentage of total employment found in agriculture.
Table 2: Nominal rates of assistance (NRAs) to tripartite agriculture
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NRAs
1995-99
5.7
2000-04
-0.1
Exportables
9.5
Agricultural
employment as a
% of total
employment in
2000-2004
9
-28.6
-28.5
-52.6
68
Mozambique
3.9
12.4
-1
81
Madagascar
-2.9
1
-29.3
74
Zimbabwe
-20.8
-38.7
-66.7
62
Tanzania
-23.2
-12.4
-48.7
80
Kenya
2.4
9.3
-0.6
75
Uganda
0.5
0.4
-0.3
79
Egypt
4
-6.1
-28.1
33
Sudan
-24.5
-11.9
-34.2
60
Ethiopia
-17.8
-11.2
-17.5
82
South Africa
Zambia
NRA 2000-04
Source: Anderson and Masters (2009)
More detailed information is provided by Anderson and Masters (2009) on the individual
products and their degree of assistance. They identify four broad groups of products, the
first being those products that are generally supported, namely sugar (the most heavily
supported), sorghum and milk. The second group includes those products that are neither
supported nor taxed, including banana, plantain, wheat and cassava, while the third group
includes lightly taxed products such as maize, rice, coffee and possibly tea. The final group
includes the highly taxed products of sheep meat, beef, groundnuts, cotton and most of all
tobacco. The next step in the agricultural support analysis is to look at the impact of these
policies upon consumer prices, and while there may be linkages it is not a one-on-one
mapping between consumer support and the NRA. This World Bank study found in general
that Ethiopia and Sudan are the largest subsidisers of farm products in the region on a total
value basis, while on a percentage basis Zambia and Zimbabwe are the largest. Conversely,
only Mozambique and Kenya effectively tax food consumers. No particular product stands
out as being heavily subsidised, unlike in the developed world where milk, rice and sugar are
heavily supported.
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In summary, the World Bank study on Africa found that while African countries have
removed much of their anti-farm and anti-trade bias, substantial distortions remain and these
impose a large burden on Africa’s poor. Trade restrictions are the most significant source of
these farm distortions. This African picture is in stark contrast to Asia and Latin America
where policies have moved to moderately supporting agriculture, and the developed world
where most of the rich countries heavily support agriculture.
3.
Agricultural and trade policies in SADC
To recognise the special place that sugar holds in SADC it is appropriate to start with a
mention of sugar policies in the region. In examining regional policy it is clear that the sector
has not undergone the policy reforms in South Africa in particular that all other sectors
have. Anderson and Masters (2009) confirm that sugar is the most the most heavily
supported agricultural product in southern Africa, and the justification used by SADC is that
internationally the sector is heavily protected and operates in a distorted trading regime.
However, according to the OECD data the global average Nominal Assistance Coefficient
(NAC) for sugar is 1.96. This means that for the rich countries producers are receiving
nearly double the world market price for their sugar. This is not the highest – rice has a
NAC of 3.96, while other products are not that far behind sugar: sheep meat at 1.74, beef at
1.54, milk at 1.41 and wheat at 1.50, for example. Furthermore, according to OECD, data
producers in the major sugar exporting countries of Australia and Brazil receive no more
than token Producer Support Estimates (PSE) supports for their sugar. Moreover, under the
terms of the Economic Partnership Agreements (EPAs) currently being negotiated with the
European Union (EU), the EU has offered non-South African Africa duty- and quota-free
access for sugar following a transition period, albeit a transition that has some provisos
attached to it. Sugar’s special treatment extends to trade restrictions on free trade from
non-SADC members into SADC as set out the SADC Protocol on Trade1, and that this
special treatment is not given to any other product, either agricultural or non-agricultural.
We now turn to general policies in SACU where Sandrey and Vink (2010) reported on
agricultural positions and policies in these five countries. This discussion of the SACU
countries draws on that work.
1
This seemed to be operational since 2007, but as of February 2011 it was under revision.
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
3.1
Botswana
The contribution of agriculture to both Botswana’s GDP and exports is very low. The beef
sector used to be the major agriculture product of importance to exports, but now accounts
for about 7 percent of total export revenue, compared to about 70 percent from diamonds.
Although the country is a net exporter of beef, it has always been a net importer of staple
food commodities such as sorghum and maize, which together account for over 90 percent
of domestic cereal production. The dualistic agriculture sector consists of commercial and
traditional subsistence subsectors; each undertakes both crop and livestock activities.
Commercial agriculture covers about 30 percent of the arable land, comprising mainly cattle
grazing on freehold or lease. The major subsistence crops are sorghum, maize, millet, and
pulses. Beans, groundnuts, sunflowers, cotton, and horticultural crops such as cabbages,
tomatoes, and potatoes are also grown. Although the economic significance of subsistence
agriculture is declining, it remains important for many people, particularly in rural areas
where there are few alternatives. Botswana is about 20 percent self-sufficient in grains,
15 percent in vegetables, 25 percent in fruits and 3 percent in dairy products. It produces
almost all of its poultry requirements, and is a net exporter of beef, exporting some
90 percent of production to the EU under preferences and to South Africa behind a
40 percent SACU tariff.
With respect to trade policies, imports of fresh pork are banned and poultry imports are
permitted only when there is a shortfall in the domestic market. In a normal year Botswana
produces only about 30 percent of its annual cereal demand. Imports of maize, wheat,
sorghum and related products, pulses, fresh milk, major fruits (e.g. oranges, watermelons)
and vegetables (e.g. cabbages, rape, spinach, potatoes, and tomatoes) are restricted; through
to early 2008 imports required a permit from the Ministry of Agriculture, but this has been
abolished. This system is designed to protect an infant horticultural industry, which meets
about 20 percent of requirements.
3.2
Lesotho
Lesotho, a small mountainous and land-locked country is not only resource-poor, but it also
faces considerable problems associated with environmental degradation and soil erosion on
its 9 percent (and shrinking) portion of the total land that is classified as arable. The only
substantial natural resource is water. Agriculture is dominant, as 80 percent of the
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
population of around two million live in rural areas deriving their livelihood from crop and
animal production. The sector is dominated by crop production, with maize as the main
staple crop. Wheat, sorghum, beans and peas are also cultivated. Other higher-value crops
include sunflower, asparagus, garlic, paprika, soya beans, potatoes, fruits and other
vegetables. Livestock production (around 30 percent of agricultural value-added) includes
cattle, goats, sheep, donkeys and horses under extensive range management systems, with
wool and mohair the only significant agricultural export. Cattle exports have traditionally
accounted for around one-third of agricultural exports, but to rural people livestock
functions as a store of wealth and is used to perform cultural activities. While this sector
plays an important role in rural income, overgrazing has an adverse impact on rangeland and
water resources, and the cattle herd in Lesotho is far from the international ‘norms’ for beef
or milk production.
One cannot isolate agricultural policies in Lesotho from its food security, and a disturbing
aspect of its agricultural performance is that in recent years Lesotho has been a regular
recipient of food aid. Perhaps 60 percent of the population is considered to live below the
poverty line, a proportion that is increasing.
3.3 Namibia
Commercial farming is undertaken by some 4,500 farmers and focuses mainly on beef
production for export to the EU under preferential arrangements, and live cattle, sheep, and
goats to South Africa, while some maize, wheat, and cotton is also produced. Communal
farming supports 95 percent of the nation’s farmers, and covers about half of total
agricultural land. This subsector is home to 65 percent of the population, and in a normal
year produces grain and supports around half of the beef and small livestock populations.
Overall, beef production accounts for about 80 percent of Namibian meat production and
for 40 percent of total agricultural output, while cereals provide about 50 percent of the
total calorie intake. The country imports about half of its cereal requirements, and imports
of sugar and dairy products are important.
Imports are prohibited for white maize meal, yellow maize meal, wheat flour (a SACU
agreement), honey and bees’ eggs from overseas and African sources (except when
imported through South Africa), fresh apricots, cherries, peaches, plums and sloes from
overseas destinations, coloured, polished, steamed or additive-coated coffee beans, coffee
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
substitutes containing less than three-quarters of its mass of coffee, and wines, other
fermented, beverages, spirits and vinegar not conforming to the provisions in the Liquor Act
(with larger volumes subject to taxation).
3.4 South Africa
In 2002, agriculture employed 9.7 percent of employment in the formal sector, and by 2009
this had declined to less than 5 percent. Agricultural exports have grown since the demise of
apartheid, but have declined as a percentage of total exports – from 35.21 percent in
1965-69 through to 8.18 percent in 2000-2005. About one-third of agricultural production is
exported, and processed products represent around 65 percent of all agricultural exports.
At the same time, South African agriculture is highly dualistic with a small number of
commercial operations (some 40,000) run predominantly by white farmers and large
numbers of black subsistence farmers. In 2002, fewer than 2500 farmers (6.6% of the total)
earned more than 50 percent of the total gross farming income of the sector.
By the late 1970s, the racial segregation of South African agriculture was complete,
subsidisation of commercial farming peaked and the productive base of the farming sector in
the homelands ceased to provide any meaningful income opportunities to all but a handful of
farmers. In the period around 1980, however, farm policy started to change. After 1994,
South Africa adopted a policy of openness and limited intervention in markets. The policy
objective was to promote trade and therefore competition that would result in efficient
allocation and use of resources as well as increased economic activities. This led to the
deregulation of both agricultural and trade policies as supports were reduced, markets
deregulated, border tariffs reduced and export subsidies eliminated. These changes were
dramatic and South Africa now has a very lightly protected (but still very dualistic)
agricultural sector. This is confirmed by Kirsten, Edwards and Vink (2009) who assessed the
NRA for South African sectors and showed that especially sugar and then yellow maize and
perhaps oranges are the only major products being heavily supported while the other main
products are basically neutral or lightly taxed.
3.5 Swaziland
Swaziland is a small land-locked country with high levels of poverty and income inequality.
Agriculture is the backbone of the economy, but is acutely dualistic. A dynamic commercial
subsector occupies 26 percent of the land, holds an estimated 90 percent of available
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
irrigation infrastructure, and uses modern technologies to produce mainly cash crops
(sugar). A traditional subsector involves smallholder farmers with communal grazing and
low-productivity subsistence agriculture and animal husbandry.
Sugar and sugar-related products are the major export, heavily dependent upon preferential
access into the EU. Around 60 percent of agricultural production is focused on the sugar
sector and it contributes some 11 percent of GDP. Swaziland has not been self-sufficient in
cereal production since 1980, and by the early 2000s food production had further declined
to only 40 percent of the nation’s needs. Many people are vulnerable and food-insecure in
the country, with the main contributing factors the high poverty rate, inequality of income
distribution, the high incidence of HIV/AIDS, chronic drought, widespread soil erosion and
land degradation, lack of agricultural land, isolation from markets, limited alternative income
generating opportunities, gender restrictions for women to access land and resources, and
lack of implementation of appropriate policies. Achieving a productive and competitive
agriculture sector will require addressing this complex set of constraints.
3.6
Zimbabwe
In the recent history of agriculture this country that was once regarded as the bread-basket
of Africa is a tragic example of the consequences of economic mismanagement of the sector.
Over the period 1990 to 2007 FAO data shows that Zimbabwe suffered an average annual
decline of 0.7 percent in agricultural production, and when per capita production is taken
into account this decline is even more dramatic as reflected in the FAO Index that started at
149 in 1960 but fell by more than half to 68 in 2007. Economic mismanagement has to be the
reason, as Zimbabwe has not been in civil war since Independence in 1980.
The decline of Zimbabwean agriculture is also clearly reflected in the FAO export statistics
of the top 20 trading products. In 2000 the country exported $967 million in agricultural
products, with tobacco heading the list at $566 million, followed by cotton at $174 million.
By 2007 these exports were less than half at $471 million: tobacco had dropped to
$245 million and cotton to $95 million. Conversely, imports had tripled – from $106 million
in 2000 to $327 million in 2007, with maize heading the list at $118 million. As of 2007, the
main production was from tobacco, cotton, maize, cow’s milk and sugar. This dismal trade
performance is mirrored in the production data: the value of tobacco output declined by
almost two-thirds by 2007, maize had halved, while cotton was down by a third from 2000.
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
Table 1 shows that the sector is heavily taxed, with an NRA of -38.7, the highest of the
countries examined by the World Bank, with only Zambia coming close at -28.5. Ndlela and
Robinson (2009), the authors of the World Bank study, found that these NRAs were
consistently high with taxation levels between -63 and -81 percent of the market value of
groundnuts, cotton, tobacco, wheat, maize, soybeans and sorghum. Perhaps surprisingly for a
country with a history of export oriented agriculture, these NRAs were consistently
negative from the mid-1970s (before Independence) at rates roughly two-thirds of the
current levels. Central to the Zimbabwean story is land rights: after Independence the
resettlement process progressed slowly but relatively smoothly until 2000, when the
precipitous ‘fast-track’ land reform programme was implemented. The rest is, as they say,
history, with no seemingly visible solution apparent. White-owned commercial farms have
mostly been expropriated and reallocated to new owners in a discriminatory manner in a
highly politicised environment, while these farms are now largely occupied by people unable
or unwilling to make productive use of the land. Ndlela and Robinson (2009) argue that the
taxation of agriculture can be ascribed to three main factors, namely agricultural policies that
have driven down producer prices, offset to some extent by intermittent direct subsidies;
market imperfections, particularly state buying practices which deprive farmers of their
returns; and macroeconomic instability, especially in terms of the exchange rate.
3.7
Zambia
In their latest TPRM of Zambia the WTO (2009) considers that agriculture’s contribution to
GDP is misleading, since, although the sector absorbs about two-thirds of the labour force,
this does not take into account the downstream agro-processing industries which depend
directly on agriculture and constitute 60 percent of Zambia’s manufacturing sector. Growth
in agriculture, however, has remained low, and enhancing the productivity of small-scale
farmers by reducing their dependency on rain-fed agriculture remains critical to the
development of the sector and the reduction of rural poverty. There are a small number of
large commercial farms which account for about 45 percent of the country’s agricultural
output, and around one million smallholder farmers who grow crops focused on maize,
cassava, rice, cotton and tobacco. Maize is the crucial crop, as it provides over half of all
calories consumed. Dependence on small-scale rain-fed maize production leads to volatile
output and Zambia’s maize crop is inadequate in around one year out of three. In good years
Zambia exports maize and in bad years it imports, and checking the FAO data shows that
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
this has been apparent in recent years. The problem, as in much of the region, is that small
size leads to a lack of mechanisation and economies of scale, with consequential low labour
productivity and hence poverty. Copper accounts for over three-quarters of total exports,
although the government promotes non-traditional exports, and primary agricultural
products accounted for more than a third of such exports, with floricultural and
horticultural products as important. Tobacco exports have increased following the influx of
commercial farmers from Zimbabwe.
The FAO reports that for 2007 the main agricultural products by value were maize, cotton,
cassava, game meat and sugar, while the top 20 exports of $238 million were slightly more
than double the agricultural imports of $112 million. The main exports were tobacco with
half of the total at $115 million, followed by cotton ($39m), sugar, wheat flour and peas. The
main imports for 2007 were fats, other food preparations, cigarettes, maize and wheat.
Table 2 shows that Zambia continues to penalise its agricultural sector through the policy
regime, as the NRA of -28.5 during 2000/2004 is (a) almost the same as that of the previous
period and (b) at a level of at least twice any other country surveyed in the region except for
the melt-down sector in Zimbabwe. Robinson et al. (2009), in calculating these NRAs, show
that all products are penalised, with the heaviest taxes falling on groundnuts, cotton and
tobacco. They also report that the three main reasons for this long and continued
penalisation of the sector are the direct influence of agricultural policies, the monopsonistic
structure of agricultural markets, and the indirect but significant influence of macroeconomic
mismanagement which has led to an overvaluation of the currency for most of the previous
five decades, largely as a result of the dominant copper exports. Their conclusion is that
there has been a failure to achieve anything like the potential of Zambia’s agricultural sector
mostly as a result of the perverse effects of subsidies and other interventions in food
markets that have imposed immense costs on the economy. This in turn is a significant
factor in the widespread persistence of poverty.
3.8
Madagascar
The WTO (2008) reports that Madagascar’s economy is mainly based on services (around
57 percent of GDP), followed by agriculture, including fisheries, livestock and forestry (27
percent), and manufacturing (16 percent). Around three-quarters of the population earns
most of its income from mainly subsistence farming and performance is generally poor
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
because of the small size of farms and the rudimentary cultivation techniques. Some
60 percent of agricultural GDP comes from crops, 25 percent from livestock and fishing and
the remainder from forestry. Around 70 percent of household spending is on food, so food
prices are crucial to purchasing power and consequential poverty.
The major crops are rice (the basic foodstuff), roots and tubers, industrial crops
(groundnuts, sugar cane, cotton, tobacco) which provide the raw material for local agroindustrial units, and some cash crops mainly intended for export (vanilla, cloves, pepper,
coffee, cocoa). FAO data shows that the country has a significant livestock sector and the
production of both milk and meat is important. The FAO confirms that for 2007 production
was headed by rice and followed by cassava, cow’s milk, sweet potatoes and fresh
vegetables. According to the report agricultural imports of $247 million for the same period
were headed by rice ($57m), soybeans ($30m), food wastes, wheat and sugar, while exports
of $181 million where headed by vanilla ($49m), cloves ($33m), cocoa, fruit preparations and
coffee.
The state no longer intervenes in fixing the purchasing price from farmers but subsidises the
supply of seed and inputs and small-scale equipment for growing rice (WTO, 2008), and
Table 2 shows that overall the sector has moved from slight taxation to a neutral or slightly
positive regime. Maret (2009), in assessing this level of support, shows that agriculture’s
performance since the 1950s has been insufficient to cope with demographic pressures or to
contribute to a significant reduction of poverty. This is accentuated by the population
growth from 4.2 million in 1950 to around 20 million currently, as this puts intense pressure
on the agricultural sector, and periods of civil unrest and political uncertainties have also
disrupted the rural economy and discouraged investment. A general finding from the World
Bank project analysis (Maret, 2009) is that producers’ incentives were increasingly distorted
in favour of urban consumers during the state intervention period of the 1970s. Those
distortions were then significantly reduced for most of the covered commodities as a result
of the liberalisation policies that were initiated beginning in the late 1980s, with the
exception of sugar and vanilla where domestic market inefficiencies still isolate producers
from world markets. Conversely, maize and to a lesser extent rice were supported during
the 2000-03 period, so therefore the neutral overall support regime does hide some
variations.
3.9
Mozambique
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
The WTO (2008) shows that the agriculture sector employs about three-quarters of the
labour force, and as they are mostly engaged in subsistence farming on an informal basis,
agriculture plays a central role in achieving poverty reduction. According to the report
exports of cashews, cotton, refined sugar, tobacco, and fishery products are also considered,
which, though low in terms of relative contribution to the total value of exports, are
significant in terms of their contribution to rural incomes. FAO data shows that agricultural
production is dominated by cassava ($363 million from a top 20 product total of
$1,064 million), followed by cotton ($140m), maize ($110m), tobacco, pulses and cashews.
Agricultural imports of $414 million are headed by rice ($128m), wheat ($93m), palm oil,
food preparations and soybeans. Exports to a total of $146 million include figs, anise,
pistachios, raisins and cotton.
Alfieri et al. (2009), in assessing the high positive NRA of 12.4 percent, discuss the
background to recent agricultural policy and outline how the country has gone about trying
to set a policy regime to harness the immense agricultural potential in what only a few short
years ago was possibly the poorest country in the world. The ex-Portuguese colony went
through a phase of socialism after gaining independence in 1975, and then from 1986 the
government initiated a programme of economic reform aimed at establishing a market
economy. At that time the country was immersed in armed conflict which, together with
other socioeconomic changes, caused production to continue declining through to the end
of the civil war in 1992. Since then, a combination of peace, political stability, economic
reform, and large aid flows has helped the country to achieve one of the highest economic
growth rates in the region. Poverty rates have been significantly reduced and agricultural
incomes have increased.
Alfieri et al. (Ibid.) suggest three main phases of agricultural policy change. In the first the
central government imposed fixed or minimum prices, and producers were clearly taxed and
consumers were subsidised. This was followed by price liberalisation in the 1990s, when
producer assistance levels became neutral or positive, mainly through market reforms and
the rationalisation of import duties and taxes. In the current period from 1999, intervention
has been largely restricted to import duties and VAT, while some specific sectors are more
highly controlled: sugar, through an import surcharge; cashew, through an export tax;
tobacco, through geographical concessions; and cotton, through minimum prices and closed
geographical concessions. The NRA rates accordingly moved from strongly negative in
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earlier years to positive across most import competing products, while exportables have
moved from strongly negative (taxed) to about neutral. However, these changes in policies
have still left rural areas with the highest incidence of poverty and the policy reversals have
not had much of a positive impact on production, nor has it ensured sustainable growth and
development.
3.10
Mauritius
According to the WTO (2008) Mauritius, a middle-income country, is a net food importer.
Agriculture (consisting mainly of sugar production) continues to be the important sector in
terms of its share in exports and on account of its linkages with other sectors. For food
security purposes, the country maintains import, export, and price controls, and/or strategic
reserve stocks on agricultural products, such as cereals and cereal products, animals and
animal products, sugar, spices, vegetables, and fruits. Marketing boards hold monopolies over
the import of products such as seed potato, whole onions, whole garlic, wheat flour, and
ration rice.
The FAO reports that sugar dominates agricultural production with a 76 percent share of
the top 20 products (and a higher 86 percent share of the top exports of $346 million).
Next comes hen’s eggs and a succession of fresh vegetables, while sugar exports are
followed a long way back by food wastes, wheat flour, non-alcoholic beverages and
cigarettes. Wheat ($50m) tops the list of the main imports, which total slightly more than
exports at $377 million. Next are rice, cotton, milk powders and food preparations.
The sugar sector is an interesting case study, as the over-reliance on sugar exported to the
EU under preferences has had the effect of ‘crowding out’ other parts of agriculture. As
these preferences are being eroded the country has to make adjustments to compensate.
Despite this, FAO data shows that agricultural production has remained stable since around
1990 in both real values and on a per capita basis. Perhaps the latter is a function of a slower
population growth associated with a middle-income country rather than the high rates
experienced through most of the region.
3.11
Malawi
Table 1 shows that Malawi is poor country, and that agriculture’s contribution to GDP (34%)
is high. Again, FAO data showing agricultural production lists Malawi (along with strifeCape to Cairo - An Assessment of the Tripartite Free Trade Area
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ravaged Angola) as having the largest regional production increase over the 1990 to 2007
period, but to put this into perspective the 1990 base year represents the lowest per capita
production in Malawi and the longer term picture is less rosy when an extended earlier
period is examined. The WTO (2010) confirms that Malawi's economy is heavily dependent
on agriculture and exports of primary commodities (tobacco, tea and sugar), but production
was volatile with major food shortages in 2002 and 2005 when Malawi experienced several
food emergencies. However, as the 2002 and the 2005 food crises eased, imports of
agricultural products declined. This production volatility is confirmed by looking at the FAO
data in detail, as the 2005 production was close to the lowest for ten years but by 2007 it
had increased dramatically to new highs. To improve the situation the WTO reports that
agricultural development and food security have been policy priorities of the government.
Because maize is the principal food and subsistence crop and central to any food security
strategy, the government started to subsidise fertilisers for maize and other agricultural
inputs. They consider that this programme, together with good rainfall, has been a success in
restoring food security.
Malawi's exports are concentrated, with agriculture accounting for nearly 90 percent of total
merchandise exports. The share of tobacco and cotton in these exports has risen recently
owing to good prices. For 2007 the FAO data shows that tobacco represented 56 percent of
agricultural exports that totalled $755 million, followed by maize ($100m), sugar, tea and
sunflower seed. Imports were much lower at $129 million, with tobacco and wheat (both
$30m) dominating, followed by soybean oil, palm oil and milk powders. Potatoes and maize
are the main agricultural products, followed by cassava, tobacco and groundnuts.
3.12
Angola
Angola’s poverty is concentrated in the agricultural sector, which, together with forestry and
fishing, contributes around eight percent to GDP and employs over 50 percent of the labour
force (WTO, 2006). Production is based largely (around 80 percent) on subsistence
agriculture. Before independence Angola was a major agricultural producer, and was selfsufficient in food and a significant exporter of coffee, cotton, bananas and sugarcane. Civil
war damage reduced the country to a net importer of food, and the presence of land mines
in certain areas and poor infrastructure continue to inhibit development. In addition, the
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strong exchange rate tends to work against the exports of this sector. The dominant and
enclave oil sector poses extreme challenges for income redistribution in a relatively poor
economy that is only just coming to grips with its post-war redevelopment and settlement of
millions of displaced persons. Meanwhile, the government recognises that it has insufficient
resources to intervene in the rural sector and is therefore trying to (a) set policies and
strategies that emphasise private sector participation and (b) revitalise public infrastructure
and institutions.
The recent FAO data shows that the main agricultural products from the sector are
dominated by cassava ($634m from a top 20 total of $1,202m), followed a long way back by
potatoes, sweet potatoes, maize and cow’s milk. Imports at $1,272 million are valued at
slightly more than production, and are headed by chicken meat, followed by wheat flour,
beer, wine and sugar. Conversely, agricultural exports have slumped to an almost nonexistent $7.4 million, with $5.5 million of these being palm oil.
3.13
The democratic Republic of Congo
It is difficult to obtain information beyond the basic indicators on the agricultural sector in
this war-torn and very poor country. Table 1 shows that its population is only surpassed by
Ethiopia in the region, but that its GNI is only marginally above Namibia’s and that
agriculture’s contribution to this GDP is one of the highest in the region. FAO data tells how
agricultural production has actually fallen over the years in total, and, importantly, in per
capita terms, this decline has been around 50 percent. The CIA reports that the economy of
this richly endowed nation is slowly recovering from two decades of decline. Conflict that
began in May 1997 has dramatically reduced national output and government revenue,
increased external debt, and resulted in the deaths of more than five million people from
violence, famine, and disease. Meanwhile, the uncertain legal framework, corruption and a
lack of transparency in government policy are long-term problems.
All the indications are that the agricultural potential is enormous, as the DRC has significant
land and water resources, but that currently the dominance of the subsistence sector, the
abject poverty of the sector and the almost failed-state status of the country mean that the
realisation of this potential is a long way off. Meanwhile the OECD confirms that
development of the food subsector is confronted with antiquated farming practices,
difficulties of access to quality inputs, especially seeds, lack of appropriate technology and
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material for processing and storage, and marketing difficulties in isolated areas. FAO reports
that the main products are cassava, plantains, groundnuts, game meat and maize. Imports of
$442 million are dominated by wheat ($68m), chicken meat, palm oil and wheat flour, while
exports totalling only $36 million are dominated by tobacco ($17 million) and coffee
($8.5 million), followed by rubber, wheat bran and cocoa beans.
4.
Agricultural and trade policies in COMESA
4.1
Egypt
FAO data shows that Egypt is the largest agricultural producer in the region, and reports
that during 2007 the leading products by value were tomatoes, rice, buffalo milk, wheat,
grapes and cow milk; the leading agricultural imports were wheat, maize, beef, soybeans and
tobacco; and the leading exports were rice, cotton, oranges, potatoes and grapes. As the top
20 imports were valued at $3,148 million while the top 20 exports were a much lesser
$818 million, Egypt is a significant importer of agricultural products. The country is also the
number one producer of dates in the world, and number two in both figs and goose meat.
The WTO confirms that agriculture’s contribution to GDP has been declining but the sector
retains a significant role in employment (about 34% of the labour force). Financial assistance
to the sector is provided in the form of subsidised electricity and water, the latter being
provided almost free of charge to farmers. The government also subsidises various food
products, most notably bread, sugar, and oil, for low-income groups. The applied average
tariff on agricultural goods was 5.8 percent in January 2005, and applied tariffs were relatively
high on meat and edible meat offal (21.2%), edible fruits and nuts (14.4%), and on various
fruits (apples, apricots, bananas, and pears) (40%). On a per capita basis, Egypt’s area of
cultivated land at 0.05 ha per head is among the smallest in the world: an estimated
70 percent of holdings are less than 0.42 hectares. The sector is strongly dependent on
irrigation from the Nile River, with most of this land used for wheat, maize and rice.
Cassing et al. (2009) emphasise that for over 50 centuries Egyptian agriculture has faced
inexorable pressure from fixed land and water constraints. In the middle of the 20th century
the state took control of ownership of the means of production and regulated prices. By the
1970s this started to change, and the change accelerated in the 1980s so much so that by
2006 Egypt had engineered a remarkable reversal of sector back to private forces.
Nonetheless, some distortions remain as shown earlier, and in particular the production of
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rice, cotton and milk is effectively taxed while maize and to a lesser extent wheat and sugar
production is subsidised. At the consumer level the important staple bread is heavily
subsidised at a considerable cost to the government coffers and lesser subsidies are provided
through ration cards for sugar and cooking oil.
4.2
Sudan
FAO data shows that Sudan is the third largest agricultural producer in the region, and Faki
and Taha (2009) report that agriculture is the most important sector in the country for
employing about 70 percent of the active labour force and contributing close to 40 percent
of GDP. The sector is based upon three systems: irrigated agriculture, the rainfed semimechanised sector, and the rainfed traditional sector, with these three accounting for
around 30 percent, 10 percent and 60 percent respectively of agricultural production. Thus,
the traditional sector dominates overall production, but for crops, which produce just over
half of the agricultural output, the irrigated sector is the main contributor with around
60 percent. Livestock contributes some 38 percent of production, with the forestry and
fisheries sector contributing the remaining nine percent. Livestock is therefore focused more
in the traditional sector with FAO reporting that Sudan had over 40 million each of cattle,
sheep and goats. The sector has been and continues to be very dualistic, with a vast
traditional sector and a small modern sector.
Faki and Taha (Ibid.) also emphasise how Sudan, despite being endowed with natural
resources, remains underdeveloped as a result of civil strife and poor economic management
(including an overvalued exchange rate). Government has traditionally been very
interventionist since Independence in 1956, with strong market interventions accompanied
by significant public investment in irrigation. With few other options, agriculture has become
the main source of government revenue. From the late 1980s civil unrest and food shortages
became the norm, and government tried to open the sector to market forces. These
reforms continued through the 1990s with generally positive results, but much remains to be
done and the future remains uncertain. Civil unrest remains acute, but on the positive side
the discovery of oil in the mid-1990s led to a change from almost total reliance on modest
agricultural exports to an oil boom.
Ranked by production Sudan’s main agricultural outputs are cow’s milk, sorghum, goat’s
milk, ground nuts, sesame and sheep’s milk. Agricultural imports of the top 20 products
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were $1,007 million during that same period, with wheat ($287m), sugars ($154m), milk
powders ($82m), palm oils and coffee being the main imports. Exports (top 20) were a
lesser $213 million, with the FAO listing sesame ($87m), cotton ($34m), sorghum, molasses
and sugar as the main exports. Live animals, and especially sheep to the Gulf area, have also
been historically important. From Table 1 the NRA is around 12 percent, and a detailed
breakdown of this shows that Sudan penalises its livestock with an NRA of -30, penalises
most exportables but heavily supports its import-competing products with NRAs such as
120 for sugar, 29 for milk and 22 for wheat.
4.3
Ethiopia
Despite considerable agricultural potential, Ethiopia’s agriculture continues to underperform
for a number of reasons that have included political instability and severe civil unrest, weak
infrastructure, weather constraints, low technology levels and generally poor productivity
and conflicting and dramatically changing policy regimes. This background is especially
important as the sector accounts for 46 percent of GDP (Table 1) as well as employing the
majority of the active workforce (80.2%) and providing the main contribution to exports.
During its rather chequered history from the imperial period through to 1974 and the
centrally planned Dergue regime to 1991, the sector was heavily controlled and regulated by
a variety of means that inhibited production. Since 1991 the government has adopted
market-oriented economic policies for the sector.
While many studies suggest the reforms have paid off, there still seems to be an open
question as to how much the sector has really transformed. Rashid et al. (2009) state that
the 1990s move to market forces initially boosted the sector and, given agriculture’s large
contribution to overall GDP, the entire economy, but that these gains petered out in the
late 1990s. FAO data on food production per capita suggests that Ethiopia, Angola and
Malawi did better than the other tripartite countries over the period 1993-95 to 2004-06,
the only time periods for which Ethiopian production data is available from FAO. Figure 1
attempts to shed some light on Ethiopian production, as it shows the annual percentage
changes since 1971. Two features stand out. The first is the considerable variability, while
the second is that from 1999 there has certainly been a period of sustained growth relative
to the earlier years.
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Figure 1: Annual change in Ethiopian agricultural production (%)
Source: FAOSTAT, 2010
The FAO reports that the main food products are roots and tubers, cow’s milk, maize,
chillies, other cereals and wheat, while the data highlights both (a) the large animal herds of
Ethiopia and (b) the muted milk and meat production from these herds. Imports (top 20)
during 2007 were $457 million, of which wheat ($210m), palm oil ($70m), sugar ($63m),
malt, vegetable oils and maize were the main components. Unusually for the region,
agricultural exports of the top 20 were a greater $751 million, with these exports
dominated by coffee ($417m) and sesame ($133m). Rashid et al. supplement Table 2 data
that shows the overall support levels to agriculture to be relatively high at 11.2 percent by
detailing the composition of this NRA. In general, the non-tradable wheat and maize are
taxed at around 5 percent, while the exportables are again more heavily taxed – except
coffee which has an NRA of a lower -6.2 percent.
4.4
Libya
In 1958, before the oil wealth, agriculture supplied over 26 percent of Libya’s GDP, and
Libya actually exported food. Although agricultural production has remained relatively
constant since that time the increasing oil revenues have meant that agriculture's overall
share of national income is now very low (Table 1). Conversely, food imports have risen
dramatically as the wealth has increased. During the 1950s when agriculture was important
the sector was characterised by low levels of productivity and income, and peasants flocked
to the cities as a result of the oil boom. Indeed, the importance of agriculture in the early
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days reflected an absence of alternatives as the country has limited agricultural resources.
This is in spite of the Great Manmade River Project, the largest water development scheme
in the world that is designed to transfer water from large aquifers under the Sahara to
coastal cities and irrigation projects.
Per capita production has been stable and increasing marginally since around 1980. FAO
data shows that production of $500 million was only one-third of the imports that were
valued at $1,443 million during 2007 and completely dwarfed exports of a mere $9.6 million.
The production is headed by olives ($83m), hen’s eggs and tomatoes of $45 million each, and
potatoes and dates, while imports are one-third each of wheat and wheat flour, followed by
maize oil, maize and rice. Sheep skins and wool with some non-alcoholic beverages make up
almost all the limited exports.
4.5
Eritrea
Eritrea is another very poor country that has had a violent recent past and a current reliance
on aid and remittance monies. The UN awarded the country to Ethiopia in 1952 as part of a
federation, and Ethiopia's annexation of Eritrea in 1962 sparked a 30-year civil war that
ended in 1991. Independence was approved in a 1993 referendum but fighting has flared up
since then and the situation remains tense. The CIA (2010) reports that the terrain is
dominated by extension of the north-south trending Ethiopian highlands, descending on the
east to a coastal desert plain, on the northwest to hilly terrain and on the southwest to flat
to rolling plains, with limited arable land in total. Like many of the tripartite economies
nearly 80 percent of the population is engaged in subsistence agriculture that produces very
little surplus. FAO data shows production of $87 million only marginally above imports of
$78 million. Production is dominated by sesame and sorghum with both around $16 million,
and followed by cow’s milk, roots and tubers and pulses, while imports are led by wheat,
sugar, wheat flour, sorghum and other cereals. Exports of $3 million are mostly sesame at
$2.2 million, with minor contributions from maize and hides and skins.
4.6
Djibouti, Seychelles and Comoros
FAO data shows that these three countries contributed only $102 million or 0.16 percent to
the agricultural production of the region in 2007 – an even lower percentage than their
contribution of 0.28 percent of the population.
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The WTO (2006) reports that Djibouti has an almost exclusively services-oriented
economy that is dominated by port activities, and the agriculture sector, which is based on
limited arable land and oasis type pastoral farming. This primarily family-run subsistence
farming system accounts for only four percent of GDP. Livestock breeding is chiefly from the
nomadic sector that is the major activity of the rural population in terms of employment and
provides livelihoods for almost one-third of the population. There are around 1,600 farms in
the country, employing an approximate total of 3,600 people with an average area of only
half a hectare. However, according to the FAO farmers produce poor crop yields as a result
of a lack of experience or an agricultural tradition among the rural population, poor soils,
the salinity of the water, shortcomings in the design of the gardens, the absence of
windbreaks and shade and the inappropriate cultural techniques used. Consequently,
Djibouti is a significant net importer of food products relative to its production. FAO data
shows that for 2007, total production of just $11 million ($4.5m vegetables and another $4m
in cow’s and camel’s milk) was completely dominated by imports of $259 million, with palm
oil ($69m), sugar ($42m), rice ($26m), vegetable oils and beef the main items. Conversely,
exports (almost certainly re-exports) were $24 million, with sugar and rice heading the list.
Seychelles is a middle-income country on a small island with limited arable land and a
population that numbers around 90,000. Since independence in 1976, per capita output in
this Indian Ocean archipelago has grown by a factor of roughly seven times the preindependence near-subsistence level as growth in the tourism sector, which employs some
30 percent of the labour force, has fuelled development. There has also been a contribution
from tuna fishing, while agriculture’s contribution is extremely limited. The FAO reports that
agricultural-related production was only $3.8 million during 2007, with hen’s eggs
contributing half, followed by cinnamon, vegetables, coconuts and bananas. Imports were a
much greater $63 million while exports were a minor $3.7 million. Imports were headed by
wine, ($9m), followed by rice, olive and sunflower oils and milk powders, while exports
were led by unspecified alcoholic beverages, nuts, cigarettes, wine and chocolate products.
Comoros is a small and relatively poor country where agriculture contributes almost half of
the GDP (Table 1). Remittances from 150,000 Comorians abroad, a figure of perhaps onequarter of the reported population, help supplement GDP. Despite its somewhat
mountainous interior about one-third of the land area is designated as being arable, although
erosion and soil degradation are a problem. The country is not self-sufficient in food
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production, and the FAO reports that food imports of $40 million were almost exactly
matched by production, with staple rice being the main import followed by chicken meat,
sugar, wheat flour and palm oil. Production is focused on bananas, coconuts, cloves, cassava
and pulses, while exports of $15 million are headed by essential oils, cloves, vanilla, coffee
and tea.
5.
Agricultural and trade policies in the EAC
The WTO (2006) reports that agriculture provides livelihoods and employment to the
majority of the EAC population; nonetheless, its contribution to GDP has decreased over
the last few years. Food insecurity in the EAC has increased recently due to several factors,
including unfavourable weather conditions (droughts or floods), making emergency food aid
necessary. Tariffs are the main trade policy instrument in the sector, although some nontariff measures are still in force. Earlier, Table 1 in this report highlighted that these
countries are relatively poor to very poor and that agriculture makes a consistently high
contribution to GDP, while FAO data shows that except for Tanzania which has maintained
ground all have fallen behind in their food production per capita indexes from earlier
periods.
5.1
Kenya
The WTO (2006) reports that agriculture is the backbone of Kenya's economy, as around
80 percent of the population lives in rural areas. The sector has been constrained by several
problems, including an inappropriate legal and regulatory framework, lack of capital and
access to affordable credit, frequent floods and droughts, and poor infrastructure.
Consequently, Kenya is a net food-importing developing country. Agriculture is
predominantly small-scale farming (75% of production), with farms averaging less than three
hectares. Large-scale farming, averaging about 50 hectares, accounts for 40 percent of
irrigated land and involves mainly crops and livestock for commercial purposes. Maize and
wheat are the main food crops, with maize the main staple food and wheat and wheat
products consumed mostly in urban areas. The main cash crops are tea, horticulture,
pyrethrum, coffee, and sugar cane, with Kenya among the world's largest suppliers of cut
flowers to Europe. Livestock contributes about 10 percent to Kenya's GDP, but production
is seriously undermined by drought and diseases. Overall, the FAO data shows that
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production per capita has shown some lower periods but overall the 2007 levels were close
to the highest levels of the last forty years.
Over-taxation is one of the main constraints for Kenya's agriculture, as a wide range of
taxes, levies, cesses, and fees charged on production, inputs, and services used by the sector,
distort prices and make farmers uncompetitive in domestic and world markets. Access to
bank credit is still a major problem for farmers, and the high cost of agricultural inputs has
resulted in reduced use of quality inputs. To assist the sector Kenya in 2004 unveiled its
Strategy for Revitalising Agriculture (SRA) 2004-14, with the objective of transforming
agriculture into a profitable, commercially oriented, and internationally competitive activity,
capable of attracting private investment, so as to reduce poverty, create high quality
employment, increase value-added, and provide food security. Indeed, the success of the cut
flower and vegetable export trade showed that targeted support to infrastructure and
extension services can be beneficial to developing an export industry.
Winter-Nelson and Argwings-Kodhek (2009) report that at independence in 1963 Kenya
inherited a relatively open and export-oriented economy with a policy set that favoured
agriculture. Over the next twenty or so years the economy and the agricultural sector did
well, but then both stagnated and faced struggles with corruption and poor governance. In
assessing the NRA for African countries Anderson and Masters (2009) show that Kenya is
one of the few countries supporting the sector (Table 1), while Winter-Nelson and
Argwings-Kodhek (2009) in detailing how these numbers were calculated show that Kenya
lightly taxes its exportables, modestly supports its import-competing products (NRA of 9.3)
while heavily supporting the mixed trade status products of wheat (46.2%) and sugar
(36.5%).
The FAO reports that the main products by value during 2007 were cow’s milk, tea (number
three producer in the world), maize, beans and bananas. The top 20 imports were valued at
$858 million, and headed by palm oil ($314m), wheat ($144m), sugar ($95m), rice and
tobacco, while the comparable exports were almost twice the imports at $1,524 million.
These were dominated by tea ($699m or 46% of top-20), flowers ($168m), coffee ($155m),
cigarettes and pineapple candy.
5.2
Tanzania
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The WTO (2006) reports that Tanzania has 44 million hectares of land suitable for
agriculture (46% of its total land area) of which only about 10 million hectares is under
cultivation, mostly by small-holder subsistence farmers. The main traditional cash crops are
coffee, cashew nuts, cotton, pyrethrum, sugar, tobacco, tea, and sisal, and their production
has increased in recent years. Livestock contributes 13 percent to agriculture and about
6 percent to Tanzania's GDP. About 40 percent of livestock production is from beef,
30 percent from dairy products, and around 30 percent is poultry and small stock
production. Tanzania is self-sufficient in food, and is a net exporter of grains and cereals
provided there is enough rain; this is reversed during droughts and floods. Demonstrating
the subsistence nature of agriculture, the WTO wrote that ‘about 70 percent of Tanzania's
crop area is cultivated by hand hoe, approximately 20 percent by ox-plough, and 10 percent
by tractor;. Relating to policy constraints, excessive taxation and the negative role of
commodity boards were identified as the main supply-side constraints facing export crops.
The FAO reports that the top 20 imports and exports of $653 million and $553 million
respectively were almost in balance. The top imports were wheat and palm oil, with values
of just over one-third of this total each, followed by sugar, fatty acids and food preparations.
Exports were more diversified than the imports, with the product listed at number twenty
(wheat bran) having an export value of $5.5 million. The main exports were coffee, tobacco,
wheat flour, cotton and tea, while for agricultural production the main products were
bananas, cassava, maize, rice and cow’s milk.
Morrisey and Leyaro (2009) report on the recent history of the sector, stating that after
Independence in 1961 agriculture’s share of value-added declined as the new government
tried to shift resources to other, potentially higher, value-added sectors. This resulted in an
initial decline in the importance of agriculture, but as the other opportunities failed to
materialise, agriculture’s relative importance grew to around 60 percent of GDP in the late
1980s and early 1990s before slowly reducing back to the most recent level of 45 as shown
in Table 1. Diversification did not happen in Tanzania, and the economy remains based on
agriculture.
In assessing the levels of support or taxation associated with agriculture in Tanzania,
Morrisey and Leyaro (Ibid.) also report on the background for the NRA level of -12.4 as
shown in Table 1. There is a very clear pattern whereby the import-competing crops of
especially sugar (103) and wheat (95) but also maize are heavily supported, while the
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exportables are mostly taxed at an average of -48.7 (tea, -95; tobacco, -55; and cotton -70).
Their analysis reveals that although exchange rate liberalisation and privatisation of
marketing has removed many distortions for tradables, marketing inefficiencies and limited
competition persist for many products, and part of the remaining high distortion is
attributable to high distribution and marketing costs from inefficient marketing structures
and high transport costs faced by exporters. For food crops (import-competing products),
persistent distortions are attributable to inefficiencies in the domestic marketing chain or
monopoly power in processing and purchasing, or both. The main problem is that real
producer prices remain low, and especially so given high costs of inputs and inefficiencies in
marketing.
5.3
Uganda
In assessing the NRA of practically zero shown in Table 2, Matthews et al. (2009) found a
clear relationship between agricultural incentives and the different periods of economic
policy. Agriculture was lightly taxed in the 1960s, but the burden of taxation increased
significantly during the chaotic years of the 1970s and 1980s. Since the onset of agricultural
liberalisation at the beginning of the 1990s, the discrimination against agricultural production
has been greatly reduced to the current insignificant levels; the detailed support levels of
near zero for almost everything except the positive supports of 17 percent to both rice and
sugar indicate this. According to them the main challenge now facing the Ugandan
government is to improve the competitiveness of agriculture through a supply-side
investment strategy as the key element in its poverty reduction strategy. Uganda’s problem
has been that its low annual increase in food production as detailed in FAO statistics has
been outstripped by population increase. Indeed, FAO data shows that the per capita
production has been one of the worst performers in the region, and Matthews et al. (Ibid.)
discuss how, despite impressive GDP growth rates from around 1980, the population
increase from 7.1 million in 1960-64 to the 31 million shown in Table 1 has meant that
Uganda remains one of the poorest countries in Africa.
The WTO (2006) confirms that agriculture is the dominant sector of the economy in terms
of contribution to GDP, exports, and employment, providing the livelihood for the majority
of Ugandans. Around 77 percent of the rural labour force is involved in agricultural activities,
and subsistence farming makes up the bulk of the rural economy: an estimated 42 percent of
agricultural GDP is non-monetised. In 2004/05, food crops constituted the leading subsector,
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
accounting for 64.5 percent of total agricultural output, followed by livestock (13.4%), cash
crops (11.1%), fishing (6.0%), and forestry (4.8%). The FAO reports that the top 20 imports
during 2007 of $967 million were dominated by wheat ($337m), palm oil ($144m), sugar
($140m), sorghum and dried peas. Exports of $582 million over the same period were little
over half of the agricultural imports, and were headed by coffee at $227 million and tobacco
at $66 million. These were followed by tea, oils and beer.
5.4
Rwanda and Burundi
The WTO (2003) in a dated review of Burundi reports that agriculture plays a key role in
Burundi's economy, employing 90 percent of the labour force and accounting for 36 percent
of the GDP. Agricultural production was still affected by the war and the country is subject
to adverse climatic conditions. Coffee was the major export and provided over 50 percent
of Burundi's total exports, followed by tea. The third most important export was cotton, but
its production has been particularly affected by Burundi's socio-political instability. These
products are usually grown by small farmers, but the state had a very strong hold over
processing and marketing through state enterprises or semi-public entities and this had a
negative impact. The state enterprises often have a buyer's monopoly, with planters obliged
to sell their production to them at prices fixed in advance, which usually means a relatively
substantial loss of income in comparison with sales under competitive conditions. The real
prices paid to planters had been gradually decreasing, and enterprises in these subsectors
find it difficult to achieve commercial objectives while supporting planters. Their lack of
efficiency leads to large losses, particularly when international markets sluggish, and these
have to be financed from the state budget. The FAO updates the data to 2007, and reports
that the main outputs were bananas, followed equally by sweet potatoes and beans and then
other vegetables and cassava. Modest exports of $55 million were dominated by exports of
coffee at $47 million or 85 percent of the total, followed a long way down by tea and cotton.
Imports were higher at $80 million, led by maize ($22m), flours of wheat and maize, malt
and peas.
The WTO (2003) highlights that agriculture plays a key role in Rwanda's economy in terms
of employment and exports, with production based mainly on family and subsistence farming,
that it was affected by the 1994 genocide, and remains subject to climatic uncertainties. The
main cash crops are tea and coffee, which provide more than half of the country's export
revenue. The tea sector performs well, and is one of the country's leading employers,
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
providing some 60,000 jobs, not counting indirect employment. Climatic conditions and soil
quality are Rwanda's main assets in that area. Tea accounts for approximately 36 percent of
state revenue. Coffee is produced by some 400,000 smallholders as there are no large coffee
plantations. Generally, Rwanda's agricultural policy targets food security. The FAO reports
that during 2007 Rwanda exported (top 20) $72 million in agricultural products, with coffee
($32m) and tea ($30m) still dominating these exports. These were followed by sheep skins,
pyrethrum extract and maize, while imports were $90 million. The main imports were palm
oil, sugar, wheat flour, malt and rice. Production was headed by plantains, as Rwanda was the
4th largest producer of plantains in the world, followed by potatoes, beans, sweet potatoes
and cassava.
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Chapter 1 – The economic and agricultural policy setting for the tripartite Free Trade Agreement
References
Alfieri, A., Arndt, C. and Cirera, X. 2009. Mozambique. In Anderson, K. and Masters, W.A.
(Eds.), Distortions to agricultural incentives in Africa. Washington, DC: World Bank. pp. 127-146.
Anderson, K. and Masters, W.A. 2009. (Eds.), Distortions to agricultural incentives in Africa.
Washington, DC: World Bank.
Anderson, K. 2010. The Political Economy of Agricultural Price Distortions, Cambridge and New
York, Cambridge University Press.
Cassing, J. et al. 2009. Arab Republic of Egypt. In Anderson, K. and Masters, W.A. (Eds.),
Distortions to agricultural incentives in Africa. Washington, DC: World Bank. pp. 71-98.
CIA.
2010.
World Fact Book.
Central Intelligence
Agency
[Online].
Available:
https://www.cia.gov/library/publications/the-world-factbook/geos/cg.html.
Faki, H. and Taha, A. 2009. Sudan. In Anderson, K. and Masters, W.A. (Eds.), Distortions to
agricultural incentives in Africa. Washington, DC: World Bank. pp. 283-306.
FAOSTAT. 2010. FAOSTAT database. [Online]. Available: www.faostat.fao.org.
Kirsten, J., Edwards L. and Vink, N. 2009. South Africa. In Anderson, K. and Masters, W.A.
(Eds.), Distortions to agricultural incentives in Africa. Washington, DC: World Bank. pp. 147-174.
Maret, F. 2009. Madagascar. In Anderson, K. and Masters, W.A. (Eds.), Distortions to
agricultural incentives in Africa. Washington, DC: World Bank. pp. 101-126.
Matthews, A., Claquin, P. and Opolot, J. 2009: In Anderson, K. and Masters, W.A. (Eds.),
Distortions to agricultural incentives in Africa. Washington, DC: World Bank. pp. 329-357.
Morrissey, O. and Leyaro, V. 2009. In Anderson, K. and Masters, W.A. (Eds.), Distortions to
agricultural incentives in Africa. Washington, DC: World Bank. pp. 307-328.
Ndlela, D. and Robinson, P. 2009. In Anderson, K. and Masters, W.A. (Eds.), Distortions to
agricultural incentives in Africa. Washington, DC: World Bank. pp. 205-227.
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Rashid, S., Assefa, M. and Ayele, G. 2009. Ethiopia. In Anderson, K. and Masters, W.A. (Eds.),
Distortions to agricultural incentives in Africa. Washington, DC: World Bank. pp. 231-252.
Robinson, P., Govereh, J. and Ndlela, D. 2009. Zambia. In Anderson, K. and Masters, W.A.
(Eds.), Distortions to agricultural incentives in Africa. Washington, DC: World Bank. pp. 175-204.
Winter-Nelson, A. and Argwings-Kodhek, G. 2009. Kenya. In Anderson, K. and Masters,
W.A. (Eds.), Distortions to agricultural incentives in Africa. Washington, DC: World Bank. pp.
253-282
WTO. 2003. Trade Policy Review: Burundi. Geneva: World Trade Organisation.
WTO. 2004. Trade Policy Review: Rwanda. Geneva: World Trade Organisation.
WTO. 2005. Trade Policy Review: Egypt. Geneva: World Trade Organisation.
WTO. 2006. Trade Policy Review: Angola, Djibouti, the East African Community (Kenya, Tanzania
and Uganda). Geneva: World Trade Organisation.
WTO. 2008. Trade Policy Review: Madagascar, Mauritius. Geneva: World Trade Organisation.
WTO. 2009. Trade Policy Review: Mozambique, Zambia, SACU. Geneva: World Trade
Organisation.
WTO. 2010. Trade Policy Review: Malawi. Geneva: World Trade Organisation.
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Chapter 2 – Agricultural production in the tripartite region
Chapter 2
Agricultural production in the tripartite region
Ron Sandrey and Nick Vink
A special emphasis in this project is given to agriculture, and the objective for this chapter is
to examine agricultural production trends in the region as a basis for the subsequent
analysis. This analysis makes extensive use of the United Nations Food and Agriculture
Organisation (FAO) data. The section starts by providing an analysis of agricultural
production in Africa as a whole as well as in the tripartite region countries in order to place
the tripartite countries in perspective before moving on to examine the individual countries
and their associated commodities in more detail. The chapter ends with an analysis of
undernourishment and food aid in the region.
1.
Total agricultural production
Many South Africans are convinced that their country is Africa’s largest producer of
agricultural products. Table 1 shows the real situation. Measured by physical production,
Nigeria produces three times as much as South Africa, and Egypt about one and a half times
as much. Nigeria and Egypt together produce more than a third of the total output of the
African continent, while the ten largest producers are responsible for two-thirds of the total.
Table 1: Africa’s largest agricultural producers, 2007
Nigeria
Egypt
South Africa
Ethiopia
Sudan
Tanzania
Uganda
Ghana
DRC
Kenya
Africa total
Source: FAOSTAT, 2010
Total output
(‘000 tonnes)
156,093
85,100
50,230
28,094
27,307
27,119
26,147
25,326
24,162
22,288
700,811
Proportion
of total
22.27
12.14
7.17
4.01
3.90
3.87
3.73
3.61
3.45
3.18
100
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Cumulative
34.42
41.58
45.59
49.49
53.36
57.09
60.70
64.15
67.33
40
Chapter 2 – Agricultural production in the tripartite region
Table 2 shows the five largest producers in Africa of a range of agricultural commodities.
South Africa appears among the top 5 in 12 of the 20 commodity groupings shown here, and
is the top producer for beer, wine (only three countries in Africa recorded production of
wine in 2009) and meat (including poultry meat). As the largest overall producer, Nigeria
obviously dominates this list: it is the largest producer in ten of the 20 commodity groupings,
and appears among the top 5 producers in 15 of the 20 commodity groupings (it is missing
from the lists for dairy products and wine). Egypt is a large producer of cereals, fibre crops,
fruit, and meat and dairy products.
Table 2: Africa’s largest agricultural producers by commodity, 2009 (tonnes)
2009
2009
Citrus
Fruit
Cereals
Nigeria
30209000
Nigeria
3400000
Egypt
23897264
South Africa
14586200
Egypt
South
Africa
Ethiopia
14449447
Morocco
Mali
2009
2009
Coarse
grain
3295000
Nigeria
South
Africa
2172127
Ethiopia
1271000
Fibre
crops
25977000
2009
Fruit
231200
Uganda
12624900
Egypt
Burkina
Faso
10179600
195000
Egypt
9803855
11887373
Nigeria
167900
Nigeria
9502000
Egypt
7874269
Tanzania
137300
South Africa
5966794
Sudan
4888000
Benin
Roots,
tubers
125300
Tanzania
4708450
6334550
Algeria
Oil
crops
844895
3115630
Nigeria
3615280
Nigeria
2969000
Nigeria
89409000
Nigeria
665500
South Africa
860818
565886
Ethiopia
1919484
Ghana
19607625
Côte d'Ivoire
291300
Sudan
746853
474429
Niger
1592680
DRC
15571730
Tanzania
105100
Uganda
450460
Sudan
South
Africa
Côte
d'Ivoire
414740
Tanzania
1108500
14633434
Morocco
100246
Senegal
Vegetables,
melons
407693
351160
647300
Angola
Côte
d'Ivoire
Groundnut oil
10051765
Mozambique
Oilcakes
Nigeria
Pulses
Egypt
20274836
Ghana
Beer of
barley
South
Africa
Nigeria
10839000
Nigeria
1600000
Morocco
5270505
Ethiopia
Algeria
4542729
Tunisia
2731660
Angola
Côte
d'Ivoire
Butter
1818923
Uganda
Cottonseed oil
Burkina
Faso
Wine
Nigeria
785000
South Africa
Egypt
22500
Sudan
152862
Algeria
52000
707676
Mali
18300
Senegal
125400
Ethiopia
1754
686900
Tanzania
17720
Guinea
72862
400000
Benin
16500
Ghana
65414
2222010
Sudan
7421902
Eggs
1050000
Nigeria
South
Africa
552800
Meat
South
Africa
450000
Egypt
1524540
Egypt
5948738
43000
Egypt
290000
Nigeria
1296265
Kenya
4238000
Morocco
37342
Morocco
244000
Morocco
853640
South Africa
3091000
Niger
29335
Algeria
185000
Sudan
813182
Somalia
2181000
116200
Egypt
610750
Morocco
25827
154055
Sudan
17820
Sudan
South
Africa
Ethiopia
17550
17498
Kenya
85000
47520
Cheese
Egypt
Tree nuts
Milk
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
Table 3 shows the trends in total agricultural output in Africa over the past five decades.
Contrary to the ‘doom and gloom’ scenarios so often found, agricultural production has
increased quite substantially for the continent as a whole and in all the different regions, and
has accelerated since 1990. The latter performance, however, is regionally concentrated in
the northern and western parts of the continent. But here, the allocation of countries to the
different regions matters1, as the meltdown in production in Zimbabwe depresses the results
in eastern Africa. When compared to the global performance, the overall picture of the
continent looks good – annual growth has been higher than for the world as a whole, and
the latter has slowed since 1990.
Table 3: Gross agricultural production in Africa, 1961-2007
Index 1961=100
Growth rates at 2007
1970
1980
1990
2000
2007
1961
1990
Africa
153
153
203
275
325
2.56%
2.77%
Eastern Africa
157
157
195
233
286
2.28%
2.24%
Middle Africa
143
143
178
202
222
1.74%
1.33%
Northern Africa
170
170
233
327
397
3.00%
3.12%
Southern Africa
167
167
184
212
218
1.70%
1.02%
Western Africa
137
137
210
330
393
2.98%
3.69%
World
159
159
200
244
280
2.24%
1.99%
Source: FAOSTAT, 2010
Table 4 shows that farmers in Africa outperformed their counterparts in the rest of the
world over the periods 1961-2007 and 1990-2007 in terms of all the components of
production (crops and the sub-set of cereals, livestock, and the ‘food’ subset of both). Again,
the regional pattern is familiar: most of the growth took place in northern and western
Africa, with crop and food production in Western Africa growing by more than 3.5% per
year since 1990. Growth rates for southern Africa (basically the Southern African Customs
Union – SACU area) are the lowest, and lower than the global growth rates, reflecting the
dominance of South Africa, whose agricultural growth performance has been relatively poor
for more than two decades2. Eastern Africa has generally performed better than the global
1
The FAOSTAT database has an idiosyncratic allocation of countries to the regions: countries that have traditionally been
regarded as part of Southern Africa (Zimbabwe, Zambia, Malawi, and Angola) are now redirected, mostly to eastern Africa,
but in the case of Angola to ‘middle Africa’, while southern Africa consists of the SACU member countries only.
2
A sign of the relative maturity of the industry in South Africa as well as the increase in Total Factor Productivity brought
about by changed farming methods, especially in the field crop sector.
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Chapter 2 – Agricultural production in the tripartite region
average despite having Zimbabwe included in its ranks, while the performance of middle
Africa has been poor, but better than southern Africa.
Table 4: Growth in crop, livestock, cereal, and food production, 1961-2007
Crops
Livestock
Cereals
Food
1961-
1990-
1961-
1990-
1961-
1990-
1961-
1990-
2007
2007
2007
2007
2007
2007
2007
2007
Africa
2.54
2.81
2.56
2.53
2.50
2.53
2.64
2.82
Eastern Africa
2.37
2.34
2.07
1.92
2.28
2.61
2.28
2.17
Middle Africa
1.67
1.25
1.92
1.47
1.88
3.46
1.87
1.43
Northern Africa
2.66
2.96
3.77
3.47
3.08
2.80
3.16
3.34
Southern Africa
1.75
0.46
1.60
1.64
0.78
-1.13
1.81
1.20
Western Africa
2.96
3.83
2.94
2.96
2.88
2.91
2.94
3.79
World
2.26
2.11
2.18
1.76
2.22
1.10
2.28
2.01
Note: Annual rates of growth in the output index, 1961=100
Source: FAOSTAT, 2010
These rates of growth in total output must, however, be seen in the context of population
growth rates that are higher than the global average. The data is shown in Table 5.
The left-hand part of Table 5 shows population growth rates from 1961 to 2010, 1980 to
2010 and 2000 to 2010 for Africa, for its component regions and for the world as a whole.
As expected, Africa’s population growth rate will exceed the global average over this entire
50-year period, with only southern Africa (South Africa and the BLNS3 countries)
experiencing a lower rate of growth than the world average, and then only over the past
decade. Southern Africa’s low rate of population growth is forecast to continue through
2050 (see the right-hand side of the table), while the rate for Africa as a whole will decline,
but will remain above the global average.
3
Botswana, Lesotho, Namibia and Swaziland.
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Chapter 2 – Agricultural production in the tripartite region
Table 5: Population growth rates, 1961-2050
Annual change (%) at 2010
Annual change (%) at 2050 from:
from:
1961
1980
2000
1961
1980
2000
2020
Africa
3.24
4.01
2.57
2.16
2.06
1.82
0.50
Eastern Africa
3.48
4.34
2.87
2.40
2.32
2.11
0.59
Middle Africa
3.51
4.60
3.04
2.38
2.35
2.09
0.57
Northern Africa
2.88
3.33
1.90
1.72
1.51
1.19
0.29
Southern Africa
2.70
2.97
1.34
1.35
1.04
0.55
0.11
Western Africa
3.27
4.15
2.80
2.23
2.18
1.97
0.55
World
2.08
2.34
1.36
1.23
1.05
0.82
0.20
Source: FAOSTAT, 2010
Hence, the question is: How well has African agriculture fared in feeding Africa’s population?
Table 6 shows the main trends in per capita production. On the face of it, Africa has fared
poorly. Per capita agricultural production has declined since 1961 in the continent as a whole
while global per capita production has been increasing. Per capita production has increased
only in northern and in western Africa.
However, this data also shows that most of the decline came before the mid-1980s: since
then the picture has looked a bit different. The turning point for northern and western
Africa at around the mid-1980s is evident. Since that time, northern Africa has outpaced
global average per capita growth in agricultural production, while western Africa has not
lagged far behind.
Table 6: Gross per capita agricultural production in Africa, 1961-2007
Index 1961=100
1970
1980
1990
2000
2007
Africa
107
92
91
96
97
Eastern Africa
108
91
84
77
79
Middle Africa
101
84
78
67
60
Northern Africa
108
105
109
127
138
Southern Africa
98
101
88
81
78
Western Africa
111
84
94
113
113
World
106
109
116
122
128
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
The countries of middle Africa include large countries that have been unstable for a long
time (e.g. the Democratic Republic of Congo – DRC) and those only recently emerging from
civil war (e.g. Angola). Eastern Africa includes Zimbabwe, as has been noted, so not much
can be said about that performance. In general, the most disappointing performance in per
capita output growth has been from southern Africa, especially because of the low
population growth rates.
However, even this relatively poor performance should be seen in perspective. Figure 1
shows the trend in the per capita output of agricultural products for the ten largest
agricultural producers on the continent, this time measured as the kilocalorie equivalent of
output per person per day. Egypt, South Africa, Ghana and Nigeria lead the group, with the
eastern African countries lagging. DRC seems to have recovered some lost ground over the
past decade, but Uganda has lost ground. The fastest growth has been from Ghana and
Nigeria, confirming the earlier observations about production growth in western Africa.
Figure 1: Trends in per capita agricultural production,
1984-2007 (kcal/person/day)
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
Table 7 extends the analysis of total production across the continent by placing the tripartite
countries under the spotlight. The data shows that:
•
All three categories of world, Africa and tripartite agricultural production have grown
in real dollar terms;
•
Average growth over the period 1990 to 2007 has been higher for both Africa and the
tripartite countries than for the world. The obvious reason is that agriculture in the
West African countries has been leading the growth in agricultural production.
Table 7: The relative performance of African agricultural production,
1970 - 2007
Change (%):
Year
2007 over
1970
1980
1990
2000
2007
1970
1990
World ($bn)
673
837
1,080
1,328
1,541
2.2
2.1
Africa ($bn)
51
58
77
104
123
2.4
2.7
Tripartite countries ($bn)
29
35
43
55
64
2.1
2.3
World Index/capita
84
86
94
100
106
0.6
0.7
Africa Index/capita
110
95
95
99
99
-0.3
0.2
Source: FAOSTAT, 2010
Table 8 puts the agricultural production from the individual tripartite countries in
perspective. It shows the FAO data for agricultural production in recent years for all the
tripartite countries. The data is expressed in real dollar million (1999-2000 base). Columns 2
to 5 inclusive show the value of agricultural production from each of the 26 tripartite
countries ranked by 2007 values. Egypt is the main producer, followed by South Africa,
Sudan and Ethiopia, with these top four contributing some 55.0 percent of the total (Column
7). The next four (Kenya, Tanzania and Uganda from the East African Community – EAC –
and DRC) contribute another 25.3 percent, while the remaining 18 countries produce
19.7 percent, a figure lower than Egypt’s 22.5 percent. This latter grouping contains the
countries of Malawi, Angola, Mozambique, Zimbabwe and Zambia whose agricultural
potential, for a variety of reasons, belies their current production. Note that the four
countries (Lesotho, Djibouti, Comoros and Seychelles) with a total 2007 production of
$193 million or 0.3 percent of the total have been omitted.
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Chapter 2 – Agricultural production in the tripartite region
An indication of the annual percentage change over the values at 2007 from a base of 1990 is
given in Column 6, and this shows in the bottom line that the average percentage change
was 2.3 percent over this period. Only nine of the 26 countries recorded a growth rate
above this figure, however, but as these tended to be in the main producing countries (five
of the top six by value) the average is higher than this figure (nine out of 26) might suggest.
Six of the countries (DRC, Zimbabwe, Burundi, Swaziland, Mauritius and the not-shown
Lesotho) actually had a decline in the real value of their agricultural production over the
period.
Table 9 extends this analysis to show FAO’s index of per capita agricultural production over
the periods 1970, 1980, 1990, 2000 and 2007. There are 13 of the 26 countries that show an
index of below 100 at 2007, and indeed of those over 100 only Egypt, Sudan, Kenya, Malawi
and Djibouti show an index value at 2007 that is above that from 1970. In general, while
total agricultural production in the tripartite region is rising at a similar rate to that of the
world, it is, like the aggregate African data from Table 1, not keeping pace with the
population.
The table also shows why per capita production in the FAO-defined southern Africa (the
SACU region) fares so badly (agricultural production per capita in Botswana and Namibia has
declined by more than 50% since 1969-71), while in Lesotho and Swaziland it has declined by
between 40 and 50 percent. In all four these countries the declining trend has not been
arrested. By contrast, in South Africa per capita production stabilised after 1995-1997.
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Chapter 2 – Agricultural production in the tripartite region
Table 8: Agricultural production in the tripartite countries, average 1999-2001
real values
Total Agricultural Production, 1999-2001
% change
2007 (%)
2007/1990
share
($m)
1980
1990
2000
2007
Egypt
5,667
8,340
12,822
14,413
3.2
22.5
South Africa
6,159
6,785
8,049
8,235
1.1
12.9
Sudan
3,014
2,854
5,073
6,464
4.8
10.1
Ethiopia
3,404
3,637
4,464
6,146
3.1
9.6
Kenya
1,991
3,032
3,422
4,901
2.8
7.7
Tanzania
2,221
2,891
3,158
4,381
2.4
6.8
Uganda
2,086
3,039
3,914
4,207
1.9
6.6
DRC
2,481
3,335
2,758
2,679
-1.3
4.2
Madagascar
1,448
1,697
1,815
2,186
1.5
3.4
Malawi
683
813
1,439
1,980
5.2
3.1
Angola
585
598
961
1,445
5.2
2.3
Rwanda
868
1,047
1,111
1,296
1.3
2.0
Mozambique
861
820
1,058
1,281
2.6
2.0
1,042
1,327
1,745
1,179
-0.7
1.8
Zambia
413
569
683
815
2.1
1.3
Libya
431
518
647
680
1.6
1.1
Burundi
523
694
597
630
-0.6
1.0
Namibia
316
278
269
282
0.1
0.4
Swaziland
155
191
168
186
-0.2
0.3
Botswana
122
165
150
168
0.1
0.3
Mauritius
124
159
157
156
-0.1
0.2
135
149
2.7
0.2
54,783
64,053
2.3
Zimbabwe
Eritrea
Tripartite
34,729
42,962
countries total
Source: FAOSTAT, 2010, tralac calculations
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Chapter 2 – Agricultural production in the tripartite region
Table 9: Food production index, net per person, average 1999-2001 real values
Country
1969-71
1979-81
1990-92
1995-97
2000-02
2004-06
2007
Angola
140
100
83
86
109
126
122
Botswana
225
164
137
125
103
105
102
Burundi
149
137
128
109
100
92
78
Comoros
142
114
106
102
99
89
88
DRC
175
157
157
115
96
86
78
Djibouti
100
110
120
102
105
116
134
62
61
80
93
100
107
101
Eritrea
96
86
80
78
Ethiopia
97
103
115
113
Egypt
Kenya
111
107
112
98
100
106
112
Lesotho
134
119
92
98
101
80
83
68
106
94
105
95
93
88
155
137
121
113
95
99
98
88
88
56
66
97
91
117
Mauritius
125
109
112
110
106
104
93
Mozambique
151
114
87
97
96
98
97
Namibia
258
212
134
112
99
94
92
Rwanda
127
130
125
102
105
104
100
Seychelles
161
116
80
111
99
84
84
South Africa
120
125
102
96
102
105
101
90
97
79
96
102
107
107
Swaziland
138
149
129
107
100
108
101
Tanzania
117
123
111
99
108
113
110
Uganda
161
108
106
96
101
91
84
Zambia
120
110
106
100
98
105
102
Zimbabwe
149
127
91
89
95
86
68
Libya
Madagascar
Malawi
Sudan
Source: FAOSTAT, 2010
By contrast, the development of agriculture in China holds salient lessons for Africa.
Table 10, using the same time-periods as Table 9, shows where dramatic progress has been
made since around 1980, with the food production per person index more than doubling
over this period. In China, agricultural progress and policy changes were part of the key
drivers of economic growth and development during the country’s reform period, and
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Chapter 2 – Agricultural production in the tripartite region
agriculture has been driven by higher agricultural yields, based partially on the wider
application of agricultural research and technologies (Sandrey and Edinger, 2010). The
African Agricultural Technology Foundation (AATF) reports that the average developing
world cereal yields which were estimated at 1 tonne per hectare in 1961 increased to
4.5 tonnes per hectare in the East Asia and Pacific region, 2.3 tonnes per hectare in the
Middle East and North Africa region, yet remained at that same 1 tonne per hectare in SubSaharan Africa, while in China it increased to about 4.5 tonnes per hectare. This same report
examines agricultural constraints in nine African countries, of which six are tripartite
countries. Farming in Sub-Sahara Africa was found to be dominated by subsistence farming
with low input levels and low technology adoption, low mechanisation levels, a reliance on
rainfall, poor infrastructural support in areas such as communications, human resources and
markets, and a strong linkage between crops and livestock which led to an undesirable
outcome when one area was affected. By contrast, China had made dramatic progress in
these and most other areas.
Table 10: China’s food production index
Food production index, Net Per Person, base period 1999-2001
Country
1969-71
1979-81
1990-92
39
45
67
China
1993-95
1995-97
2000-02
87
103
78
2004-06
116
Source: FAOSTAT, 2010
2.
Commodity production
2.1
Crops
The main features of the tripartite region’s (mostly) crop component of agricultural
production are shown in Table 11. Shown is the production of the top 20 lines from firstly
the tripartite region and then the contribution from Egypt, South Africa (RSA), Ethiopia,
Sudan, Tanzania and Uganda as ranked by the value of the lines shown. Note that the order
of the top six as shown here is slightly different from the top six from total agricultural
production in Table 3: Sudan and Ethiopia switch places, and Kenya drops to seventh
position as more emphasis is given to crop production in Tanzania and Uganda rather than
to the animal orientation of agriculture in Kenya. The rows are ranked by the aggregate
value of the production during 2007 as reported by FAO, and blank cells suggest that the
values are more likely to be very low rather than zero.
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Chapter 2 – Agricultural production in the tripartite region
Table 11: Crop contribution to agricultural production, largest six producers,
2007
EthioEgypt
%
RSA
pia
TanzaSudan
Africa
nia
Total
Ugan-da
’000 tons
Sugar cane
89
17,014
20,300
2,200
6,800
2,370
2,350
80,345
Cassava
43
0
0
0
10
6,600
4,456
48,645
Maize
72
6,243
7,125
3,337
70
3,659
1,262
33,785
Cow milk, fresh
75
3,187
3,000
1,250
5,292
850
735
20,316
Plantains
60
600
9,231
14,709
Potatoes
76
2,760
1,972
526
264
650
650
13,343
Wheat
71
7,379
1,905
2,219
803
83
19
13,177
Rice, paddy
58
6,877
3
11
23
1,342
162
12,178
Tomatoes
67
8,639
419
34
642
145
14
10,823
Sorghum
42
844
176
2,174
4,999
900
456
10,491
Bananas
81
945
348
250
74
3,500
615
9,758
Sweet potatoes
72
364
51
389
9
1,322
2,602
9,202
Vegetables n.e.s.
43
580
300
450
643
955
395
5,960
Beer of barley
67
284
2,653
634
311
173
5,712
Roots n.e.s.
97
4
Sugar beet
69
5,458
Oranges
67
2,055
1,412
48
18
1
4,025
Grapes
83
1,485
1,813
7
0
14
3,382
Cattle meat
67
320
805
363
340
247
106
3,247
Onions, dry
48
1,486
369
178
59
52
147
2,745
Total
54
85,100
50,230
28,094
27,307
27,119
26,147
380,391
4,950
5,642
5,458
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
The next set of seven tripartite countries ranked by their (mostly) crop production is shown
in Table 12, along with their combined contribution to the tripartite totals. There have been
some changes to the rankings of the products as these countries have a different product
mix to the overall combined tripartite mix. In order of total production as defined here the
countries are DRC, Kenya, Malawi, Angola, Madagascar, Mozambique and Rwanda, with
cassava, sugar cane and maize now being the lead products.
Table 12: Crop contribution to agricultural production, next seven producers,
2007
Cassava
Sugar cane
Maize
Potatoes
Cow milk, fresh
Plantains
Rice, paddy
Sweet potatoes
Bananas
Vegetables other
Oil palm fruit
Beans, dry
Beer of barley
Mangoes etc
Groundnuts
Cattle meat
Pineapples
Cabbages, etc.
Tomatoes
Sorghum
Grand total
%
Tripartite
total
73
18
28
45
26
33
30
38
21
31
95
43
19
43
35
24
73
43
6
6
27
DRC
Kenya
Malawi
15,004
1,550
1,156
94
5
1,205
316
237
315
350
1,120
112
180
200
369
12
195
23
398
5,204
2,929
850
4,230
593
47
812
593
595
0
430
275
384
21
445
429
609
3,239
2,500
3,226
2,859
30
330
113
0
390
185
0
129
27
67
262
27
0
60
42
6
24,162
560
147
22,288
40
64
14,505
Angola Madagascar
’000 tons
8,840
2,450
360
2,600
570
370
615
225
200
530
0
0
9
3,000
710
890
300
325
248
310
280
21
105
88
396
82
0
220
60
42
85
147
40
54
0
13
14
0
13,305
22
1
12,589
Mozambique
Rwanda
5,039
2,061
1,152
80
66
0
105
67
90
105
0
0
48
24
103
29
0
0
700
120
90
1,300
120
2,750
62
800
0
59
0
280
67
0
10
23
0
0
9
170
10,490
0
190
7,180
Source: FAOSTAT, 2010
The increases in production shown here have been accompanied by increases in the physical
yields of most agricultural commodities on the African continent. Figure 2 shows the yield
increases for the major field crops over the past five decades. Note that these yields lag
global average yields for all the commodities.
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Chapter 2 – Agricultural production in the tripartite region
Figure 2: Yields for the major field crops in Africa, 1961-2008
Note: Roots and tubers measured on the right-hand axis
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Chapter 2 – Agricultural production in the tripartite region
2.2
Livestock
Livestock numbers for the major tripartite countries are shown in Table 13, along with
world and African numbers to place the production from the tripartite countries in
perspective. Africa’s goat, sheep and cattle numbers make up a significant portion of the
world total, while chickens and especially pigs do not. Furthermore, the tripartite countries
make a large contribution to the total African cattle herd – indeed, in 2008 Ethiopia and
Sudan were home to the world’s sixth and seventh largest cattle herds, just marginally
behind Argentina. However, beef production is significantly different: Argentina’s 2,830,000
tonnes versus Ethiopia’s 380,000 tonnes, largely because livestock productivity in most of
Africa is low by best global practices (accentuated by the custom of viewing cattle as a
wealth store)4.
Table 13: Livestock numbers in the tripartite countries, million head 2008
Cattle
Chickens
Goats
Sheep
Pigs
Million head
World
1,347
18,398
862
1,078
941
Africa
270.0
1,430.5
291.1
287.6
26.5
Africa % world
20.0
7.8
33.8
26.7
2.8
Tripartite group total
186
595
149
146
11
Tripartite group as a share of Africa
68.9
41.6
51.3
50.6
41.8
0.0
Main tripartite countries - ranked by cattle numbers
Ethiopia
49.3
35.0
21.9
25.0
Sudan
41.4
35.0
43.1
51.1
Tanzania
18.0
30.0
12.6
3.6
0.5
South Africa
14.4
126.2
6.7
25.2
1.7
Kenya
13.5
29.6
14.5
9.9
0.3
Madagascar
9.7
25.5
1.3
0.7
1.4
Uganda
7.4
27.5
8.5
1.7
2.2
Zimbabwe
5.4
23.0
3.0
0.6
0.6
Egypt
5.0
96.0
4.2
5.0
0.0
Angola
4.2
7.0
2.1
0.3
0.8
Subtotal
168
435
118
123
7
Source: FAOSTAT, 2010
4
The AATF (2009) reports that smallholder beef cattle productivity in most Sub-Sahara African countries
remains low. Heifers calve late, with an average age at first calving of between 42 to 48 months instead of a
possible 24 months, and calving percentage is between 40-50 percent instead of a possible 80 percent.
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Chapter 2 – Agricultural production in the tripartite region
An indication of the milk and meat output from the regions’ animals is given in the next two
tables. The first, Table 14, shows the milk production of the region expressed in thousand
tonnes. Milk from traditional cows dominates supply, but buffalo milk from Egypt is locally
important. Goats and sheep make a significant contribution in Sudan. To put productivity in
perspective, Table 6 shows the region to have 168 million head of cattle (including of course
beef breeds), yet the milk production of around 20,000 tonnes is little more than New
Zealand’s 15,000 tonnes from a total cattle herd of 9.7 million.
Table 14: Milk production, 2007
Cows
Goats
Sheep
Buffalo
’000 tons
Egypt
3,187
15
93
Sudan
5,292
1,456
498
Kenya
4,230
130
31
South Africa
3,000
Ethiopia
1,250
Tanzania
850
Uganda
735
Madagascar
530
Total (including others)
20,316
2,610
44
105
1,835
729
2,610
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
Meat production is shown in Table 15. Here, South Africa, with almost one-third of the
total production, is the main contributor, followed by Egypt, Sudan and Ethiopia. Again,
productivity can be roughly estimated by comparing Tables 13 and 15, where the cattle herd
in both Ethiopia and Sudan is around three times the South African herd, yet beef
production in South Africa is around two and a half times that of both Ethiopia’s and Sudan’s.
Chicken meat is the next important meat (South Africa and Egypt), followed by goat (Sudan),
sheep (Sudan and South Africa) and pig (South Africa). The ‘other’ is quite significant, and
includes game meat (DRC and Ethiopia) and camel meat (Sudan and Egypt).
Table 15: Meat production, 2007
Cattle
Chicken
Goat
Sheep
South Africa
805
974
37
118
Egypt
320
560
18
Sudan
340
Ethiopia
363
Kenya
445
Tanzania
247
Madagascar
147
Zimbabwe
97
Uganda
46
46
150
43
481
1,421
186
148
86
760
65
85
98
657
45
34
27
551
17
340
54
32
233
28
32
197
31
60
18
Libya
100
Angola
85
Zambia
58
195
148
28
113
34
40
Mauritius
40
166
128
28
Mozambique
Total
2,103
29
DRC
Other
20
40
106
Pig
25
92
65
40
Namibia
39
39
Botswana
35
35
Total
3,247
2,073
546
505
457
794
7,507
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
3.
Undernourishment and food aid
There are two aspects of agricultural policy – trade and production – in the region that are
worrying and relevant to the future. One is the level of undernourishment in the region,
while the other is the extent of food aid delivered in the last few years to the region. We
have introduced a note on each of these aspects as they relate to the region to place them
in perspective.
3.1
Undernourishment in the region
An especially damning indictment of African agriculture is the data shown in Table 16 that
details the percentage of the population deemed as ‘undernourished’. For 2004-2006 there
are highs in the 60s from both Burundi and Eritrea, but even more worrying is the
75 percent shown for the DRC. Too many countries are in the 30s and 40s, and too many
countries have not made satisfactory progress in reducing these levels as shown by the
percentage share changes from 1990-92 to the most recent data. Data is not available for
Egypt, Libya and South Africa. Again, China presents a powerful example of how increasing
agricultural production can mitigate these figures, as the increases in Chinese production
shown earlier have resulted in the comparable Chinese data reading of 15 percent for the
first period and then 10 percent for the more recent period. Note that these figures are
indicative only, as over-nourishment (obesity) is an equally serious health problem in many
developing countries such as the Pacific Islands where incomes are not high.
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Chapter 2 – Agricultural production in the tripartite region
Table 16: Percentage of population considered undernourished
Angola
Botswana
Burundi
66
20
44
44
26
63
Change %
points
22
-6
-19
Comoros
Djibouti
DRC
Eritrea
Ethiopia
Kenya
Lesotho
Madagascar
Malawi
Mauritius
Mozambique
Namibia
Rwanda
Seychelles
Sudan
Swaziland
Tanzania
Uganda
Zambia
Zimbabwe
40
60
29
67
71
33
15
32
45
7
59
29
45
11
31
12
28
19
40
40
51
31
75
66
44
30
15
35
29
6
37
19
40
8
20
18
35
15
45
39
-11
29
-46
1
27
3
0
-3
16
1
22
10
5
3
11
-6
-7
4
-5
1
1990-92
2004-06
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
Table 17 shows that food aid distribution in the region over the first few years of the
current millennium has been significant. It shows the total shipments by tonnes for both
cereals and non-cereal food aid to countries in the tripartite region from 2000 to 2006
inclusive. Ethiopia heads the table, followed by Sudan and Eritrea, but note four of the five
SACU countries shown (Lesotho, South Africa, Swaziland and Namibia) at the bottom of the
table. Only five of the 26 tripartite countries (Botswana, Libya, Mauritius, Comoros and
Seychelles) are shown as not having received food aid.
Table 17: Food aid to the tripartite region, 2000 to 2006 combined
Cereals (tonnes)
Non-Cereals (tonnes)
Ethiopia
5,461,780
318,036
Sudan
2,133,004
387,066
Eritrea
1,357,834
172,153
Kenya
1,150,101
234,496
Mozambique
1,073,339
84,907
Zimbabwe
975,250
137,021
Angola
901,150
202,637
Uganda
617,804
138,105
Malawi
511,485
62,264
DRC
418,476
112,370
Tanzania
434,462
89,694
Zambia
338,667
87,116
Burundi
322,906
101,122
Rwanda
205,731
164,109
Madagascar
242,425
84,198
Egypt
123,478
18,387
Lesotho
94,858
26,077
South Africa
89,698
5,114
Djibouti
78,065
13,580
Swaziland
59,957
11,423
Namibia
51,810
3,510
Source: FAOSTAT, 2010
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Chapter 2 – Agricultural production in the tripartite region
4.
Conclusion
Africa’s five largest agricultural producers (Nigeria, Egypt, South Africa, Ethiopia and Sudan in
that order) produce just shy of 50 percent of the continent’s total agricultural output. This
means that some 20 percent of the production comes from countries adjacent to the Nile
River (Egypt, Ethiopia and Sudan). Nevertheless, Nigeria dominates, with 22 percent of the
total physical production of the continent.
Gross agricultural production in Africa has increased faster than the world average growth
since 1961, and has accelerated since 1990. This growth has occurred over all commodity
groupings. However, growth in per capita production has been lower than the world
average. West and North Africa have experienced positive per capita growth in agricultural
output since the mid-1980s, while East and southern Africa’s growth started a decade or
more later. Per capita agricultural production has declined by around half in all four of the
BLNS countries.
Many African countries suffer from high rates of undernourishment and poverty, and food
aid has become endemic in some areas.
References
AATF. 2009. Study on the Relevance of Chinese Agricultural Technologies to African
Smallholder Farmers. Nairobi:
African Agricultural Technology Foundation. [Online].
Available: http://www.aatf-africa.org.
FAOSTAT. 2010. FAOSTAT database. [Online]. Available: www.faostat.fao.org.
Sandrey, R. and Edinger, H. 2010. Sino-African agricultural cooperation: Lessons for Africa’s
agricultural development. tralac Working Paper. Stellenbosch: tralac.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Chapter 3
Intra-African trade in southern and eastern Africa and the role of South Africa
Ron Sandrey and Hans Grinsted Jensen
1.
Introduction
The purpose of this chapter is to analyse the implications for South Africa of the proposed
COMESA-EAC-SADC1 tripartite free trade agreement, with a focus on agricultural trade.
Any analysis of this nature needs to take into account the problem of regional data quality.
African countries are notorious for the lack of openness, timeliness and reliability of trade
data. For this reason, a wide range of data sources has been investigated and, where
possible, the best data for the specific components has been used.
First, the recently-built Global Trade Analysis Project (GTAP) African database is used to
present what is possibly the most accurate report on intra-Africa trade data, including
agricultural trade. As this analysis is built upon 2001 data, however, the COMTRADE data
base is then used to update the analysis to the 2006 to 2008 period. This is followed by an
examination of the ‘mirror’ data for most of the major countries exporting agricultural
products to Africa using World Trade Atlas (WTA) data for the December years through to
and including December 2009, and then by an analysis using the United Nations Food and
Agricultural Organisation (FAO) data to look at a consistent but slightly dated analysis of
African imports by product. Finally, given the focus on the implications for South Africa,
South African agricultural trade with the tripartite region is analysed, again using WTA data.
Compounding the problems of timeliness and completeness of the data are the
inconsistencies in the sector definitions used. In general, this analysis is based on the
definitions used by the World Trade Organisation (WTO), but this has not always been
practical, and the approaches used to facilitate consistency are discussed at the appropriate
points in the analysis. The chapter starts by introducing the trade data and its components,
1
Common Market for Eastern and Southern Africa, East African Community, and Southern African
Development Community.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
namely agriculture (processed and unprocessed), resources, and manufacturing as used in
the GTAP database.2
2.
The GTAP database
In an attempt to ameliorate the African data problems a special data version of the GTAP
model has been constructed,3 augmented by the estimates of Villoria (2008) of the missing
data. In this case it is defined as the exports and imports that may have taken place between
two potential trading partners, but which are unknown to the researcher because neither
partner reported them to the United Nation’s COMTRADE, the official global repository of
trade statistics. Villoria (Ibid.) reports that it is commonly accepted that in some instances
over 40 percent of the potential trade flows fit this definition and systematically sets out to
estimate the likely magnitude of this missing trade by modelling the manufacturing trade data
(including agriculture) in the GTAP Database using a gravity approach. The gravity model
used proposes that bilateral trade is related to country size, distance, and other trade costs.
In particular, high fixed costs can actually prohibit trade within Africa and this may explain at
least some of the common zero-valued flows that characterise intra-African trade. His
analysis suggests that these missing exports were valued during the base year of 2001 at
approximately $300 million, with missing trade highest in the lowest income countries of
Central and West Africa.
2.1
The global picture
The top part of Table 1 shows the data summary of the GTAP African database of global
trade for 2001 by monetary value in US dollars. Total global exports were
$5,664,177 million. Of these, $1,541,317 million derived from Asia, $1,056,500 million from
North America, $2,133,565 million from the EU254 (including intra-EU trade),
2
‘Resources’ are essentially unprocessed or lightly processed natural resources such as minerals, fuels, timber
and live fish in the sea. The processed components of these are generally included in manufacturing. Similarly,
harvested fish products are split between live and lightly processed fish in ‘resources’ and further processed
fish in ‘agriculture’. The WTO definition of agriculture does not include any fisheries products.
3
The widely used GTAP Database currently (Version 7.0) disaggregates just 16 of the 54 countries in Africa,
and recognises the need for more disaggregation. GTAP developed the Africa Database which includes data for
39 regions (30 African regions and 9 other aggregated regions) and the 57 sectors of the GTAP 6 Database.
https://www.gtap.agecon.purdue.edu/databases/Africa/default.asp.
4
EU25 refers to the member states of the European Union as at the Fifth Enlargement (Romania and Bulgaria
are not included)
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
$795,998 from the rest of the world (ROW) while the remaining $136,777 was exported by
Africa – including intra-Africa trade.
Table 1: Global exports, 2001
Exported by
Composition
Asia
North America
EU25
ROW
Africa
Total
$ million
Merchandise total
1,541,317
1,056,572
2,133,512
795,998
136,777
5,664,177
Agriculture
68,426
86,915
182,223
108,972
20,310
466,846
Resources
30,592
36,894
25,692
225,223
49,348
367,749
1,442,299
932,763
1,925,597
461,803
67,120
4,829,581
Manufactures
Shares of composition of exports from each region
Percent
Agriculture
4.4
8.2
8.5
13.7
14.8
8.2
Resources
2.0
3.5
1.2
28.3
36.0
6.5
93.6
88.3
90.3
58.0
49.1
85.3
100.0
100.0
100.0
100.0
100.0
100.0
Manufactures
Totals
Relative contribution from each region
Percent
Merchandise total
27.2
18.7
37.7
14.1
2.4
100.0
Agriculture
14.7
18.6
39.0
23.3
4.4
100.0
Resources
8.3
10.0
7.0
61.2
13.4
100.0
29.9
19.3
39.9
9.6
1.4
100.0
Manufactures
Source: GTAP African database
The middle part of Table 1 places the type of exports in perspective by showing the relative
composition from each region. The rest of the world and Africa had a much higher
percentage of agricultural products in their export baskets than the global average: 13.7 and
14.8 percent from the rest of the world and Africa respectively compared to the global
average of 8.2 percent. Similarly, their exports of resources were also significantly higher
than the global average of 6.5 percent, while conversely their manufacturing shares were
consequently lower, and in Africa’s case significantly lower at 49.1 percent. Finally, the lower
part of Table 1 shows the relative percentage share of the total exports and their subtotals
from each region. Nearly 40 percent is from the EU (including intra-EU trade), while another
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
nearly 30 percent is from Asia. The contribution from Africa (again including intra-African
trade) is only 2.4 percent of the global total but a significant 13.4 percent of the total global
resources export trade.
2.2
African-related global trade
2.2.1 The big picture
Table 2 provides a further breakdown of African merchandise exports to the world in 2001
and shows that, of the total of $136,777 million in exports, $18,737 million (or 13.7%) were
to Asia; $23,479 million (17.2%) to North America; $66,466 million (48.6%) to Europe;
$11,595 million (8.5%) to the ‘rest of the world’; and the remaining $16,501 million or
12.1 percent of the total were intra-African exports.
Table 2: Exports from Africa to the world, 2001
Exported from Africa to
$ million
Composition
Merchandise total
Asia
North America
EU25
ROW
Africa
Total
18,737
23,479
66,466
11,595
16,501 136,777
Agriculture
3,040
1,271
10,479
1,981
3,540
20,310
Minerals & fuel
6,726
13,165
23,675
3,417
2,040
49,024
Manufactures
8,958
9,027
32,041
6,188
10,907
67,120
Shares of composition of exports from each region
Percent
Agriculture
16.2
5.4
15.8
17.1
21.5
14.8
Minerals & fuel
35.9
56.1
35.6
29.5
12.4
35.8
Manufactures
47.8
38.4
48.2
53.4
66.1
49.1
Relative contribution from each region
Percent
Merchandise total
13.7
17.2
48.6
8.5
12.1
Agriculture
15.0
6.3
51.6
9.8
17.4
Minerals & fuel
13.7
26.9
48.3
7.0
4.2
Manufactures
13.3
13.4
47.7
9.2
16.3
Note: The (minor) trade in fish has not been included in this and the following table to simplify the
presentation.
Source: GTAP African database.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
The central part of the table shows that 14.8 percent of African total exports were
agriculture, 35.8 percent minerals and fuels and the remaining half (49.1%) manufactures.
Both agriculture (21.5%) and manufacturing (66.1%) comprised a higher share of the total
intra-African exports than their comparable global shares, with of course minerals and fuels
commensurably lower. The other feature of this section is that exports of minerals and fuels
to North America were some 56.1 percent of the total exports from Africa to North
America.
The lower portion of the table shows where the relative shares of each sector were
destined. For example, some 51.6 percent of the agricultural exports were destined for the
EU while only 12.1 percent were destined for elsewhere in Africa. Overall, the share of
exports destined for the EU was consistently around 50 percent of the total exports by
sector.
Table 3 duplicates Table 2 by showing the sources and composition of total African imports.
Firstly note that data in the Africa’ column on the right-hand side of the table showing
imports into Africa from Africa (intra-African imports) duplicates the intra-African
exports5.Note secondly that African trade with the world is close to balance: total exports
to the world of $137 billion (Table 2) and total imports from the world of $133 billion.
While the EU is also the main source of African agricultural imports, its relative share of
these imports is lower that the export share. Africa exports $10.5 billion in agricultural
products to the EU (Table 2) while importing a lesser $6.3 billion. Manufactures dominate
the imports, while resources are a minor part of the imports.
5
Note that trade here is expressed in free on board (FOB) prices, so transportation costs are not included.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 3: Exports to Africa from the world, 2001
Exported to Africa by
$ million
$ million
Composition
Asia
Merchandise total
25,240
13,955
59,231
18,117
16,501
133,045
2,797
3,103
6,297
4,076
3,540
19,813
109
144
227
2,751
2,040
5,270
22,326
10,708
52,691
11,286
10,907
107,918
Agriculture
Resources
Manufactures
North America
EU25
ROW
Africa
Total
Shares of composition exports from each region
Percent
Agriculture
11.1
22.2
10.6
22.5
21.5
14.9
Resources
0.4
1.0
0.4
15.2
12.4
4.0
88.5
76.7
89.0
62.3
66.1
81.1
Manufactures
Relative contribution from each region
Percent
Merchandise total
19.0
10.5
44.5
13.6
12.4
Agriculture
14.1
15.7
31.8
20.6
17.9
Resources
2.1
2.7
4.3
52.2
38.7
20.7
9.9
48.8
10.5
10.1
Manufactures
Source: GTAP African database.
Overall, the EU dominates African trade in both manufactures and agriculture, while intraAfrican trade is more than the exports from Africa to ROW but less than those to North
America, while imports from North America, ROW and intra-Africa are somewhat similar.
2.2.2 The African country details for exports
This section continues the 2001 GTAP analysis and examines African exports and the
contribution of agriculture. Table 4 shows the major sources of African exports to the
world, Table 5 the main sources of African exports to the EU, and Table 6 the main sources
of African exports to Africa itself (intra-African trade). In each case the data is shown by
total merchandise exports on the left-hand side and agricultural exports on the right-hand
side of the respective tables. South Africa dominates both the total merchandise and
agricultural exports to the world (Table 4). For total exports, the oil-exporting countries of
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Libya and Algeria, and Nigeria are in second and third places respectively, while the Ivory
Coast and Morocco hold positions two and three for agricultural exports.
Table 4: Main sources of African exports to the world, 2001
Merchandise
exports
Total
$m
136,777
Of which from:
ProAgricultural exports
portion (%)
100.00
$m
20,310
Proportion
(%)
100.00
Of which from:
South Africa
35,654
26.07 South Africa
3,939
19.39
Libya & Algeria
20,750
15.17 Ivory Coast
2,482
12.22
Nigeria
15,055
11.01 Morocco
1,649
8.12
Morocco
8,219
6.01 Kenya
1,158
5.70
Tunisia
6,876
5.03 Egypt
1,106
5.45
Egypt
6,439
4.71 Zimbabwe
1,100
5.41
Rest SADC (Angola)
5,160
3.77 Namibia & Swaziland
770
3.79
Source: GTAP
Table 5, which shows African exports to the EU, highlights that South Africa drops to
second place in both total merchandise and agriculture, with the top spots being held by
Algeria and Libya for total merchandise and Ivory Coast for agriculture. Morocco is in third
place on both sides of the table.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 5: Main sources of African exports to the EU
Merchandise
exports to EU
Total
$m
66,466
Of which from:
Proportion Agricultural
(%)
exports to EU
100.00
$m
10,479
Proportion
(%)
100.00
Of which from:
Libya & Algeria
16,128
24.27 Ivory Coast
1,738
16.59
South Africa
11,410
17.17 South Africa
1,564
14.93
1,027
9.80
Morocco
5,640
8.49 Morocco
Tunisia
5,577
8.39 Kenya
664
6.34
Nigeria
4,115
6.19 Zimbabwe
504
4.80
Egypt
2,486
3.74 Cameroon
494
4.70
Botswana
2,465
3.71 Ghana
393
3.75
Source: GTAP
South Africa dominates intra-African exports for both total merchandise and agricultural
trade (Table 6), with almost half (47.2%) of the total intra-African exports but a lesser
31.0 percent share of the agricultural exports within Africa. The rest of the Southern African
Customs Union (SACU)6 (Namibia, Lesotho and Swaziland) occupy third position in total
merchandise exports behind Nigeria, but climb to second place in the agricultural exports
ahead of Zimbabwe.
6
SACU consists of South Africa, Botswana, Lesotho, Swaziland and Namibia.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 6: Main sources of African exports to Africa
Merchandise
exports
$m
Proportion
(%)
Agricultural
exports
16,501
$m
Proportion
(%)
3,540
Of which from:
Of which from:
South Africa
7,787
Nigeria
1,420
Namibia, Lesotho &
Swaziland
Rest WAEMU
1,074
47.19 South Africa
1,096
30.96
8.61 Namibia, Lesotho &
Swaziland
6.51 Zimbabwe
385
10.88
214
6.05
634
3.84 Rest WAEMU
212
5.99
Ivory Coast
528
3.20 Kenya
181
5.11
Tunisia
480
2.91 Morocco
147
4.15
Libya & Algeria
446
2.70 Ivory Coast
145
4.10
Source: GTAP, where Rest WAEMU (West African Economic and Monetary Union) represents
Benin and Burkina Faso as the other WAEMU countries are represented in their own right.
2.2.3 The African country details for agricultural imports
This section examines the GTAP agricultural trade data relating to Africa. Table 7 shows the
main GTAP agricultural trade sectors ranked by their total import values into Africa from
Asia, North America (NAM), EU25, Rest of the World (ROW) and Africa itself. The last
two columns show the percentage shares from Africa and the percentage shares of these
intra-African imports that are sourced from South Africa. In the lower part of the table the
values of total merchandise imports are shown to put agricultural imports in perspective,
and the percentage shares of both total merchandise and agricultural imports from the
respective sources. Table 7 highlights that:
•
‘Other processed foods’, wheat and beverages and tobacco are the three main
agricultural imports into Africa;
•
The EU, with a 44.9 percent share, is the main source of supply;
•
Intra-African agricultural imports are $3,827 million or 13.3 percent of the total
agricultural imports of $16,126 million;
•
Some 23.4 percent of these intra-African imports are sourced from South Africa (this
percentage is lower than the 46.9% of total intra-African imports that are sourced
from South Africa);
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
•
‘Other crops’ with 42.3 percent sourced from Africa has the highest percentage of
intra-African trade, while wheat with less than one percent has the lowest. Note,
however, that nearly one-quarter of the intra-African trade is in ‘other processed
foods’ and that South Africa has around a one-third share in these exports.
•
Not shown is that after South Africa’s intra-African agricultural exports of
$1,318 million are Rest of SACU (Lesotho, Namibia and Botswana) with $387 million,
Rest of WAEMU with $221 million, Zimbabwe with $216 million and then Kenya with
$181 million. The latter four sources contribute some 26.2 percent of the intra-African
agricultural trade, and when added to South Africa’s 34.4 percent the top five
contribute 60.7 percent of the intra-African agricultural trade.
Table 7: Main sources of African imports by GTAP sector, 2001
Other processed
foods
Wheat
Beverages, tobacco
Dairy
Other crops
Vegetable oils
Other grain
Rice
Sugar
Vegetable fruit
Other meat
Beef
Rest of agriculture
Merchandise
Source
merchandise
Total agriculture
Source agriculture
Total
Sources of African imports ($m)
North
Asia
America EU25 ROW Africa
4,149
3,134
1,717
1,520
1,461
1,440
1,289
1,227
889
729
601
469
1,074
122,825
284
57
50
46
364
374
4
840
352
78
118
111
111
21,673
374
1,340
72
26
57
123
705
70
5
121
67
38
104
12,792
1,749
939
924
1,054
236
308
131
176
41
163
271
56
168
55,104
741
775
260
269
187
502
339
50
211
176
77
192
295
17,130
1,001
23
412
125
618
133
110
91
281
190
69
71
395
16,126
24.1
0.7
24.0
8.2
42.3
9.2
8.5
7.4
31.6
26.1
11.4
15.2
36.8
13.1
31.8
71.7
51.9
60.2
8.6
32.8
57.9
25.7
28.1
49.1
67.8
30.5
10.8
46.9
100.0
28,677
100.0
17.6
5,973
20.8
10.4
3,905
13.6
44.9
9,994
34.8
13.9
4,978
17.4
13.1
3,827
13.3
13.3
34.4
Africa
(%)
To Africa
from South
Africa (%)
Source: GTAP database
To undertake a more in-depth analysis this section will fast-forward to COMTRADE data for
2008. There is a problem in that COMTRADE data is incomplete for Africa, which was the
reason for GTAP’s development of their 2001 African database. For example, Table 8 does
not show data for the non-reporters of Angola and Libya, where the WTO reports imports
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
of $21.1 billion and $11.5 billion respectively. There is a difference in base years (2000 for
COMTRADE and 2001 for GTAP).
Table 8 shows the available African total merchandise imports for 2000 and the most
recent 2008. The data represents 92.9 percent of the African import data available on
COMTRADE, with only the top 20 importing countries displayed.7 Shown are the total
imports and the percentage of those imports from China, the US and the EU. For GTAP we
could only present ‘Asia’ and not China specifically, so there is a concordance issue. Several
features are apparent:
•
The dominance of the EU is declining: from an average share of 42.0 percent in 2000
to 31.0 percent in 2008;
•
Conversely, the presence of China increased from 3.3 percent in 2000 to 9.1 percent
in 2008 (supplanting the US as the second supplier).
7
The next five importers were Zimbabwe, Cameroon, Malawi and Guinea and Mauritania, which represented a
further 3.2 percent of the COMTRADE African imports.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 8: African merchandise imports 2000 and 2008 by source
Imports 2000
Imports
($m)
China
Imports 2008
US
EU
Imports
China
US
EU
South Africa
26,771
3.7
11.9
39.5
87,593
11.3
8.0
29.5
Egypt
14,010
4.6
15.0
35.0
52,752
8.4
10.8
25.0
Morocco
11,533
2.3
5.6
57.9
31,650
5.9
6.1
65.2
Nigeria
5,817
4.3
11.4
47.9
28,194
15.2
8.2
28.5
Algeria
9,152
2.3
11.4
57.3
27,631
8.6
7.7
48.9
Tunisia
8,566
1.2
4.6
70.5
24,638
3.7
3.0
54.6
Sudan
1,860
6.8
2.3
21.7
16,417
7.9
0.3
10.3
Kenya
2,891
3.5
4.4
31.9
11,128
8.4
3.6
16.8
Ethiopia
1,260
7.7
4.8
29.8
8,680
20.2
4.6
16.1
Ivory Coast
2,482
2.7
3.6
41.9
7,884
6.9
2.7
26.8
Ghana
2,933
3.2
7.5
42.8
7,278
11.1
7.6
33.7
Senegal
1,553
2.7
3.6
48.0
6,528
6.0
2.0
39.5
Tanzania
1,586
4.1
3.5
20.1
5,919
7.0
3.2
17.4
Botswana
2,072
0.4
1.7
16.0
5,099
2.8
1.2
10.3
888
1.2
4.7
14.0
5,060
4.5
1.4
10.1
Namibia
1,435
0.5
1.3
7.1
4,689
3.3
2.0
15.5
Mauritius
2,081
7.6
2.9
27.1
4,670
11.5
2.4
21.3
954
3.1
3.2
20.1
4,526
8.1
2.6
18.5
1,162
1.9
3.5
15.8
4,008
3.9
4.0
27.1
Madagascar
991
11.9
4.6
22.0
3,846
21.0
5.0
20.8
Africa Total
110,611
3.3
8.5
42.0
380,403
9.1
6.3
31.0
Zambia
Uganda
Mozambique
Source: COMTRADE.
2.2.3 Regional African imports
The objective of the tralac research is to examine the proposed Tripartite Agreement
between the countries of COMESA-EAC-SADC. Table 10 shows import data for those
countries that report to COMTRADE for 2008 (2007 where 2008 data was not available).
Countries missing from the table include Angola, DRC, Djibouti, Eritrea, Lesotho, Libya and
Sudan, although Angola, Libya, DRC and Eritrean ‘mirror’ data is shown in Table 11. The
countries are ranked by value of total merchandise imports from the world, and the data
also shows the regional (Tripartite) imports by value and percentage share, the percentage
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
share of total imports from South Africa, and the value of imports from the second to fifth
main sources of Angola (oil), Kenya, Zambia and Mozambique into the relevant countries.
While South Africa and Egypt dominate total regional imports, tripartite imports are not
significant in either country except for Angolan imports into South Africa. Conversely, intraregional imports from South Africa are crucial for Botswana, Zambia, Namibia, Mozambique,
Zimbabwe and Malawi, while Kenya has established crucial markets in Uganda and Rwanda.
Table 10: African imports into (most of) the tripartite countries
Regional total
(%)
All merchandise imports ($m)
From tripartite sources ($m)
MozamAngola Kenya
Zambia
bique
2,686
37
287
398
Total
87,593
Regional
5,326
All
6.1
RSA
0.5
52,752
1,222
2.3
0.2
0
201
601
0
11,128
1,174
10.5
6.1
0
0
27
4
Ethiopia
8,680
232
2.7
0.7
0
30
0
0
Tanzania
5,919
833
14.1
10.1
0
104
19
17
Botswana
5,099
4,099
80.4
78.6
1
1
7
1
Zambia
5,060
3,077
60.8
42.6
0
80
0
53
Namibia
4,689
3,271
69.8
67.8
5
1
14
2
Mauritius
4,670
534
11.4
8.1
0
37
8
7
Uganda
4,526
956
21.1
6.7
0
511
1
0
Mozambique
4,008
1,283
32.0
29.1
3
3
15
0
Madagascar
3,846
390
10.1
6.1
0
7
0
1
Zimbabwe
3,442
2,379
69.1
44.6
0
4
110
125
Malawi
2,204
1,351
61.3
26.6
0
55
69
447
Rwanda
1,146
517
45.2
6.6
0
184
2
0
Seychelles
912
102
11.2
5.9
0
4
0
0
Burundi
315
101
32.2
3.2
0
28
3
0
Comoros
120
10
8.3
2.6
0
0
0
0
206,108
26,858
13.0
7.6
2,695
1,287
1,162
1,056
South Africa
Egypt
Kenya
Subtotal
Note: RSA stands for the Republic of South Africa.
Source: COMTRADE data
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 11 completes the analysis shown in Table 10 by showing the missing tripartite partners
as viewed from mirror data. Both Angola and Libya are significant importers, and the DRC is
a significant importer of regional merchandise. As expected, given the geographical locations,
South Africa is a major exporter to the DRC and significant to Angola but hardly registers as
an exporter to Libya.
Table 11: African imports into the remaining tripartite countries, mirror data
All merchandise imports, ($m)
Reporter
Total Regional
Regional %
total
All
RSA
Main tripartite sources
Angola
20,289
1,431
7.1
4.4
RSA
Libya
17,887
1,760
10.3
0.1
Tunis
Egypt
DRC
3,746
1.960
52.3
30.0
RSA
256
27
10.5
2.6
Egypt
42,178
3,220
7.6
4.8
Eritrea
Subtotal
Namibia Morocco
Egypt
Nigeria
Morocco
RSA
Sudan
Zambia
Kenya
Ivory
Uganda
RSA
Kenya
Nigeria Morocco
Source: COMTRADE data, mirror data.
2.3
Regional agricultural trade
Table 12 extends the analysis of African imports to agricultural imports into the tripartite
countries that report to COMTRADE. Shown are the total imports for 2000 and 2008 along
with the percentage of these imports classified as ‘agriculture’ (HS codes HS 01 to HS 24
inclusive, including all fish but excluding wool and cotton fibres). The data is ranked by
agricultural imports during 2008, and Egypt is the leading agricultural importer by virtue of
its greater share of total imports in agriculture compared to South Africa. South Africa has
the lowest agricultural share, but Zimbabwe, Zambia and Tanzania are very close and then
only Sudan is in single figures. Eritrea and DRC have the highest shares, followed by
Seychelles. Egypt’s share of agriculture in total imports has declined over time, while
Ethiopia’s has increased.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 12: The tripartite imports, 2000 and 2008,for total merchandise &
agriculture
Imports 2000 ($m)
Imports 2008 ($m)
Total
imports
14,010
Agricultural
3,577
Agric %
tot
25.5
Total
imports
52,752
Agricultural
9,034
Agric %
tot
17.1
26,771
1,312
4.9
87,593
4,720
5.4
Angola
20,289
2,844
14.0
Libya
17,887
2,425
13.6
Reporter
Egypt
South Africa
Kenya
2,891
411
14.2
11,128
1,328
11.9
Ethiopia
1,260
90
7.1
8,680
1,313
15.1
16,417
1,238
7.5
4,670
980
21.0
3,746
780
20.8
Sudan
Mauritius
2,081
299
14.4
DRC
Tanzania
1,586
234
14.8
11,838
683
5.8
Namibia
1,435
246
17.1
4,689
666
14.2
Botswana
2,072
292
14.1
5,099
619
12.2
954
126
13.2
4,526
580
12.8
1,162
165
14.2
4,008
578
14.4
991
130
13.1
3,846
406
10.6
6,883
380
5.5
Uganda
Mozambique
Madagascar
Zimbabwe
Zambia
888
75
8.5
5,060
295
5.8
Malawi
532
54
10.1
2,204
266
12.0
Seychelles
342
41
11.9
912
185
20.3
1,146
120
10.5
256
71
27.8
315
38
12.0
Rwanda
Eritrea
Burundi
Comoros
Lesotho
Swaziland
150
35
23.4
36
16
43.4
613
109
17.8
1,099
211
19.2
Source: COMTRADE.
Note: Zimbabwe and Tanzania data for 2007, while data for Angola, Eritrea, Libya and DRC is mirror
data.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
This next step looks at regional imports into tripartite countries (reporting and mirror) for
the individual commodities. Table 14 shows both (a) South Africa’s share of these imports
and (b) South Africa’s share of the trade expressed as the export share on the right hand
side. Both cases highlight variability in the South African trade shares. Wheat is the major
regional import at the next level of disaggregation; with Russia and the US the major sources
for wheat. This is followed by palm oil from Indonesia and Malaysia and maize from the US
and Belgium (EU).
Table 14: Tripartite country agricultural imports by commodity, 2008
Imports
RSA %
RSA %
($m)
Main Suppliers
Imports
Exports
17,905
19.3
6.5
3,917.5 Russia
US
11.1
1.5
2,175.7 Indonesia
Malaysia
13.4
1.6
1,356.5 US
Belgium
2.0
10.4
1,038.2 Brazil
India
5.5
9.9
932.3 Thailand
Pakistan
49.8
4.9
923.4 Argentina
Brazil
30.7
4.9
596.3 Spain
Netherlands
3.9
3.7
549.8 Argentina
US
0.8
0.6
519.8 Brazil
India
2.4
0.8
456.5 France
Canada
14.0
3.0
417.1 Argentina
US
74.5
2.4
411.3 Ukraine
Argentina
13.6
19.5
370.5 New Zealand US
6.3
11.6
363.9 UK
South Africa
69.7
10.1
337.3 Ireland
US
44.1
11.3
Other food preparations*
Tobacco
332.5 Zambia
Uganda
31.2
6.1
Poultry
288.3 Brazil
South Africa
65.8
14.7
Tea
275.1 Kenya
India
10.3
3.5
Cotton
270.8 Greece
Zimbabwe
18.8
0.2
Cigarettes
264.5 South Africa Kenya
6.3
35.0
Subtotal
15,797
18.0
5.3
*
Note: Including both crop and livestock products, i.a. homogenised composite food preparations;
soups and broths; ketchup and other sauces; mixed condiments and seasonings; vinegar and
substitutes; yeast and baking powders; stuffed pasta, whether or not cooked; couscous; and protein
concentrates. Also including turtle eggs and birds' nests.
Source: COMTRADE
Commodity
Sub Total
Wheat
Palm oil
Maize
Sugar
Rice
Soybean oil
Frozen fish
Soybeans
Beef
Dried beans/peas
Oilcake
Sunflower oil
Milk powder
Ethyl alcohol
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
3.
Partner trade with Africa for 2009
3.1
Overview
The objective for this section is to analyse partner agricultural trade with Africa for the 2009
December year. The data is sourced from the WTA export data from the respective
countries,8 with further consistency achieved by presenting 2009 data along with 2008 and
2000 as the common base year (with all data December years). Table 15 shows the main
features with the values of exports to Africa from these countries for both 2008 and 2009,
along with the percentage changes for 2009 over the common base at 2000 and the main
destination and main product (to Africa) in each case. The table highlights that:
•
The EU was by far the main supplier during 2009, but that along with the US its
exports declined from the 2008 levels (we have not researched this facet in detail but
assume that the 2008 decline in global commodity prices caused that decline)9;
•
Most other countries have seen a greater export growth than the EU and US from
2000, with Brazil in particular being the big mover; and
•
The main destinations are Algeria, Egypt, Nigeria and Sudan, with wheat, rice, palm oil,
sugar, tea, milk powders and wine as the main products exported from the sources
shown.
8
While the WTA data does not represent complete coverage, it does represent most of the external
agricultural exporters to Africa. Missing for the tripartite countries, as highlighted from Table 14 on
COMTRADE 2008 data, are the sources of Russia (wheat), Pakistan (rice), Argentina (soybean and oil cake
products) and Ukraine (sunflower oil).
9
For example, the EU wheat export average price of $258 per tonne in 2007 increased to $335 per tonne in
2008 before declining to $222 during 2009, while Thailand’s rice average of $410 per tonne in 2007 increased
to $610 in 2008 but declined marginally to $580 in 2009. For Malaysian palm oil the 2007 average of $711 per
tonne increased to $963 in 2008 before declining significantly to $666.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 15: Partner agricultural exports to Africa, 2009
Agricultural exports to Africa ($m)
Country
EU
2008
2009
Growth 20002009 (% p.a.)
Main destination Main product
15,434
12,993
8.5
Algeria
wheat
Brazil
4,499
4,698
24.2
Egypt
sugar
US
5,792
4,195
6.4
Egypt
wheat
Thailand
2,708
2,545
18.8
Nigeria
Malaysia
1,941
1,670
18.3
Egypt
China
1,359
1,388
12.5
Nigeria
Indonesia
1,265
951
21.6
Egypt
New Zealand
719
604
18.4
Algeria
milk powder
Australia
329
563
6.5
Sudan
wheat
Mexico
448
210
31.0
Algeria
wheat
rice
palm oil
tea
palm oil
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
3.2
Trade between the EU and Africa
Table 16 shows the key export destinations in Africa for the EU. These exports were
$12.99 million during 2009, a figure down from $15.43 million during 2008, while the overall
growth in agricultural exports to Africa over the decade was 8.5 percent (marginally higher
than the comparable growth to the world of 7.6 percent). Algeria (18.2 % of the total) was
the main destination, followed by Egypt (10.8%) and South Africa with 9.4 percent. The top
ten destinations shown took three-quarters (74.3%) of the total exports during 2009, while
Angola and South Africa were the fastest growing markets.
Table 16: EU agricultural exports to Africa by destination
Country / Year
Agricultural exports to world
2000
46,395
2008
106,178
2009
92,268
Growth
(% p.a.)
7.6
Agricultural exports to Africa
6,032
15,434
12,993
8.5
13.0
14.5
14.1
1,060
3,297
2,369
8.9
18.2
Egypt
684
1,045
1,404
8.0
10.8
South Africa
308
1,113
1,220
15.3
9.4
Morocco
598
1,700
1,015
5.9
7.8
Angola
232
1,119
988
16.1
7.6
Nigeria
348
1,032
868
10.2
6.7
Libya
395
739
615
4.9
4.7
Ivory Coast
157
442
413
10.7
3.2
Tunisia
319
739
402
2.6
3.1
Senegal
171
459
363
8.4
2.8
4,273
11,687
9,658
9.1
70.8
75.7
74.3
% to Africa
Algeria
Subtotal
Subtotal%
Share (%)
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 17 continues with EU export data and shows the main export products (as defined at
the HS 4 level) for agriculture. The same format is used in this table (and subsequent tables)
to show 2009 data set against 2008 and the 2000 base, with growth rates from 2000 and the
relative shares by destination/product. Wheat dominates exports with nearly-one quarter by
value, followed by milk powders and malt extract. The growth rates are variable, with milk
powders in particular showing only a modest increase.
Table 17: EU agricultural exports to Africa by product
2000
2008
2009
60,501
173,098
147,672
9.9
6,032
15,434
12,993
8.5
Agriculture as % total
9.97
8.92
8.80
Wheat
808
4,061
3,081
14.9
23.7
Milk powder
744
1,621
1,054
3.9
8.1
Malt extract
168
657
564
13.4
4.3
Ethyl alcohol
161
520
485
12.3
3.7
Food preparations
190
507
442
9.4
3.4
Malt
76
425
382
17.9
2.9
Beer
46
368
356
22.7
2.7
Wine
98
395
340
13.8
2.6
Tomato preparations
107
289
305
11.6
2.3
Soybean oil
189
281
290
4.7
2.2
2,587
9,125
7,300
11.5
42.9
59.1
56.2
Total exports to Africa
Agricultural exports to Africa
Subtotal
Subtotal %
Growth (% p.a.)
Share (%)
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 18 introduces imports into the EU from Africa by country, again using the common
format. For 2009 these were some $15.94 billion, a figure above the comparable EU
agricultural exports to Africa of $12.99 billion. The growth rates over the decade of
8.1 percent were marginally below the growth rate of EU exports to Africa (8.5%), while the
percentage of agriculture as defined by the WTO was very similar in the 13 to 15 percent
range for both exports to Africa and imports from Africa. The main sources were Ivory
Coast, South Africa and Morocco (42.9% of the total), while the top ten sources accounted
for just over 80 percent of the total in both 2008 and 2009.
Table 18: EU agricultural imports from Africa
Country / Year
2000
2008
Agricultural imports from world
53,244
128,276
105,627
Agricultural imports from Africa
7,721
16,756
15,939
14.5
13.1
15.1
Ivory Coast
1,284
2,432
2,769
8.5
17.4
South Africa
1,255
3,013
2,500
7.7
15.7
Morocco
629
1,764
1,567
10.1
9.8
Kenya
669
1,427
1,304
7.4
8.2
Ghana
318
1,397
1,194
14.7
7.5
Cameroon
346
767
999
11.8
6.3
Egypt
253
870
846
13.4
5.3
Nigeria
147
538
720
17.6
4.5
Tunisia
262
808
471
6.5
3.0
Ethiopia
183
480
450
10.0
2.8
Subtotal
5,347
13,497
12,819
9.7
69.3
80.5
80.4
% agriculture from Africa
Subtotal %
2009
Growth (% p.a.)
7.6 Share (%)
8.1
of Africa
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 19 continues the analysis of EU imports from Africa and shows these imports by
product. Cocoa beans (25.4% of the total), followed by coffee and citrus fruits, are the main
imports.
Table 19: EU agricultural imports from Africa by product
2000
2008
2009
74,021
217,765
147,643
7.7
Agricultural imports from Africa
7,721
16,756
15,939
8.1
Agriculture as % total
10.43
7.69
10.80
Cocoa beans
1,085
3,285
4,056
14.6
25.4
Coffee
861
1,020
957
1.2
6.0
Citrus
370
1,013
776
8.2
4.9
Cut flowers
261
787
733
11.5
4.6
Sugar
368
787
654
6.4
4.1
Wine
249
637
631
10.3
4.0
Grapes
226
665
631
11.4
4.0
Tobacco
463
401
555
2.0
3.5
Cocoa butter
119
470
546
16.9
3.4
Legume vegetables
182
522
476
10.7
3.0
4,184
9,588
10,014
9.7
54.2
57.2
62.8
Total from Africa
Subtotal
Subtotal %
Growth (% p.a.)
Share (%)
A notable feature of the EU-African bilateral trade for both exports to Africa and imports
from Africa is the degree of concentration. The top three import sources account for
43 percent of the total, while the three top products account for 36 percent of the imports.
At the next level down, the concentration is often even more apparent (Ivory Coast: 80%
are cocoa related; South Africa: the top seven imports are all fruit related – including wine –
accounting for 83 % of the total). A similar situation occurs for exports to Africa, where the
top three export destinations account for 38 percent of the agricultural exports to Africa,
and the top three products represent 38 percent of the total.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
3.3
Trade between the US and Africa
Table 20 details the agricultural exports from the US to Africa. The three main destinations
of Egypt, Nigeria and Morocco account for 64.5 percent of the total agricultural exports to
Africa, while the percentage of US global agricultural exports that are destined for Africa
range between 3 and 6 per cent over the decade (with a high of 5.6 percent in 2007 and a
low of 3.7 percent in 2001). The overall growth to Africa has matched the 6.4 percent
annual change as recorded globally, although there is a variation shown for the top ten
destinations with Algeria and Ethiopia declining.
Table 20: US agricultural exports to Africa by destination
Agriculture to world
2000
56,701
2008
117,758
2009
100,764
Growth (%
p.a.)
6.4
Agriculture to Africa
2,359
5,792
4,195
6.4
4.2
4.9
4.2
1,061
2,028
1,350
2.7
32.2
Nigeria
179
1,055
837
17.1
20.0
Morocco
172
522
514
12.2
12.3
Tunisia
91
176
180
7.6
4.3
Algeria
257
367
173
-4.4
4.1
South Africa
122
399
171
3.7
4.1
28
94
153
19.1
3.7
Djibouti
3
97
102
39.0
2.4
Ethiopia
114
165
95
-2.0
2.3
Angola
55
142
77
3.8
1.8
2,082
5,044
3,653
6.2
88.2
87.1
87.1
% agriculture to Africa
Egypt
Kenya
Subtotal
Subtotal %
Share (%)
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
As shown in Table 21, wheat is the main export to Africa, followed by corn (maize) and then
soybeans (22.8% when oils and soy cake are added to soybeans). Not shown is the
concentration for the products: 84 percent of the wheat went to Nigeria, Egypt and Ethiopia;
92 percent of the corn went to Egypt, Morocco and Kenya; while 100 percent of the soy
beans went to the North African destinations of Egypt, Tunisia and Morocco.
Table 21: US agricultural exports to Africa by product
2000
10,959
2008
28,388
2009
24,327
Growth (%
p.a.)
8.9
Agricultural exports to Africa
2,359
5,792
4,195
6.4
Agriculture as % total
21.53
20.40
17.24
Wheat
974
2,082
1,099
1.3
26.2
Corn
596
839
609
0.2
14.5
Soybeans
44
436
597
28.9
14.2
Soybean oil
38
274
227
19.9
5.4
Vegetable fats & oils
45
318
157
14.0
3.7
Poultry meat
35
184
141
15.7
3.4
109
74
130
2.0
3.1
61
99
113
6.8
2.7
Sorghum
5
201
111
35.0
2.6
Residues
24
107
111
16.9
2.6
Subtotal
1,931
4,613
3,295
5.9
81.8
79.6
78.5
Total exports to Africa
Soybean oilcake
Rice
Subtotal %
Share (%)
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Imports of agricultural products from Africa into the US are shown in Table 22. During 2009
just over 38 percent of these imports were from the Ivory Coast, and although not shown,
99.1 percent of these imports were from the cocoa and chocolate products in HS 18. The
top three sources of Ivory Coast, South Africa and Tunisia supplied 56.6 percent of the total
agricultural imports. The African share of US agricultural imports is a minor 2 percent,
although the growth rate over the decade of 7.7 percent is marginally above that of the
6.4 percent of global imports into the US.
Table 22: US agricultural imports from Africa by destination
Country / Year
2000
2008
2009
Growth (% p.a.)
Share (%)
Total imports from world
42,149
83,130
75,049
6.4
Agricultural imports from Africa
838
1,592
1,675
7.7
% agriculture from Africa
2.0
1.9
2.2
Ivory Coast
259
633
641
10.0
38.3
South Africa
136
205
199
4.2
11.9
Tunisia
11
114
108
25.7
6.4
Ghana
71
24
96
3.4
5.8
Morocco
40
112
92
9.2
5.5
Egypt
41
56
89
8.6
5.3
Ethiopia
26
121
85
13.1
5.1
Kenya
34
63
59
6.1
3.5
Nigeria
4
53
54
28.2
3.2
Malawi
48
52
54
1.3
3.2
Subtotal
671
1,433
1,479
8.8
Subtotal %
80.1
90.0
88.3
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 23 reinforces the degree of concentration in these imports, with 36.3 percent being
cocoa beans (HS1801), and a further 7.7 and 5.9 percent being coffee and cocoa paste
respectively. These agricultural exports as a percentage of total African exports to the US
are minor: only 1.4 percent in 2008 but a higher 2.7 percent in 2009. Oil, with an 82 percent
share in 2009 and 85 percent in 2008, dominates these imports.
Table 23: US agricultural imports from Africa by product
2000
Total from Africa
2008
2009
Growth (% p.a.)
Share (%)
27,638
113,495
62,403
9.0
Agriculture from Africa
838
1,592
1,675
7.7
Agriculture as % total
3.0
1.4
2.7
Cocoa beans
270
589
609
9.0
36.3
Coffee
73
158
130
6.5
7.7
Olive oil
10
107
99
26.0
5.9
Cocoa paste
18
65
83
17.3
5.0
Cocoa butter
18
24
73
15.7
4.4
Citrus fruit
10
66
61
20.6
3.6
Tobacco
48
37
45
-0.8
2.7
Vegetables
16
61
44
11.0
2.6
Oil Seeds
10
34
41
16.2
2.4
Wine
11
45
41
14.2
2.4
Subtotal
483
1,188
1,226
10.4
Subtotal %
57.6
74.6
73.2
Source: WTA
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3.4
Other external sources of agricultural imports into Africa
In an ideal world African import data would be used to compile this subsection, but given
the problems relating to reliable, comprehensive and timely data from Africa, mirror data
from most of the main exporters to Africa have been employed using WTA data. This keeps
a degree of consistency with the EU and US data. Table 24 shows the aggregate picture for
the EU and the US, plus (in order of exports during 2009) Brazil, Thailand, Malaysia, China,
Indonesia, New Zealand and Mexico – these entities constituted the ten biggest sources of
agricultural imports into Africa in 2009. On the right-hand side are (a) the main destinations
for the exporter and (b) the main products by exporter, while the central column shows the
annual growth over the decade. More detailed summaries of these import sources are given
below, again with WTA data and using a common table format.
Table 24: Major agricultural exports to Africa, 2008 and 2009
Country
EU
Exports to Africa
($m)
2008
2009
15,434
12,993
Growth 2000Main
2009 (% p.a.)
destination
8.5 Algeria
Main product
wheat
Brazil
4,499
4,698
24.2 Egypt
soybeans
US
5,792
4,195
6.4 Egypt
Thailand
2,708
2,545
18.8 Nigeria
rice
Malaysia
1,941
1,670
18.3 Egypt
palm oil
China
1,359
1,388
12.5 Nigeria
tea
Indonesia
1,265
951
21.6 Egypt
palm oil
New Zealand
719
604
18.4 Algeria
milk powder
Australia
329
563
6.5 Sudan
wheat
Mexico
448
210
31.0 Algeria
wheat
wheat
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 25 highlights the dramatic growth in Brazil’s agricultural exports to both the world but
especially to Africa, with an average annual growth to Africa of 24.2 percent over the last
decade. Consequently, Africa’s share in Brazilian agricultural exports rose from 4.1 percent
in 2000 to 8.6 percent in 2009. The massive growth to Algeria is another highlight, with
these imports again being dominated by sugar.
Table 25: Brazil’s agricultural exports to Africa
Agricultural exports to world
2000
13,047
2008
58,250
2009
54,743
Agricultural exports to Africa
530
4,499
4,698
% agricultural exports to Africa
4.1
7.7
8.6
107.2
728.2
Algeria
31.6
Nigeria
Growth
(% p.a.)
Share (%)
15.9
24.2
8.6
733.8
21.4
15.6
537.8
639.4
33.4
13.6
114.6
457.3
520.4
16.8
11.1
Top 3 as % total
26.4
38.3
40.3
Sugar
316
1,827
2,313
22.1
49.2
Chicken
26.0
443.8
489.3
32.6
10.4
6.3
539.0
434.3
47.0
9.2
65.6
62.4
68.9
Egypt
Beef
Top 3 as % total
Source: WTA
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Table 26 shows that 93 percent of the agricultural exports from Thailand to Africa are rice,
with Nigeria, South Africa and Benin the main destinations (53.6% of the total during 2009,
and over 50% for 2000 and 2008). Africa is also an important market for Thailand (over 15%
of its agricultural exports to the world in the last two years) and a rapidly growing one as
the 18.8% per annum growth over the decade attests.
Table 26: Thailand’s agricultural exports to Africa
Growth
(% p.a.)
Share (%)
11.4
Country / Year
Agricultural exports to world
2000
5,903
2008
17,682
2009
16,493
Agricultural exports to Africa
470
2,708
2,545
% agricultural exports to Africa
8.0
15.3
15.4
156.6
622.5
584.5
14.6
23.0
South Africa
86.4
374.2
441.1
18.1
17.3
Benin
21.0
405.3
337.7
30.8
13.3
Top 3 as % total
56.1
51.8
53.6
Rice
438.3
2,578.6
2,362.5
18.7
92.8
Sugar
15.0
27.4
78.8
18.4
3.1
5.7
4.6
13.3
9.4
0.5
97.6
96.4
96.5
Nigeria
Tobacco
Top 3 as % total
18.8
Source: WTA
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Malaysia’s exports to Africa (Table 27) are almost as concentrated by both destination
(49.7% to the top three markets of Egypt, Benin and South Africa) and product (72.2% palm
oil). Again, the 18.3% annualised growth is impressive.
Table 27: Malaysia’s agricultural exports to Africa
Agricultural exports to world
2000
4,782
2008
20,972
2009
16,265
Growth
(% p.a.)
13.6
Agricultural exports to Africa
321
1,941
1,670
18.3
% agricultural exports to Africa
6.7
9.3
10.3
Egypt
148.0
284.1
405.6
11.2
24.3
Benin
1.3
369.1
290.5
60.2
17.4
South Africa
61.4
186.4
133.6
8.6
8.0
Top 3 as % total
65.7
43.3
49.7
193.1
1,486.5
1,206.0
20.4
72.2
65.7
156.5
181.9
11.3
10.9
7.5
69.0
87.8
27.3
5.3
83.0
88.2
88.4
Palm oil
Vegetable fats & oils
Malt extracts
Top 3 as % total
Share (%)
Source: WTA
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Chinese agricultural exports to Africa (Table 28) are less concentrated and not as important
to overall agricultural exports. Nigeria, South Africa and Morocco are the main destinations,
while tea and processed tomatoes are the main products.
Table 28: China’s agricultural exports to Africa
Agricultural exports to world
Agricultural exports to Africa
% agricultural exports to Africa
Nigeria
South Africa
Morocco
Top 3 as % total
Tea
Tomatoes
Rice
Top 3 as % total
2000
11,072
450
4.1
13.9
38.1
69.5
27.0
146.1
4.9
187.2
75.2
2008
27,936
1,359
4.9
149.3
146.2
142.9
32.3
316.5
218.2
198.0
53.9
2009
27,180
1,388
5.1
190.2
184.8
169.5
39.2
363.2
272.6
126.2
54.9
Growth
(% p.a.)
Share (%)
10.0
12.5
29.1
17.5
9.9
13.7
13.3
12.2
10.1
44.7
-4.4
26.2
19.6
9.1
Source: WTA
Indonesia’s profile (Table 29) is similar to Malaysia’s (Table 26): palm oil dominates; both
Egypt and South Africa are in the top three destinations; and the growth rate is high.
Table 29: Indonesia’s agricultural exports to Africa
Agricultural exports to world
Agricultural exports to Africa
% agricultural exports to Africa
Egypt
South Africa
Algeria
Top 3 as % total
Palm Oil
Coffee
Margarine
Top 3 as % total
2000
3,999
137
3.4
14.5
30.9
4.1
36.3
79.6
19.5
6.1
77.0
2008
21,526
1,265
5.9
472.3
182.5
237.2
70.5
902.8
101.8
102.1
87.5
2009
17,641
951
5.4
379.3
128.3
97.7
63.6
675.4
72.8
72.4
86.2
Growth (%
p.a.)
Share (%)
16.5
21.6
36.3
15.8
35.2
39.9
13.5
10.3
23.8
14.6
27.5
71.0
7.6
7.6
Source: WTA
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Unsurprisingly, New Zealand’s exports are dominated by the dairy products of milk powders
and butter (Table 30), and Africa has been a fast-growing market over the decade.
Table 30: New Zealand’s agricultural exports to Africa
Agricultural exports to world
Agricultural exports to Africa
% agricultural exports to Africa
Algeria
Egypt
Nigeria
Top 3 as % total
Milk powder
Butter
Malt Extract
Top 3 as % total
2000
5,902
115
2.0
20.2
42.6
1.5
55.8
31.6
47.4
0.2
68.8
2008
14,872
719
4.8
205.8
109.0
152.5
64.9
418.0
122.9
60.2
83.6
2009
Growth (% p.a.) Share (%)
12,504
8.3
604
18.4
4.8
149.6
22.3
24.8
116.4
11.2
19.3
107.3
47.1
17.8
61.8
365.6
27.2
60.5
110.5
9.4
18.3
52.3
60.4
8.7
87.5
Source: WTA
Wheat dominates Australian exports (Table 31), and again the top three destinations of
Sudan, Egypt and South Africa dominate the destinations. These wheat sales are not
consistent, suggesting opportunistic trading by the Australians to Sudan and Egypt in
particular. The overall growth in agricultural exports to Africa is also somewhat modest.
Table 31: Australia’s agricultural exports to Africa
Agricultural exports to world
Agricultural exports to Africa
% agricultural exports to Africa
Sudan
Egypt
South Africa
Top 3 as % total
Wheat
Sheep meat
Milk powder
Top 3 as % total
2000
14,107
314
2.2
2.2
153.7
83.9
76.3
0
51
34
27.1
2008
23,244
329
1.4
20.5
58.4
106.0
56.2
1
73
54
38.9
2009
21,179
563
2.7
164.5
149.3
92.7
72.2
282
52
35
65.7
Growth (%
p.a.)
Share (%)
4.5
6.5
48.1
-0.3
1.1
29.2
26.5
16.5
128.4
0.2
0.3
50.2
9.2
6.3
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
During 2009, exports from Mexico (Table 32) were dominated by the single product of
wheat to the single market of Algeria, with growth in these two products/markets driving
the rapid growth in agricultural products, albeit growth from a low base.
Table 32: Mexico’s agricultural exports to Africa
2000
2008
2009
Growth
% share
Agricultural exports to world
7,636
16,606
15,005
7.5
Agricultural exports to Africa
13
448
210
31.0
% agricultural exports to Africa
0.2
2.7
1.4
Algeria
9.0
388.5
172.9
32.8
82.2
Tunisia
0.4
13.9
10.6
36.3
5.0
South Africa
1.9
6.0
6.4
13.7
3.1
87.9
91.1
90.4
Wheat
3.9
412.7
159.2
41.3
75.7
Vegetables
6.2
20.4
28.5
16.9
13.5
Yeasts
0.0
3.6
6.8
78.6
97.4
92.5
Top 3 as % total
Top 3 as % total
3.2
Source: WTA
3.5
Partner country agricultural exports to tripartite countries
The analysis above looks at the most recent agricultural trade data between most of the
significant partners and Africa as a whole. This section briefly looks at those same partners
and their trade with the tripartite countries. Table 33 shows the partner country exports to
the tripartite countries in 2009 by both dollars (in millions) and percentage of the African
agricultural exports that are destined for the tripartite countries, followed by the three main
export markets. The main exporter is again the EU ($5,397m), followed by the US, Brazil
and Malaysia. The percentage of African exports destined for the tripartite countries varies
from a low of 28.0 percent for Thailand to 91.1 percent for Australia. Egypt and South Africa
(RSA) figure prominently as major markets, with Sudan being Australia’s main market.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 33: Partner country agricultural exports to the tripartite countries, 2009
EU
Exports to the tripartite countries,
2009
% of exports to
$m
Africa
5,397
41.5
Egypt
Main markets
RSA
Angola
US
2,242
53.4
Egypt
RSA
Kenya
Brazil
2,025
43.1
Egypt
Angola
RSA
Malaysia
887
53.1
Egypt
RSA
Djibouti
Thailand
712
28.0
RSA
Mozambique
Angola
Indonesia
647
63.8
RSA
Tanzania
Angola
Australia
513
91.1
Sudan
Egypt
RSA
China
457
32.9
RSA
Egypt
DRC
New Zealand
260
43.0
Egypt
Sudan
RSA
Source: WTA, tralac calculations
A valuable source of agricultural trade data is the FAO database as shown in Table 34, where
69 percent of the total agricultural imports for 2007 are shown in the top 25 products. The
FAO provides imports for all the tripartite countries by all products through to and including
2007, but unfortunately this information is not available by source. The concordance codes
for linking this trade to the following section, which shows South African exports to
tripartite countries by HS codes, is also not available, with the result that the big picture only
can be shown.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 34: Agricultural imports into the tripartite countries
Imports into the tripartite
region
Growth (%
2000
2007
p.a.)
Wheat
% of Africa to
tripartite region
2000
2007
RSA % of
tripartite
2000
2007
1,554
3,450
11.4
44.2
42.9
5.5
7.6
Palm oil
372
1,609
20.9
66.5
68.3
12.6
12.2
Maize
897
1,539
7.7
70.3
59.7
3.5
13.5
Milled rice
425
837
9.7
40.1
40.5
30.7
34.7
Refined sugar
264
697
13.9
33.3
47.1
0.5
0.7
Boneless beef
260
594
11.8
86.3
75.2
3.7
4.0
Food preparations n.e.s.
Wheat flour
295
574
9.5
49.2
45.1
21.6
31.4
395
527
4.1
77.5
70.3
0.1
1.0
Soybean oil
209
510
12.8
46.9
38.0
6.0
41.0
Soybeans
101
479
22.3
66.9
69.6
18.3
7.1
Chicken meat
131
433
17.1
62.2
63.8
24.2
40.1
Tobacco, raw
350
385
1.3
69.3
64.7
12.2
21.1
Distilled beverages
141
383
14.3
80.3
74.9
49.4
67.5
Soybean cake
298
364
2.8
65.2
53.3
22.6
57.5
97
352
18.3
52.8
60.4
0.0
39.9
Raw sugar
151
319
10.7
40.6
27.1
0.0
0.0
Sunflower oil
177
315
8.2
58.6
84.0
25.5
39.5
*
Crude materials
Beer of barley
56
279
23.0
64.7
71.5
8.4
32.5
111
269
12.7
22.5
18.6
5.0
3.2
Rubber natural dry
93
257
14.6
89.4
86.7
80.8
51.1
Non-alcoholic beverages
90
216
12.5
83.2
62.4
17.9
24.5
Wine
71
210
15.6
60.8
54.2
10.6
8.8
Milk, skim dry
73
204
14.7
21.8
27.4
0.0
0.0
Confectionery
56
187
17.2
70.5
68.5
19.5
27.0
Malt
78
176
11.6
53.2
51.9
25.5
14.7
10,700
21,741
10.1
53.6
49.8
13.1
20.0
Milk whole dried
Total
Note: *Including, of vegetable origin: bulbs, tubers, tuberous roots, corms, crowns and rhizomes; live plants,
cuttings and slips; mushroom spawn; cut flowers and flower buds; foliage, branches and grasses, mosses and
lichens; plants and parts used primarily in perfumes, pharmaceuticals, insecticides, fungicides, or for similar
purposes; seaweeds and other algae; vegetable saps and extracts; materials used for plaiting, stuffing or padding;
materials used primarily in brooms or brushes; and materials used primarily in dyeing and tanning. Including, of
animal origin: human hair, unworked and waste; pigs bristles and hair; badger hair and other brush- making hair
and waste; guts, bladders and stomachs of animals (other than fish); skins and other parts of birds with their
feathers or down; bones and horn-cores, unworked, defatted, simply prepared; powder and waste; ivory,
tortoiseshell, whalebone, claws and beaks; coral and shells of molluscs and crustaceans; sponges of animal
origin, ambergris, castoreum, civet and musk; cantharides, bile glands and other animal products used in
pharmaceuticals.
Source: FAOSTAT database, tralac analysis
The table shows that the main imports in 2007 were wheat, palm oil, maize, rice, sugar and
beef, with these six comprising 40.1 percent of the total imports. The columns in the centre
of the main table show the percentage that these imports into the tripartite countries
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
comprise, of the African import total, both by the figures of 53.6 and 49.8 percent for 2000
and 2007 respectively for total imports and the associated percentages for each line. These
individual lines in 2007 varied from a high of 84 percent for sunflower oil to a low of
18.6 percent for whole dried (powdered) milk. On the right-hand side there are two
columns showing the percentage of tripartite imports that are destined for South Africa. For
2007 the overall figure was 20.0 percent, significantly up on the 2000 figure of 13.1 percent,
confirming that South African imports are rising faster than regional or indeed African
agricultural imports. By product line there is variation from zero for raw sugar and skim milk
powder to 67.5 percent for distilled alcoholic beverages.
4.
South Africa’s intra-African agricultural trade
This section examines the role of agricultural trade for South Africa and the importance of
intra-African trade in that portfolio. It again uses the WTA data and starts with a general
outline of South African agricultural exports to the world during 2008 and 2009 and shows
how Africa fits into that picture; the historical perspective over the December years 1997 to
2009 inclusive is also shown. Note that in general the data excludes intra-SACU trade, and
while this is an omission from the big picture it is of little relevance to tralac’s overall
research into the implication of greater liberalisation in the tripartite countries as intraSACU trade is virtually free of restrictions10.
10
We use ‘virtually free of restrictions’ advisedly, as there are constraints imposed by the BLNS countries of
Botswana, Lesotho, Namibia and Swaziland that are seemingly contrary to the wording and spirit of the SACU
Agreement (Sandrey and Vink, 2008).
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
4.1
South African agricultural exports
Table 35 shows the total South African exports to (a) the world and Africa for the 2008 and
2009 December years. The data is shown at HS4 level and ranked by agricultural exports to
Africa.
Table 35: South Africa: aggregate exports to world and Africa for 2008 and 2009
Exports to
the world
HS
code
Description
Total agriculture ($m)
2008
5,517
2009
5,603
Growth
19972007 (%
p.a.)
6.6
Exports to
Africa
2008 2009
1,721 1,866
Growth
19972007 (%
p.a.)
8.3
2008
31
2009
33
% to Africa
1005
Corn (Maize) ($m)
510
445
4.9
445
419
11.0
87
94
1701
Cane Sugar ($m)
218
387
3.8
133
171
7.0
61
44
83
89
13.2
73
79
12.9
87
88
170
170
7.7
52
66
12.4
31
39
2009
Food preparations,
other ($m)
Fruit Juice ($m)
2202
Waters ($m)
65
70
8.0
56
62
7.1
86
88
1512
Sunflower oil ($m)
91
60
9.8
24
60
9.8
27
99
0808
Apples, pears ($m)
367
365
6.8
59
56
10.3
16
15
1103
Cereal meal etc. ($m)
52
52
10.2
51
51
10.2
98
99
2204
Wine ($m)
754
727
12.0
54
49
9.2
7
7
2402
Cigarettes ($m)
63
80
3.3
41
47
0.8
65
59
2,373
2,445
7.2
987
1059
9.1
42
43
43.0
43.6
57.3
56.8
2106
Top 10 Africa $m
Top 10 Africa % Total
Source: WTA
Total agricultural exports from SACU to Africa during the two periods of 2008 and 2009
were $1,721 and $1,866 million respectively, representing 31 and 33 percent of South
Africa’s global agricultural exports respectively. The growth rate of agricultural exports to
Africa is also higher than to the rest of the world (8.3% per year as opposed to 6.6%). By
commodity, maize, cane sugar and ‘other food preparations’ were the main exports to
Africa, with Africa taking some 94, 44 and 88 percent of the totals in these lines respectively.
The top 10ten products to Africa shown represent around 43 percent of global exports but
a higher 57 percent of exports to Africa.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 36 shows the destination of South Africa’s agricultural exports to Africa. After some
years of economic meltdown, Zimbabwe is once again the main destination, followed by
Kenya and Mozambique. These three destinations account for 53.8 percent of the intraAfrican total. Only Nigeria is outside the tripartite alliance.
Table 36: South African agricultural exports to Africa
1997
2007
2008
2009
$m
World
growth
% p.a.
2,536
4,233
5,517
5,603
6.61
686
890
1,721
1,866
8.3
58
60
421
420
16.5
22.5
22.5
Kenya
128
44
123
333
7.9
17.8
40.3
Mozambique
124
197
262
252
5.9
13.5
53.8
Angola
91
131
179
173
5.4
9.3
63.1
Zambia
42
58
223
135
9.8
7.2
70.3
DRC
39
30
41
61
3.7
3.3
73.6
Mauritius
46
53
54
54
1.4
2.9
76.5
Nigeria
4
41
50
49
20.3
2.6
79.1
Tanzania
18
27
41
42
7.2
2.3
81.3
Malawi
32
23
34
41
2.0
2.2
83.5
AFRICA
Zimbabwe
African total %
2009
cumulative
Source: WTA
The main exports to Zimbabwe were maize, sunflower seed oils, cereal groats and sugar
(41.3 % in total) and to Kenya maize constituted 85.4 percent of the total exports, having
increased from $1 million during 2007 to $70 million in 2008 and $284 million in 2009.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
4.2
South African agricultural imports
Table 37 shows that Africa (non-SACU) is not a major source of South African agricultural
imports. During 2008 and 2009 these imports were $259 and $256 million respectively and
only constituted some 5.5 and 6.0 percent of the total imports over these two periods.
Furthermore, with a growth rate of only 2.2 percent from 1997 to 2009 compared to the
total growth of 6.8 percent, Africa is becoming less significant as a source of agricultural
imports. Imports from Zimbabwe, Malawi, Zambia and Mozambique (68% of the total from
Africa) are duty free under the SADC Agreement and hence unlikely to be increased by
trade liberalisation. The top ten countries shown represent 91.8 percent of the total African
imports into South Africa for 2009, with nearly one-third (31.4%) from Zimbabwe.
Table 37: South Africa’s agricultural imports from Africa for 2008 and 2009
1997
1,894.2
2008
4,735.3
2009
4,275.7
Growth
(% p.a.)
6.8
195.8
259.0
255.5
2.2
% from Africa
10.3
5.5
6.0
Zimbabwe ($m)
86.4
77.8
80.2
-0.6
Malawi ($m)
28.9
43.5
41.7
3.1
16.3
47.7
Zambia ($m)
19.6
26.6
29.4
3.4
11.5
59.2
Mozambique ($m)
8.9
26.4
21.6
7.4
8.4
67.7
Ivory Coast ($m)
20.0
16.2
17.6
-1.1
6.9
74.5
Uganda ($m)
0.2
11.4
14.6
34.8
5.7
80.2
Tanzania ($m)
2.1
15.2
13.7
15.5
5.3
85.6
Kenya ($m)
7.0
7.1
6.5
-0.6
2.6
88.1
South Africa ($m)
7.2
6.0
5.0
-3.0
1.9
90.1
Ethiopia ($m)
0.3
4.7
4.4
21.5
1.7
91.8
RSA imports from the world ($m)
RSA imports from Africa ($m)
Share of Africa
total
Cumu%
lative %
31.4
31.4
Source: WTA, where South African imports are either a statistical oddity associated with
SACU or re-imports.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 38 shows the major imports into South Africa from Africa by product. Tobacco is the
main product (Zimbabwe and Uganda), followed by raw cotton (Zimbabwe and Zambia), tea
(Malawi) and cocoa paste (Ivory Coast). The African share of total imports is high in several
of these products, indicating that while Africa is not important overall it is important in
several niche products.
Table 38: South African imports from Africa by product, $ million and % shares.
Africa share of total imports (%)
2008
2009
5.5
6.0
2008
259.0
2009
255.5
Growth (%
pa)
2.2
Tobacco
50.3
48.2
2.1
48.3
25.0
Cotton, raw
50.9
45.9
-2.9
100.0
99.9
Tea
23.9
35.1
6.7
83.1
85.6
Cocoa paste
14.8
16.5
10.7
78.9
88.8
Oilcake
17.5
16.3
6.0
69.4
50.8
Oilseeds
5.5
9.7
4.9
56.6
72.1
Molasses
3.8
8.2
27.8
34.9
74.8
11.2
6.2
9.0
96.8
90.0
Coffee
5.2
6.0
4.5
6.6
11.9
Leguminous vegetables
5.9
4.3
10.2
8.9
6.5
Description
Total
Bran
Source: WTA
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
5.
Informal and unreported trade
Accurate and timely trade data is hard to come by, in Africa even more so because of the
prevalence of informal and unreported trade. Dongala (1993) reports on Africa’s large
informal trade. He postulates that all manner of goods are traded; that the trade is large in
scale, unregulated and undertaken by a multitude of players generally operating on a small
scale but with formal networks; that there is generally complete freedom and ease of entry
and exit for these players; that they escape many formal requirements such as taxes and use
parallel foreign exchange markets to alleviate currency conversion problems; and finally that
the goods are traded at market prices. His regression analysis suggests that there are
positive links between informal incentives to trade and variations in the money and quasimoney situations and that macroeconomic instability influences informal trade.
Villoria (2008) reports that it is commonly accepted that in some instances over 40 percent
of the potential trade flows are informal and unreported: his work systematically estimated
the likely magnitude of this missing trade by using a gravity model to assess where high fixed
costs can actually prohibit trade within Africa, on the assumption that this may explain at
least some of the common zero-valued flows that characterise intra-African trade. Overall,
his analysis suggests that missing exports were valued at approximately $300 million during
the base year of 2001, with missing trade highest in the lowest income countries of Central
and West Africa but not of southern Africa.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
In southern Africa, the Famine Early Warning System (FEWS) network monitors crossborder trade that takes place without being recorded and therefore does not form part of
official statistics.11 They currently monitor 29 border points in the region and report on
trade that is unrecorded because it is carried out without any formal documentation, and/or
is
considered
negligible
so
as
not
to
require
formal
permits
and
other
documentation. Though small, the volumes reported in Table 39 end up being quite
substantial in many cases. They only report on maize, rice and beans, with maize meal
converted into grain equivalents. More recently, a separate analysis has been included to
show the proportion of maize meal as against the maize grain. This is a recent addition to
the data, and shows that maize meal is around ten percent of the maize total by weight.
Table 39: Informal cross-border food trade, July 2004-July 2010 (tonnes)
Maize
Exports
Malawi
Mozambique
RSA
Tanzania
Zimbabwe
Totals
Imports
Exports
Beans
Imports
Exports
Imports
31,319
511,718
14,596
9,489
3,182
33,760
421,137
12,588
12,503
1,486
20,531
1,427
531
270
19,207
434
8,321
130,193
DRC
Zambia
Rice
699
16,958
4,875
75,214
5,954
47,577
130,518
50,879
54,039
9,653
1,452
55,583
185
12,739
722,941
722,941
86,898
86,898
32,262
33,674
6,585
2,388
77,125
77,125
Source: FEWS NET Southern Africa, Phumzile Mdladla personal communication
11
See http://documents.wfp.org/stellent/groups/public/documents/ena/wfp221803.pdf.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Table 40 provides a comparison between the assessed informal trade in these products as
shown above and the official reported data from the FAO database for maize, rice and
beans. These are indicative only, as the years overlap somewhat and the definition may not
be directly comparable, but nonetheless if the FEWSNET data is indeed not recorded, then
in some instances the informal trade is significant:
Table 40: A comparison between formal and informal trade, 2005-2007 (tonnes)
Exports
Maize
Malawi
Mozambique
RSA
Tanzania
DRC
Zambia
Imports
FAO
189,288
Informal
312,201
Proportion
(%)
165
446,150
1,160
0
FAO
392,882
Informal
5,550
Pro-portion
(%)
1
123,264
223,251
181
2,825,335
1,737
0
209,568
112,921
54
346,809
4,609
1
0
56,906
22,481
40
263
100,167
39,179
39
178,752
25,150
14
1,277
379
30
940,663
17,416
2
3,652,756
383,017
10
2,158,568
383,017
18
5,120
1,922
38
8,843
6,927
78
334
7,248
2170
1,067,223
14
0
RSA
57,685
146
0
2,521,402
Tanzania
34,074
2,731
8
214,492
1,802
1
188,889
26,970
14
Zimbabwe
Maize tot
Rice
Malawi
Mozambique
DRC
0
0
Zambia
439
27,929
6362
53,217
1,851
3
Zimbabwe
282
0
0
75,143
2,412
3
Rice, total
97,934
39,976
41
4,129,209
39,976
1
4,884
1,081
22
11,915
15,044
126
Beans
Malawi
Mozambique
RSA
Tanzania
8,615
762
9,810
408
4
208,904
0
0
27,390
8,077
29
15,552
4
0
11,418
22,672
199
1918
20,994
1,886
9
0
38,576
944
2
94
307,359
41,312
13
DRC
Zambia
Zimbabwe
Beans, total
1,206
23,131
556
43,846
41,312
Source: FAO database and FEWS data, tralac analysis
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
•
Except for some relatively minor flows from South Africa to Zimbabwe, the South
African informal trade appears to be very low based on this analysis, and given the
often large numbers that dominate trade overall, in some cases this lowers the overall
percentage figures;
•
Conversely, there are significant trade flows associated with Mozambique and maize
exports and Malawi and maize imports where the informal trade is more than the
reported trade. An examination of the FEWSNET data shows that over their entire
reporting period this particular bilateral maize flow represents just over half of the
total maize informal trade, and maize is the most significant of the commodities traded;
•
Exports of rice from Mozambique and particularly Zambia are significantly above the
reported data; this in turn influences the informal rice percentages. The data shows
that the Mozambique exports are to Malawi and Zimbabwe, while the DRC is the
destination of most of the large Zambian rice exports;
•
The informal trade in beans is dominated by Zambian exports to the DRC (especially
in the very early period), Tanzanian exports to Malawi (especially in the later period)
and Mozambique’s exports to Malawi where the trade is spread evenly across all three
periods;
•
Overall, for maize the informal trade represents ten percent of the exports and 18
percent of the imports, for rice the comparable percentages are 41 percent for
exports and an insignificant one percent for imports, while for beans the informal
exports almost match the FAO data and the formal imports are thirteen percent of
the reported data.
It is therefore evident that in many instances informal trade is significant while especially with
processed products, such as rice, not generally grown in the region, it is likely that reexports of products that may have entered the exporter are reported here and then
informally transshipped. This is almost certainly the case within SACU, as except for tariff
revenue distribution there is no real need to report intra-SACU trade flows.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
7.
Key points and summary
The objective of this chapter has been to examine Africa’s trade in agricultural products,
with special attention to intra-African trade and the proposed COMESA-EAC-SADC
tripartite FTA grouping. It has been difficult to compile a comprehensive and consistent
presentation of agricultural trade, necessitating the use of different data sources with
different agricultural definitions over different time periods.
The study began with a discussion of the special GTAP African database, a base adjusted to
incorporate best estimates of the ‘missing’ unreported trade that beguiles African data. The
drawback is that its base is 2001. This data shows that:
•
While 2.4 percent of global merchandise exports were from Africa, a greater
4.4 percent of global agricultural exports originated there. In total, Africa exported
$137 billion’s worth of goods to the world, with some $20.3 billion (14.8%) of these
agricultural products, and $3.5 billion representing intra-African exports that remained
within the continent. Nearly half (48.6%) of Africa’s agricultural products went to the
EU, with South Africa ($3.9 billion) and Ivory Coast ($2.5 billion) the main exporters;
•
Africa imported agricultural produce to the value of $19.8 billion (14.9% of total
merchandise imports into Africa), with 31.8 percent sourced from Europe and
12.4 percent ($3.5 billion) originating within Africa. Of the latter, $1,096 million was
from South Africa and $385 million from Namibia, Lesotho and Swaziland combined.12
The COMTRADE data confirms the dominance of the EU in African trade, and reports from
their database that South Africa, Egypt, Morocco, Nigeria and Algeria were the biggest
traders in 2008 overall. They report total African imports of $380 billion, although there is
missing data. For the tripartite countries, COMTRADE reports imports of $206 billion, with
13.0 percent intra-African trade. Mirror data for the missing reporters adds another
$42 billion, with 7.6 percent of this intra-African trade. Tripartite agricultural imports were
$29.5 billion, 10.8 percent of the total. The main agricultural importers were Egypt,
South Africa, Angola and Libya, with wheat (Russia and the US), palm oil (Indonesia and
Malaysia) and maize (US and Europe) the main import products.
12
This includes intra-SACU trade, trade that is not always reported in South African trade data.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
An examination of the main sources of agricultural imports into Africa using the more recent
2009 data for their exports shows that:
•
For agricultural products, the EU exported $12,993 million to African countries in
2009 and imported $15,939 million from the same group. Algeria, Egypt, and South
Africa were the main export destinations while Ivory Coast, South Africa and Morocco
were the main import sources. Wheat, milk powder and ethyl alcohol were the main
exports and cocoa beans, coffee, citrus and cut flowers the main imports.
•
The US exported agricultural products worth $4,195 million to Africa in 2009, and
imported $1,675 million. Egypt, Nigeria and Morocco were the main export markets,
and wheat, corn (maize) and soybeans were the main export products. Ivory Coast,
South Africa and Tunisia were the main import sources, with cocoa beans, coffee and
olive oil the main import products.
•
Other main exporters to Africa examined, with the main product and market in
parentheses, were Brazil (sugar to Nigeria and Algeria), Thailand (rice to Nigeria),
Malaysia (palm oil to Egypt), China (tea to Nigeria), Indonesia (palm oil to Egypt), New
Zealand (milk powders to Algeria), Australia (wheat to Sudan) and Mexico (wheat to
Algeria).
•
South Africa exported $1,866 million in agricultural products to Africa (33% of South
Africa’s global agricultural exports) during 2009. Maize, sugar, food preparations and
fruit juices were the main exports, and Zimbabwe, Kenya and Mozambique the main
destinations. South African agricultural imports from Africa were only $256 million or
6.0 percent of its total agricultural imports. Tobacco, raw cotton and tea were the
main imports.
The overall conclusion from detailed agricultural trade analysis of partner country exports to
Africa is that the EU was by far the main supplier during 2009, but that along with the US its
exports declined from the 2008 levels; that both the EU and US in particular show exports
declining in 2009 over 2008; that most other countries have seen a greater export growth
from 2000 than the EU and US; and that the main destinations are Algeria, Egypt, Nigeria and
Sudan, with wheat, rice, palm oil, sugar, tea, milk powders and wine being the main products
exported from the major sources examined.
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Chapter 3 – Intra-African trade in southern and eastern Africa and the role of South Africa
Looking only at the African sub-set of the tripartite countries we found that the main
exporter is again the EU, followed by the US and Brazil. The percentage of African exports
destined for the tripartite countries varies from a low of 28.0 percent for Thailand to
91.1 percent for Australia. Egypt and South Africa figure prominently as major markets, with
Sudan as Australia’s main market. Examining the best available African import data from
COMTRADE during 2007/08 we found that of the top 13 specific agricultural imports that
accounted for 76 percent of the total imports, the main two sources in all cases were
external to Africa. Intra-African agricultural trade is not significant. Earlier analysis of the
2001 GTAP database confirms this, and adds that where there was a degree of intra-African
trade, South Africa was often the main source of this trade
References
Dongala, J, 1993. The informal sector trade among Sub-Saharan African countries: a survey
and empirical investigation. The Development Economies, XXXI-2, June 1993.
FEWS & USAID. 2010. Informal Cross-Border Food Trade in Southern Africa. A United
Nations World Food Programme Issue 59, April-May 2010., FEWS and USAID publication.
Sandrey, R. and Vink, N. 2008. unpublished report.
Villoria, N. 2008. Estimation of Missing Intra-African Trade. Paper submitted to the Eleventh
Annual Conference on Global Economic Analysis. Helsinki, Finland, June 12-14
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Chapter 4
The tripartite Free Trade Agreement: A computer analysis of the impacts
Hans Grinsted Jensen and Ron Sandrey
Summary and key points
In this chapter we examine the implications of the so-called tripartite countries of the
Common Market for Eastern and Southern Africa (COMESA), the East African Community
(EAC) and the Southern African Development Community (SADC) entering into a genuine
Free Trade Agreement (FTA). We use the Global Trade Analysis Project (GTAP)1 latest prerelease Version 8 database to assess the welfare and trade gains from this FTA as
determined by duty-free merchandise goods access and with a small (2%) reduction in
assumed non-tariff barriers to both merchandise goods and services barriers also factored
in. Importantly, our simulation starts from the assumption that the three regional blocs of
COMESA, EAC and SADC have their FTAs operating in a comprehensive manner in that all
three have tariff-free trade within their blocs but not outside their blocs. Thus, our results
relate to combining these three blocs into one large tripartite FTA. We stress that our
GTAP results are indicative only, but as we are using the pre-release Version 8 GTAP
database with extensive African country disaggregation we feel that these results offer a very
realistic view of the final outcome. They are, to coin a phrase, ‘the best game in town’, as the
use of a model such as GTAP forces consistency and closure in the resource allocative
process and GTAP is the international model of choice for trade analysis.
We have also identified and updated the GTAP database with developments such as the
implementation of the Trade, Development and Cooperation Agreement (TDCA), and,
most significantly, we have assumed that the Economic Partnership Agreements (EPAs)
between all African countries except South Africa and the European Union (EU) will be
implemented. For the EPA we have assumed that (a) EU27 tariffs are reduced to zero for all
EPA countries except for sugar and beef where reductions were made but not to zero,
(b) for South Africa the EU reduces their tariffs by 20 percent in an agreement associated
with the EPA, and (c) all EPA countries reduce their tariffs by a blanket 40 percent on EU
1
See the GTAP website at https://www.gtap.agecon.purdue.edu/ for a full introduction to the model.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
imports.2. We concentrate in the paper upon the results for South Africa, and to a lesser
extent the BLNS (Botswana, Lesotho, Namibia and Swaziland) countries as represented in
GTAP by (1) Botswana and (2), from the perspective of this analysis, the unsatisfactory
GTAP aggregation of Lesotho, Namibia and Swaziland.
The results show that there are significant gains for South Africa, but only for South Africa
and Mozambique. South African welfare increases by US$1,321 million or 0.29 percent of
the real Gross Domestic Product (GDP). Although these gains cannot be taken as exact
figures but rather as indicative, they suggest that an FTA covering the entire eastern part of
Africa is a policy objective that warrants every consideration. The gains to South Africa
derive from a better use of land, labour and capital (enhanced allocative efficiency), increased
net investment increasing the amount of capital employed in the economy, and some
contribution from increased labour employment and terms of trade gains. These welfare
gains are much more than the gains from either an FTA with China (with its associated
impacts on South African manufacturing) or with Mercosur (with its associated impacts on
the agricultural sector) as inferred from earlier tralac research.
Results for the rest of the Southern African Customs Union (SACU) (namely Lesotho,
Namibia and Swaziland) are disappointing with a welfare loss of $84 million, while Botswana
similarly loses $16 million. Most other tripartite partners gain or lose very marginally, except
Mozambique which gains $57 million. All non-African countries outside the FTA lose.
This increase in demand for South African exports leads to an appreciation of the real
exchange rate in South Africa, resulting in a terms of trade gain. The South African economy
gains from this appreciation of the real exchange rate (0.335%), as the value of total income
(sum of factor income and indirect tax receipts) increases by 0.600 percent, while prices
‘only’ increase by 0.270. This translates into a raise in Equivalent Variation (EV) of
US$1321 million when measured in fixed prices.
For agriculture, the tripartite FTA is only beneficial in sugar and then only for the South
African and Mozambican agricultural sectors. Sugar production increases by $388 million or
12.4 percent at 2020 relative to where it would have been. Most of this is driven from
enhanced exports to the rest of east Africa (Kenya) where these South African exports and
2
We appreciate that this may not be an exact representation of the EPA outcomes but it seems to us a
realistic one.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
those from Mozambique replace imports from non-Africa. We caution that there are some
uncertainties about initial Kenyan tariffs, and to the extent that we overstate these we
overstate the gains for South Africa.
Manufacturing is the big gainer for South Africa, but again really only for South Africa with
lesser gains for Egypt and rest of eastern Africa (Kenya). The large bulk of increased South
African exports is the extra $940 million going to rest of east Africa, and this is large enough
to cause South African imports from non-Africa to increase and trade diversion away from
the US to take place. The overall result is that exports from South Africa increase by
$511 million but imports from mostly outside Africa increase by $754 million. We cannot
provide a breakdown as to what type of manufacturing is driving this response as we are
using one aggregated sector only.
Decomposing the GTAP results shows that most of South Africa’s gains are from tariff
reductions in the rest of the East Africa aggregation, an aggregation that contains Kenya in
particular. And these gains are almost exclusively from manufacturing and sugar.
Revenue for the SACU tariff actually increases by US$49 million as a result of South Africa
manufacturing imports from non-African countries to replace increased exports to the rest
of east Africa. Therefore the 2002 formula from the SACU Agreement is at least not
working against the BLNS countries and tariff revenue loss is not an issue here. This is in
complete contrast to the SACU tariff revenue losses from SACU-Mercosur and SACUChina FTAs.
Employment and real wage outcomes are both positive for South Africa: employment in
unskilled workers increases by 0.14 percent and real wages by a greater 0.47 percent. The
unskilled labour market closure used in our version of the model is a function of a labour
supply elasticity which is calculated from initial unemployment rates. We model two
extreme alternatives to this. The first is an option which has wages fixed and forces
adjustments in the labour market through changes to the employment rate. This time the
welfare gains to South Africa increase by around two and a half times to US$3,082 million or
0.746 percent of GDP as a result of an increase in unskilled labour of 1.03 percent in the
workforce. The second alternative is to fix the number of persons employed with
adjustments as changes to wage rates. Here the result is a 20 percent decline in welfare
relative to our standard closure of US$1,046 million following an increase of 0.54 in the real
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
wage rate. Hence, the policy of promoting employment rather than increasing wages is
clearly a superior option for South Africa to pursue and a tripartite FTA would potentially
make a contribution to this objective.
Finally, it is notable that the overall services output in South Africa increases by
US$3,296 million, with this mainly being driven by increased demand for services as the
production of capital goods and other industries expand the South African economy. Trade
in services sees South African exports decline by 1.2 percent and imports increase by
0.9 percent in order to provide for these increased services.
Alternative scenarios
Negotiating and implementing an FTA is a complex and drawn-out process,. Recognising this
we use the GTAP model to explore an alternative option called ‘Proudly Africa’ that we feel
has both political attraction and an ease of implementation throughout the continent to
mutually assist fellow African countries. It is simply a continent-wide campaign to ‘Buy
Africa’. It is not driven by tariff preferences but merely by a cooperative understanding to
increase the share of trade sourced from within Africa; tariffs are not relevant.
Essentially, for African countries we instruct GTAP to import ten percent more in total from
African sources in each commodity. It then reduces the number of exports from nonAfrican regions into Africa so that the new total demand for imports in each African country
still remains equal to the old total number of exports to the country in question from all the
different sources (African and non-African countries).
Gains to Africa are a significant $3,871 million. These gains accrue to all African
countries/regions except Botswana and Angola/DRC. The big winner is manufacturing in the
continent’s powerhouse, South Africa, although this time we are seeing solid gains also from
the rest of West Africa aggregation and Nigeria. All non-African countries lose and world
welfare declines as Africa marginally turns away from the global low-cost producers. Thus,
‘Proudly Africa’ is good for Africa but not for the world.
‘Proudly Africa’ presents an interesting study that shows that large gains can be made in the
absence of a formal negotiation that which would undoubtedly require difficult and
protracted negotiations. It is simply a non-binding agreement to encourage the purchase of
African merchandise goods, and is modelled by increasing imports of African products by ten
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percent as applicable. It clearly shows that the way forward for Africa is largely in African
hands. ‘Proudly Africa’ capitalises on World Bank data for GDP growth over the current
decade, reinforcing the concept that this century may belong to Africa. Its beauty lies in its
simplicity, and, in the final analysis, the welfare gains are significant and relatively evenly
spread.
Policy implications
A full FTA tripartite is beneficial for the South Africa, but with gains from manufacturing and
sugar only. Given our labour market closure assumption, a full FTA tripartite leads to an
increase in employment and wages for unskilled workers. It also produces a small increase in
the SACU tariff revenue pool monies as there are tariffs to pay on increased manufacturing
imports since most imports are from outside Africa. The policy implications for South
Africa are clear: there are no downsides to this FTA such as the decimation of South Africa’s
manufacturing sectors that would result from an FTA with China or the challenges that a full
FTA with Mercosur would present to agriculture. Every effort must be made to facilitate this
African initiative.
A policy issue is that we are unable to comment upon is the distributional effects of the
beneficial changes in the agricultural sector between the numerically smaller commercial
sector with its disproportionate share of production and the numerically much larger small
or subsistence sector. We would, however, hypothesise that in general the latter is only
marginally exposed to the changes as it has a much smaller exposure to the full agricultural
market as producers, but stress that the only real gains are in the sugar sector where there
is more black empowerment and ownership taking place – something which may potentially
be advantageous to black empowerment.
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Section 1: Introduction
In assessing South Africa’s future trade policy options, the increasing focus on the African
continent and in particular the so-called ‘tripartite’ agreement has to be considered. The
trade and political economic background to this agreement was discussed in earlier chapters,
and now the focus is on the quantitative analysis of how South Africa’s trading relationship
with the tripartite countries may be advanced by the adoption of an FTA between
South Africa (or more properly, SACU) and the remaining blocs of COMESA, EAC and
SADC. To assist with this analysis, the internationally accepted benchmark GTAP database –
discussed below – and its associated general equilibrium model is used here as an analytical
tool. In undertaking this analysis, the starting point is a simulation of the ‘known’ and best
estimate conditions that will prevail at the end of a given period (2020 in this case) followed
by an assessment of the difference that the selected FTA policy change under consideration
is likely to make.
It is important to be up-front as to what our policy change is. We simulate a full FTA
between COMESA, EAC and SADC after each of these three regions itself has made the
necessary steps to full sub-regional integration. Thus, we are not examining the benefits to
SACU of taking the FTA to its logical conclusions, for example, but rather the next and final
steps in regional integration past that intermediate point. Our interest is in the tripartite
FTA only, and we believe that the necessary starting point for that FTA is the fully
functioning operation of the three sub-FTAs in the tripartite region. We note that in order
to reach this point there needs to be a resolution of the overlapping memberships in the
region, and unfortunately Kenya is aggregated into a regional group that includes, among
other smaller countries, Sudan. Annex 1 shows a table outlining the problem of overlapping
membership and how this complicates not only the politics but also our modelling results.
Basically, if a country like Mauritius belongs to both SADC and COMESA, and Kenya belongs
to both EAC and COMESA, there will be no gains to Mauritius for sugar exports to Kenya
as it already has that access since both countries belong to COMESA. Thus, it is hardly
surprising that, as both the powerhouse of Africa and one of the few countries not claiming
multiple memberships, South Africa gains the most from a tripartite FTA.
The objective of this chapter is therefore to simulate the tripartite agreement and not the
pathways to that agreement. We believe that such an analysis provides a useful pointer to
the potential gains for South Africa in particular, although we assure readers that we are fully
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
cognisant of the complexities of the South African/SACU trade policy formulation
procedures and will do no more than outline the main implications for BLNS in this chapter.
It is tralac’s intention, however, to examine BLNS more comprehensively in a separate
report later. Following the theme of this report as encapsulated in the earlier chapters we
will again mostly concentrate upon the agricultural sectors in this analysis.
Section 2: The GTAP database/model
GTAP is supported by a fully documented, publicly available global database, as well as
underlying software for data manipulation and for implementing the model. The framework
is a system of multi-sector economy-wide input/output tables linked at the sector level
through trade flows between commodities used both for final consumption and intermediate
use in production. The latest GTAP pre-release Version 8 database divides the global
economy into 112 countries/regions with 57 commodities specified in the database. The
database represents global trade in the year 2007 measured in millions of (2007) US dollars.
The standard GTAP model is a comparative, static, general equilibrium model, which means
that while it examines all aspects of an economy via its general equilibrium feature (as
distinct from a partial equilibrium approach that examines only the sector under
consideration), it is static in the sense that it does not specifically incorporate dynamics such
as improved technology and economies of scale over time unless these are specifically built
in. The economic agents of consumers, producers and government are modelled according
to neoclassical economic theory, with both producers and consumers maximising their
profits and welfare respectively with markets assumed to be perfectly competitive and all
regions and activities linked. Results are measured as a change in welfare arising principally
from the reallocation of resources within an economy and the resulting changes in allocative
efficiency, terms of trade effects3, capital accumulation and changes in unskilled labour force
employment. This change in welfare is based upon a representative household, so unless this
is modified it is not possible to examine the distributional aspects other than through the
3
Where terms of trade are the relative changes in import and export prices following a change. Improved
allocative efficiency within a country comes about as it moves resources into more internationally competitive
activities following a reduction in its own border protection. Paradoxically, it is this allocative efficiency that
provides most of the benefits to the ‘home’ country from reducing its own protection rather than the exporter
gaining better market access as the partner country reduces tariffs. This is an example of where a general
equilibrium model is often able to counter the common mercantilist argument that a country needs protection
to develop its own industrial sector.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
skilled/unskilled labour market closures. The standard GTAP model also does not address
the time path of benefits and capital flows over time. These changes are important as they
allow consumers to borrow, which in turn allows consumption patterns to vary over time.
The interpretation of GTAP results
The GTAP model expresses the welfare implications of a modelled change in a
country’s policy as the Equivalent Variation (EV) in income. The EV in income
measures annual change in a country’s income (gains or losses) from having
implemented, for example, an FTA. The EV in income is simply defined as the
difference between the initial pre-FTA income and the post-FTA income after
implementation of the FTA, with all prices set as fixed at current (pre-FTA)
levels.
EV in income = post-FTA income – pre-FTA income
If a country’s EV in income increases due to a policy change, the country can increase its
consumption of goods equal to the increase in income and thereby improve the national
welfare in the country. The EV is a doubly effective measure for measuring the global
economic impacts of an FTA agreement between groups of countries. Firstly, the EV
provides a monetary valuation of effects induced by FTA policy changes globally and at the
country or regional level, so as to illuminate winners and losers. And secondly, the EV also
facilitates comparisons of different policy scenarios, given that income changes are measured
in initial base prices.
These total welfare gains/losses can be decomposed into contributions from improvements
in allocative efficiency, capital accumulation, changes in the employment rate of the labour
force, and terms of trade.
Gains from allocative efficiency arise from improved reallocation of productive resources
(such as labour, capital and land) from less to more productive uses. For instance, when
import tariffs are abolished, resources shift from previously protected industries towards
other sectors, which are more in line with the country’s comparative advantage, producing
an increase in real GDP and economic welfare.
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Terms of trade effects are the consequence of changing export and import prices facing a
country. So, when a country experiences an increase in its export price relative to its import
price (e.g. due to improved market access), it may finance a larger quantity of imports with
the same quantity of exports, thus expanding the supply of products available to the
country’s consumers. Whereas allocative efficiency contributes to increases in global welfare
gains, changes in terms of trade affect the distribution of global welfare gains across
countries; essentially, one country’s terms of trade gain is another country’s terms of trade
loss. The global total must therefore add to zero, and if a large proportion of the benefits to
South Africa from an FTA are derived from terms of trade effects, this implies transfers to
South Africa from the rest of the world.
Capital accumulation summarises the long-run welfare consequences of changes in the
stock of capital due to changes in net investment. A policy shock affects the global supply of
savings for investment as well as the regional distribution of investments. If a trade
agreement has a positive effect on income through improvements in efficiency and/or terms
of trade, a part of that extra income will be saved by households, making possible an
expansion in the capital stock. At the same time, rising income will increase demand for
produced goods, pushing up factor returns and thus attracting more investments. Generally,
economies with the highest growth will be prepared to pay the largest rate of return to
capital, and will obtain most of the new investments. Therefore we will tend to see that the
long-run welfare gains from capital accumulation reinforce the short-term welfare gains
deriving from allocative efficiency and terms of trade.
The welfare effects of changed employment rates are consequences of changes in the
extent of the unskilled labour force employed due to changes in the real wage. In a situation
where the demand for labour and thereby the real wage increases, the amount of labour
employed increases, reducing the relative rise in the real wage and thereby increasing the
competitiveness of the country’s industries (increasing EV in income).
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Section 3: The GTAP simulation
The analysis undertaken in this chapter is based upon a variant of the GTAP model to
simulate the impact of possible multilateral market access reforms resulting from a tripartite
FTA. The database is the Version 8 pre-release GTAP database (Badri & Walmsley 2008)
with the base year 20074, where the 2004 tariff data originating from the Market Access
Maps (MAcMap) database has been used with some verification and minor modifications. The
main unskilled labour market closure of the model has been changed so that the supply of
unskilled labour is endogenously determined by the labour supply elasticity.
As with any applied economic model, this model is, of course, based on assumptions, both in
terms of theoretical structure and the specific parameters and data used. Regional
production is generated by a constant return to scale technology in a perfectly competitive
environment, and the private demand system is represented by a non-homothetic demand
system (Constant Difference Elasticity function).5 The foreign trade structure is
characterised by the Armington assumption implying imperfect substitutability between
domestic and foreign goods.
The macroeconomic closure is a neoclassical closure where investments are endogenous
and adjust to accommodate any changes in savings. This approach is adopted at the global
level, and investments are then allocated across regions so that all expected regional rates of
return change by the same percentage. Although global investments and savings must be
equal, this does not apply at the regional level, where the trade balance is endogenously
determined as the difference between regional savings and regional investments. This is valid
as the regional savings enter the regional utility function. The quantity of endowments (land,
skilled labour and natural resources) in each region is fixed exogenously within the model,
while the extent to which unskilled labour is employed is endogenously determined. The
capital closure adopted in the model is based on the theory according to which changes in
investment levels in each country/region appear on-line instantly, updating the capital stocks
4
The
documentation
of
the
Version
7
database
can
be
found on
the
website
https://www.gtap.agecon.purdue.edu/databases/v7/v7_doco.asp. Documentation of the Version 8 database is
not available at this point in time (January 2011).
5
Hence, the present analysis abstracts from features such as imperfect competition and increasing return to
scale, which may be important in certain sectors. We are therefore using what can be thought of as a base
GTAP structure.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
endogenously in the model simulation.6 Finally, the numeraire used in the model is a price
index of the global primary factor index.
The applied ad valorem equivalent (AVE) tariff data found in the GTAP Version 8 database
originates from the MAcMap database, contributed by the Centre d’Etudes Prospectives et
d’Information Internationales (CEPII). The MAcMaps database is compiled from UNCTAD
TRAINS data, country notifications to the WTO, the Agricultural Marketing Access
Database (AMAD), and from national customs information (Bouet et al. 2005). The MAcMap
database contains bilateral applied tariff rates (both specific and ad valorem) at the 6-digit
Harmonised Systems (HS6) level. These are then aggregated to GTAP concordance using
trade weights.7
Baseline projection 2007–2020
Before simulating the trade policy (FTA) scenario, we construct a baseline scenario to serve
as an updated basis for analysis. The baseline scenario updates the standard database with a
projection of the world economy from 2007 to 2020, applying suitable shocks to GDP,
population, labour and capital, as well as incorporating the most important developments,
realised or planned, since 2007. We have identified and updated the database with the
developments such as the implementation of the Trade, Development and Cooperation
Agreement (TDCA, and, most significantly, we have assumed that the Economic Partnership
Agreements (EPAs) between all African countries except South Africa and the EU will be
implemented. For the EPA we have assumed that (a) EU27 tariffs are reduced to zero for all
EPA countries except for sugar and beef where reductions of 50 percent were made rather
than to zero, (b) for South Africa the EU reduces their tariffs by 20 percent in an agreement
associated with the EPA, and (c) all EPA countries reduce their tariffs by a blanket
40 percent on EU imports.8 Finally, again we reiterate that we have used as our point of
departure the position that the COMESA, EAC and SADC FTAs are all fully functional in
that there is tariff-free access between their respective members.
6
This capital closure adopted in the model is the so-called Baldwin closure as documented in GTAP Technical
Paper no. 7.
7
Although the base year is 2007 for macro data and trade data, the tariff data (AVE) is from 2004. This should
make little difference to the result as tariff changes over the 2004 to 2007 period were minimal.
8
We appreciate that this may not be an exact representation of the EPA outcomes but it seems to us a
realistic one.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
For the country/regional aggregation we have used the countries and aggregations as shown
in Table 1.
Table 1: GTAP countries/regions used and their associated GTAP codes
The tripartite countries
ZAF
South Africa
BWA
Botswana
XSC
Rest of SACU (Lesotho, Namibia and Swaziland)
XAC
Rest of southern Africa (Angola and DRC)
EGY
Egypt,
XNF
Rest of north Africa (Algeria and Libya)
ETH
Ethiopia
MDG
Madagascar
MWI
Malawi
MUS
Mauritius
MOZ
Mozambique
TZA
Tanzania
UGA
Uganda
ZMB
Zambia
ZWE
Zimbabwe
XEC
Rest of Eastern Africa (including Kenya and Sudan)
Other Africa
MAR
Morocco
TUN
Tunisia
NGA
Nigeria
SEN
Senegal
XWF
Rest of Western Africa
XCF
Rest of Central Africa
Outside of Africa
CHN
China
EU
EU27
US
United States of America
IND
India
BRA
Brazil
LAM
Rest of Latin America
RUS
Russian Federation
ROW
Rest of the world
Source: GTAP database
Notes:
•
The three available groupings for SACU of South Africa and Botswana as countries in their
own right and the only option of the ‘rest of SACU’ comprising Lesotho, Namibia and
Swaziland. This is of course not ideal as the three economies are very different, but there is no
alternative;
•
Another thirteen tripartite countries/regions, although note that XNF, the rest of North
Africa, includes Algeria with Libya;
•
Another 6 African countries/groupings outside of the tripartite; and
•
And the remaining groupings of China, the EU, US, India, Brazil, rest of Latin America, the
Russian Federation and the rest of the world (RoW).
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
For the GTAP sectors we have used the full set of agricultural sectors that are available but
we often only report on the main ones of interest. Manufacturing, natural resources and
services are merged into their respective aggregated sectors as the focus of this report is on
agriculture.
As always, we apply shocks to GDP, population, labour force, and capital to project the
world’s economy to the baseline year of 2020 – a year in which we assume that an FTA
could be fully implemented. The projection of the world economy uses the exogenous
assumptions listed in Table 2, and is important in shaping the baseline scenario. The general
sources for the assumptions in Table 2 are given in a footnote to the table, and these
assumptions represent the best estimates of the possible future path of the data.
The GTAP model then determines changes in output through both an expansionary and a
substitution effect in each country/region of the model. The expansionary effect represents
the effects of growth in domestic and foreign demand shaped by income and population
growth and the assumed income elasticities. The substitution effect reflects the changes in
competitiveness in each country/region shaped by changes in relative total factor
productivity, cost of production as well as any policy changes. The GTAP model uses this set
of macroeconomic projections to generate the ‘best estimate’ of global production and trade
data for 2020. The relative growth rates of each country/region for GDP, population, labour,
capital and total factor productivity play an important role in determining the relative growth
in output of the commodities when projecting the world economy from 2007 to 2020, and
we can now take the resulting data set from this baseline simulation as the new base for our
FTA scenario. A simulation scenario measures the difference between our baseline model’s
output in 2020 in the absence of, for example, the FTA, against the likely output if an FTA
were concluded. The model results shown in this chapter present the isolated effect of a
possible FTA or other simulated scenario in the year 2020.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Table 2: Macroeconomic projections expressed as average annual growth rates,
2007–2020
Real Population
GDP
3.8
0.4
3.4
0.5
3.6
1.0
4.9
1.4
4.7
1.3
4.0
1.2
3.3
1.4
5.8
1.8
4.0
1.9
Total
Force
1.3
0.8
0.9
1.7
2.1
2.1
2.2
3.0
2.9
Labour
Unskilled
Skilled
Capital
Total
Factor
Productivity
0.5
0.5
0.5
0.8
0.7
0.4
0.4
1.2
0.3
South Africa
1.2
1.7
3.8
Botswana
0.7
4.2
3.4
Rest of SACU
0.8
1.8
3.6
Egypt
1.7
2.2
4.9
Morocco
2.1
2.6
4.7
Tunisia
2.0
4.5
4.0
Rest of North Africa
2.0
3.6
3.3
Nigeria
2.9
3.4
5.8
Senegal
2.9
4.2
4.0
Rest of Western
Africa
3.8
2.0
2.6
2.5
3.1
3.8
Rest of Central Africa
4.6
2.1
3.3
3.2
3.9
4.6
Angola/DRC
11.7
2.8
1.8
1.8
3.3
11.7
Ethiopia
8.2
2.0
2.7
2.7
3.2
8.2
Madagascar
5.2
2.3
2.9
2.9
3.4
5.2
Malawi
5.6
1.7
2.6
2.6
4.1
5.6
Mauritius
3.4
0.9
3.5
3.4
5.6
3.4
Mozambique
6.7
1.0
2.7
2.7
4.1
6.7
Tanzania
6.6
1.6
2.7
2.7
-0.8
6.6
Uganda
6.0
2.7
3.1
3.1
5.9
6.0
Zambia
5.3
1.1
2.8
2.8
4.3
5.3
Zimbabwe
0.0
0.7
1.6
1.6
3.2
0.0
Rest of Eastern Africa
5.2
1.7
2.3
2.3
3.2
5.2
China
8.6
0.6
0.8
0.8
3.9
8.6
EU27
0.9
-0.1
0.0
0.1
-0.1
0.9
United States of
America
1.9
0.7
1.2
1.6
0.8
1.9
India
6.7
1.1
1.7
1.5
3.9
6.7
Brazil
3.2
1.0
1.1
0.9
3.0
3.2
Rest of Latin America
2.8
1.3
2.3
1.9
4.2
2.8
Russian Federation
3.5
-0.6
0.0
-0.1
0.4
3.5
Rest of the world
1.6
1.1
1.7
1.6
2.2
1.6
Source: World Bank forecasts, Walmsley (2006) and own assumptions
Note: The annual growth rate in total factor productivity (TFP) is determined endogenously by the
exogenous variables (GDP, unskilled/skilled labour force and capital), the model and the associated
database.
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0.2
0.5
2.0
2.0
0.8
1.0
-0.1
1.2
1.4
1.3
0.7
-0.3
0.9
1.0
0.2
0.3
1.3
0.4
0.1
0.8
0.0
Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
The tripartite FTA scenario
The FTA primary scenario considered in this chapter entails the results of the removal of
trade barriers between tripartite members as measured in the year 2020 in a world shaped
by the baseline scenario and after the three regional groupings have activated their own
FTAs. This implies that:
•
all ad valorem tariffs and ad valorem equivalents of specific tariffs between tripartite
countries are abolished;
•
an assumed two percent blanket tariff equivalent to represent non-tariff barriers
(NTBs) has been built in to proxy a reduction in these barriers from an FTA. We note
that there is no empirical justification for that two percent level other than an intuitive
feel that NTBs are often of that level or even considerably more;
•
a similar two percent NTB has also been applied to services to proxy some gains from
an FTA where services trade has been factored in.
When all ad valorem tariffs and ad valorem equivalents of specific tariffs between members
are abolished, the differences between the so-called baseline scenario and this so-called
primary scenario as measured by the gains at 2020 in 2007 real dollar terms are therefore
the result of the implementation of the simulated tripartite FTA. Again, it does not account
for the interim benfits for the three regional groupings of activating their own FTAs.
Section 4: The big picture – GTAP results
Table 3 shows the changes in welfare from the FTA under the assumptions used, with the
data expressed in US$ millions as one-off increases in annual welfare at the assessed end
point of 2020. The results for South Africa are impressive: welfare increases by some
$1,321 million, a figure that is more than the total gain to Africa of $1,202 million. These
gains to South Africa result from the contributing factors of increased investment expanding
the capital stock (US$618m) and allocative efficiency gains of US$327 million as resources
are better employed in the economy. These are enhanced by solid gains from increased
labour employment (US$124m) and by the terms of trade improvement of US$253 million
resulting from a favourable change in relative prices between South African exports and
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
imports. To put these welfare gains in perspective, they are around five times the welfare
gains found from tralac research using an earlier version of GTAP (Sandrey et al. 2010) to
assess the gains from FTAs with Mercosur ($236 million) and China ($295) million. Clearly,
the current policy thrust of concentrating upon African integration is the best pathway for
South Africa.
The GTAP welfare result for South Africa’s fellow SACU members from the FTA shows a
different picture. Botswana demonstrates a welfare loss of $16 million (mostly from terms of
trade loss), while the rest of SACU as an aggregation of Lesotho, Namibia and Swaziland
shows a significant loss of $84 million from less optimal capital use in particular.
Mozambique, a country that belongs exclusively to SADC, is the only other country to show
a significant welfare gain ($57 million), and indeed of the other tripartite countries there is
none which records a double-digit gain while several report double-digit losses (Egypt in
particular).
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Table 3: Change in welfare (EV of income) due to tripartite FTA (US$ million)
zaf
bwa
xsc
egy
mar
tun
xnf
nga
sen
xwf
xcf
xac
eth
mdg
mwi
mus
moz
tza
uga
zmb
zwe
xec
total Africa
chn
EU
ua
ind
bra
lam
rus
row
Total
$ million
Total
1,321
-16
-84
-29
-2
-1
3
-7
0
12
-2
-16
-14
-1
-5
-3
57
8
6
-10
-5
-10
1,202
-151
-237
-132
-80
-71
-28
-38
-206
258
Allocative
Efficiency
327
1
-5
-10
0
0
0
-2
0
4
0
1
-4
0
0
0
10
0
1
0
0
48
370
-7
-29
-27
-18
-19
-11
-5
-51
203
Labour
124
0
-8
-2
0
0
0
-1
0
0
0
-1
-1
0
0
0
7
0
-2
0
0
-16
100
-5
-5
-3
-2
-5
-1
-1
-9
71
Capital
618
-6
-44
-10
-1
-1
0
-6
0
6
-2
-6
3
0
-1
-1
15
9
13
-5
-1
78
658
-142
-178
-82
-43
-34
-17
-37
-139
-15
Terms of
Trade
253
-11
-28
-6
0
0
3
1
0
1
0
-10
-12
-1
-4
-2
25
-1
-5
-5
-4
-119
74
3
-26
-20
-17
-14
2
5
-7
-1
Source: GTAP results
In addition, Table 3 shows:
•
Losses to the all non-African countries: in particular the EU with a loss of
$237 million, China ($151 million) the US ($132 million) and the rest of the world
($206 million);
•
all of these changes result in a global welfare gain of just $258 million, and as shown
in Table 3, this is mainly from enhanced allocative efficiency with a contribution from
labour effects; and
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
•
importantly, that the very large welfare gain for South Africa translates into an
increase in real GDP of 0.291 percent (Table 4) in 2020 through better allocative
efficiency and the expansion of the capital stock, which push out the production
frontier in the South African economy, and the resultant labour and terms of trade
gains.
Why the negative results for most of the tripartite regions?
Much of this can be explained by the time path taken to reach the adoption of the tripartite
agreement. Here we assumed that all three regional FTAs have been fully activated, and we
have taken the current memberships at their face value. This means, for example, that
Tanzania is a member of both SADC and EAC, with zero tariffs operating between other
members of each group. Similarly, Botswana is a member of SACU/SADC and COMESA.
For these countries with multiple memberships there is therefore (a) a limited up-side from
a tripartite agreement as they already have most of the gains through being allied with an
extensive grouping and (b), consequently, when South Africa, the dominant economic
powerhouse in the region, enters the full tripartite agreement these countries face new
competition. As outlined above, we have not analysed their welfare and trade gains from
their initial FTAs prior to tripartite.
In further examining the GTAP results we are able to decompose the results to find that:
South Africa’s welfare gains of $1,321 million derive as follows: $145 from the two percent
reduction in goods and services to represent better trade facilitation, $1,177 from the
general tariff elimination access into tripartite countries and the balance an offset loss from
other reactions in tripartite countries. For non-African countries the overall loss of
$943 million breaks down to $-127 from the two percent facilitation changes with the bulk
of $-816 from the general tripartite tariff elimination.
Table 4 expands on the welfare gains for selected economies to show on the left-hand side
what the actual percentage changes are in terms of trade, real GDP and factor income. The
right-hand side of the table provides some insights into where the contributions to changes
in factor income derive from, and the individual contributions shown that must add to the
total factor income.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Column 3 (real GDP) shows that South Africa gains by 0.29 percent, while Botswana’s loss is
0.03 percent and the rest of SACU’s 0.29 percent. The only other significant gainer is
Mozambique with a GDP gain of 0.18 percent as the country becomes fully integrated into
the tripartite region, and, as discussed, all other tripartite countries gain or lose marginally.
Table 4: Percentage changes in terms of trade, real GDP and factor income,
2020
South Africa
Terms
Of
Trade
0.20
Total
Real
Factor
GDP
Income
0.29
0.61
With contributions Factor Income from
Labour
Natural
Unskilled Skilled
Land
Capital
resource
0.009
0.202
0.112
0.293
-0.010
Botswana
-0.13
-0.03
-0.01
0.000
0.006
0.000
-0.001
-0.017
Rest SACU
-0.30
-0.29
-0.59
-0.075
-0.178
-0.099
-0.268
0.026
Egypt
-0.01
-0.01
-0.02
-0.011
-0.012
-0.003
-0.011
0.014
Ang/DRC
-0.01
0.00
-0.01
-0.004
-0.002
-0.001
-0.002
0.000
Ethiopia
-0.12
0.00
-0.13
-0.041
-0.046
-0.005
-0.031
-0.008
0.34
0.18
0.92
0.318
0.352
0.064
0.205
-0.021
Tanzania
-0.01
0.02
0.03
-0.003
0.011
0.001
0.015
0.003
Uganda
-0.06
0.04
-0.12
-0.034
-0.057
-0.012
-0.027
0.014
Zambia
-0.07
-0.03
-0.04
-0.013
-0.010
-0.002
-0.010
-0.002
Zimbabwe
-0.11
-0.06
-0.18
-0.071
-0.031
-0.012
-0.039
-0.027
Rest East
Africa
-0.21
0.06
-0.16
-0.028
-0.071
-0.022
-0.048
0.007
Mozambique
Source: GTAP results
On the right-hand side of Table 4, the relative contributions to total factor income9 are
shown. These must equate with the total factor income percentages shown. Thus, for South
Africa’s 0.61 percentage increase in total factor income, the majority (0.293%) comes from
capital, skilled labour (0.112%) and unskilled labour (0.202%) and a small contraction from
land and natural resources. Note that for land, skilled labour and natural resources, the
quantities are fixed in the GTAP model, so the small changes derive from price changes as
their values are bid up or down, while for both unskilled labour and capital, where the
9
In this paper the percentage change in factor income is defined as the percentage change in returns to primary
factors employed in the economy (i.e. the returns to land, labour, capital and natural resources). These changes
in returns to primary factors employed occur due to reallocation of resources in the economy (allocative
efficiency), changes in employment and changes in investment/capital stock which are all driven by price changes
due to the modelled FTA tariff reductions. These changes in factor income occur with no changes in the
production technology employed in the economy (no changes in TFP) as we have not modelled technological
spillovers through trade in the FTA scenarios.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
quantities are not fixed, there is both a price and a quantity effect. Mozambiques gains derive
from unskilled labour, although capital also making a small contribution.
Table 5 extends this analysis to look at the agricultural sector changes. Here the quantity of
land is fixed in that it can only be used in primary agricultural production while
unskilled/skilled labour and capital can change in both price and quantity as resources can
move freely in and out of other industries in the economy. We can see that for all SACU
members, and for the rest of SACU in particular, the impacts upon agricultural factor
income are modest for South Africa, neutral for Botswana but marginally negative for rest of
SACU. Land prices increase by up to 0.26 percent in South Africa and 1.16 percent in
Mozambique while contributions from employed unskilled agricultural labour and agricultural
capital are also marginally positive for these two countries. Thus, tripartite FTA is modest
news for agriculture in South Africa and Mozambique but has no effect elsewhere. Other
tripartite members with the more measurable results are also shown.
Table 5: Percentage changes in primary agricultural factor income, 2020
South Africa
Agricultural
factor
income
0.54
With the relative contributions from
Land
0.262
Unskilled
labour
0.167
Skilled
labour
0.002
Capital
0.107
Botswana
0.02
-0.007
0.003
0.000
0.021
Rest of SACU
-1.81
-1.318
-0.288
-0.004
-0.203
Egypt
-0.16
-0.085
-0.057
-0.001
-0.017
Mozambique
1.96
1.161
0.665
0.005
0.124
Uganda
-0.14
-0.065
-0.060
0.000
-0.018
Zimbabwe
-0.47
-0.423
-0.036
0.000
-0.005
Rest East Africa
-0.30
-0.134
-0.137
-0.003
-0.029
Source: GTAP results
Changes in trade flows
Table 6 introduces the aggregate overall changes to trade flows for the different
countries/regions in 2020, expressed as percentage changes in both exports and imports,
and then in US$ millions for the trade balance. The trade balance is fixed in the model to
savings minus investments, so the FTA increases investments more than savings in
South Africa, thus reducing the trade balance by $203 million as these investments are
sourced from abroad. The rest of SACU sees both exports and imports decline, while there
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
are modest improvements for trade performances in Mozambique, Uganda and rest of
east Africa and perhaps Ethiopia. There are effectively no changes to aggregate trade flows
for other tripartite countries, and as expected from the welfare results, all of the nontripartite countries/regions show an almost imperceptible change in their trade percentages.
Table 6: Percentage changes in quantity of total import/export & trade balance,
2020
Change in
South Africa
Botswana
Rest of SACU
Egypt
Morocco
Tunisia
Rest of North Africa
Nigeria
Senegal
Rest of Western Africa
Rest of Central Africa
Angola/DRC
Ethiopia
Madagascar
Malawi
Mauritius
Mozambique
Tanzania
Uganda
Zambia
Zimbabwe
Rest of Eastern Africa
China
EU27
US
India
Brazil
Rest of Latin America
Russian Federation
Rest of the world
Exports (%)
0.82
-0.14
-0.88
0.03
0.00
0.00
0.01
0.00
0.00
0.02
-0.01
0.00
0.11
-0.05
-0.22
-0.07
0.53
0.14
0.57
-0.15
-0.21
0.65
Imports (%)
0.61
-0.02
-0.57
0.09
0.00
0.00
0.01
0.00
0.00
0.02
0.00
0.04
0.49
-0.02
-0.10
-0.04
0.25
0.19
0.71
-0.07
-0.10
1.44
Trade balance
US$m
-202.7
-0.7
6.3
8.7
1.2
0.3
0.6
0.6
0.2
-5.7
0.5
5.1
5.2
-0.5
0.5
0.1
-8.4
-2.1
-1.8
-0.3
0.3
11.7
-0.01
0.00
0.00
-0.01
-0.02
0.00
0.00
0.00
0.00
0.00
0.00
-0.01
-0.01
0.00
0.00
0.00
-11.8
73.0
64.9
-7.2
11.9
4.5
9.8
35.8
Source: GTAP results
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Section 5: The agricultural sector in South Africa
This section discusses the production, trade and relative price changes in the main GTAP
agricultural sector, with an emphasis on how they relate to South Africa (the aggregation we
used in the analysis only presents natural resources, manufacturing and services in that
aggregated form). Table 7 shows the changes from the agricultural sectors of (a) primary
agriculture and (b) secondary (processed) agriculture in the top and middle sections of the
table and (c) natural resources, manufacturing and services in the bottom section. Column 1
shows GTAP sectors, with Column 2 showing the increase in US dollar (million) values and
Column 3 in percentage terms. The next two columns show contribution of these changes
from firstly the two percent reduction in NTB as proxied and then the contribution from
tripartite tariff eliminations. The three right-hand columns show firstly the percentage
changes in exports and imports and then the change in real producer output prices.
Table 7: Changes to South Africa’s production, trade and output prices
Change Production
GTAP sector
$ million
%
Primary agriculture
paddy rice
0
-0.1
wheat
-1
-0.4
other grains
13
0.2
vegetable, fruit
10
-0.1
oil seed
2
0.2
sugar cane
55
4.9
plant fibre
8
0.1
other crops
11
-0.1
live animals
15
0.1
other animal products
17
0.0
milk
6
0.1
wool
-30
-0.9
Secondary agriculture
beef/sheep/goat
23
0.1
other meat
17
0.1
vegetable oil
6
0.0
dairy
12
0.0
processed rice
0
0.3
sugar
388
12.4
other food
110
0.3
Beverages, tobacco
93
0.3
Total: Agriculture
757
resources
10
0.0
manufacturing
1,022
0.2
services
3,296
0.3
Total
5,084
Of which, from
NTB
Tariffs
-0.1
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
-0.1
0.0
-0.4
0.1
-0.1
0.2
4.8
0.1
0.0
0.1
0.0
0.1
-0.9
0.0
0.0
0.0
0.0
0.0
0.3
0.0
0.0
0.0
0.0
0.0
Change in
Export Import
Price
-0.7
0.8
-0.1
1.7
0.8
0.5
1.8
1.3
-1.5
-0.1
0.7
-0.8
1.1
2.4
1.6
0.9
-1.6
1.9
0.3
0.3
0.4
0.3
0.4
1.1
0.3
0.4
0.4
0.3
0.4
0.2
0.1
0.1
0.0
0.0
0.2
12.2
0.2
0.3
-0.5
0.4
0.9
-0.6
0.3
37.8
0.9
1.0
1.7
1.5
0.7
1.4
0.2
2.6
1.0
0.6
0.3
0.3
0.2
0.3
0.0
0.4
0.3
0.3
0.0
0.1
0.3
-0.1
0.7
-1.2
0.3
0.9
0.9
0.1
0.2
0.3
Source: GTAP results
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
The outcome from Table 7 is a simple one. For agriculture, virtually all the gains are from
sugar, with minor contributions from other food and beverages/tobacco. With sugar cane,
when the input into raw sugar is added to processed sugar, other foods and
beverages/tobacco, this accounts for over 85 percent of the increase in the value of
agricultural production in South Africa. In percentage terms the only significant change is the
12.4 percent increase in processed sugar production and the 4.9 percent increase in cane
values leading into this processed sugar. The result for trade is similar, where only processed
sugar, other foods and beverages/tobacco register meaningful changes.
Output prices
generally increase marginally by between 0.2 to 0.4 percent across the board, with sugar and
sugar cane driving this increase through substitution effects. South African sugar does
moderately well from the tripartite FTA, while there are very limited changes elsewhere.
Manufacturing production in South Africa increases by $1.022 billion, but this is still well
behind the increase of $3.3 billion in services output. Manufacturing and services increase in
output price by 0.2 and 0.3 percent respectively, and this drives the respective production
increase of the same values. Service exports decline and imports increase as attention
focussed internally because of the growing economy, while for manufacturing both exports
and imports increase. We are not able to offer information on which manufacturing sectors
have prospered. The final profile for natural resources is one of absolute neutrality.
Sugar
As the results show, the only agricultural sector that does warrant extra attention is the
sugar sector. These results come about because sugar is highly protected in most of the
region (and indeed the world), and Table 8 shows changes to global sugar trade at 2020 over
and above what it would have been in the absence of the tripartite FTA. The destinations
are shown in rows on the left-hand side and exporters are in the columns across the table.
Reading along the bottom row we see that global exports from South Africa increased by
$371.8 million and from Mozambique by $134.8 million, making these two countries the
clear winners in the sugar outcome from the tripartite FTA. Indeed, there are no other
countries which increase exports, and notable is that rest of SACU (read Swaziland)
decreases its exports by $118.1 million and Egypt, relying to some extent on sugar beet,
decreases exports by $68.4 million. These changes are at the expense of the non-African
exporters where their exports decline by $208.8 million. Global exports overall increase by
$64.5 million, so the sugar outcome is trade creating rather than purely trade diverting. The
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
big change in imports is the trade into the rest of East Africa (Kenya and Sudan and some
other smaller countries). Here their imports increase by $50.9 million, with big increases of
$351 million from South Africa and $151.4 million from Mozambique that are replacing
imports from the rest of SACU ($123.6 million) and non-Africa ($205.5 million). All other
changes are minor, but note that there are some substitution effects surrounding the EU
sugar trade, with more from the rest of SACU and less from Mozambique and non-Africa in
particular.
Table 8: Changes to regional sugar trade
Destination/
Exporter
South Africa
Rest SACU
Uganda
Rest East
Africa
EU
Total
zaf
xsc
egy
mwi
0.2
-0.6
25.0
-6.7
-1.2
-1.3
moz
0.0
0.0
0.8
351.0
-0.2
371.8
-123.6
10.7
-118.1
-68.1
0.1
-68.4
-4.3
0.0
-5.2
151.4
-16.5
134.8
tza
-7.5
-0.5
-11.1
uga
nonAf
8.7
-0.1
-0.2
tot
9.0
-1.3
11.4
-11.9
0.1
-11.6
-205.5
-10.9
-208.8
50.9
-4.7
64.5
-3.1
Source: GTAP output
We need to add a caveat on this sugar outcome for the rest of east Africa. There are
conflicting reports for sugar tariffs into Kenya, presumably the main market influencing the
trade changes in sugar. MacMaps, the standard source for these tariffs, shows that Kenyan
tariffs are 85.3 percent for sugar at an aggregate level, while the WTO reports them at
62.5 percent at that same aggregate level. In the GTAP model they are applied at the
detailed tariff rate of 79 percent for Kenya as found in MacMaps. The higher the tariff the
greater the impacts of these tariffs reducing to zero will be. Unfortunately, a great deal of
the result for the simulation hangs on this one tariff.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Section 6: Manufacturing
This sector is driving the tripartite results, and in particular manufacturing in South Africa,
the only country in Africa with an established manufacturing capacity. Although
manufacturing is just modelled as one aggregate sector and therefore no details are available,
there is nonetheless valuable information contained in the GTAP output. This relates to the
absolute changes in manufacturing trade as measured by imports into each country from
each source in the country/regional set. This change can either be expressed in relative
percentage change terms or in absolute dollar value terms, with the absolute dollar values of
the increases shown in Table 9. The table is read as for the sugar table above, with
importers down Column 1 and exporters in the rows across the table.
Table 9: Changes in manufacturing trade following the tripartite FTA,
2007 $ million at 2020
Destination/
Importer
zaf
South Africa
Rest SACU
xsc
egy
2.4
40.5
0.7
xwf
14.0
moz
-2.5
tza
uga
4.3
9.5
0.1
3.8
0.9
24.6
nonAf
total
648.2
754
1.8
1.6
-39
0.8
-39.1
22
-44.8
0.1
Egypt
55.5
0.6
Ethiopia
69.3
0.4
-2.4
0.2
2.8
1.1
-59.5
11
Uganda
92.2
-0.1
-0.9
0.2
-0.7
-10.0
-68.1
12
939.7
-1.5
-12.5
-0.3
0.3
5.6
-4.3
-4.3
-723.6
185
EU
-155.8
4.0
-0.9
-0.3
-57.9
0.1
0.3
54.8
51.8
-100
Rest nonAf
-291.4
7.3
-0.9
-0.3
-1.3
1.2
3.5
43.4
1.1
-239
511
24
59
13
-68
19.6
16
196
-155
619
Rest East Africa
Total
-0.1
xec
Source: GTAP output, where ‘Rest nonAf’ denotes non-African exporters other than the EU
South Africa’s increased trade of $511 million is impressive, with this driven by the increase
in exports of $940 million to rest of East Africa (Kenya). These exports are partially
replacing South African exports to both the EU ($155.8 million) and rest of non-Africa
excluding the EU ($291.4 million), although there are also increases from South Africa to
Egypt, Ethiopia and Uganda as shown to be between $55.5 million and $92.2 million. Note
also that South African exports to the rest of SACU decline by $45 million. Along the
second row we see that South African imports from the world in total (right-hand column)
increase by $754 million or more than the increase in exports of $511 million. This increase
is driven by more imports from outside Africa, although Egypt increases its exports to
South Africa by $40.5 million. In the right-hand column we see that imports into the rest of
east Africa increase by $185 million (as a result of more competitive imports from
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
South Africa replacing previous non-African imports) while the reduced exports from
South Africa to non-Africa (including the EU) reduce total exports to non-Africa. The overall
effect is trade creating, as global trade in manufactures increases by $619 million as seen in
the very bottom right-hand entry. The other cells of note concern (a) Mozambique, where
imports from the EU decline by $58 million and total imports decline by $68 million, and (b)
the rest of East Africa where global exports increase by $196 million at the same time as
imports increase by a lesser $185 million following the large substitution effects of
South Africa replacing non-African imports.
Section 7: Decomposition of the welfare changes
Output from the GTAP model allows us to examine the relative contribution to welfare
changes to each country/region in the model from both (a) every other country/region,
including own liberalisation as shown in Table 10, and (b) each commodity sector as shown
in Table 11.
Table 10: Welfare decomposition by country, 2007 $ million
zaf
bwa
xsc
egy
xac
eth
moz
xec
RAfrica
nonAf
World
zaf
-3
0
0
39
-2
3
0
36
9
-131
-47
bwa
-3
1
0
2
0
0
0
2
1
-5
-2
xsc
-3
0
0
1
0
1
0
5
0
-6
-2
egy
58
-1
0
-23
0
0
0
0
2
-26
11
xnf
12
0
1
-4
0
0
0
-1
1
-6
3
xac
-3
0
-1
-4
-13
0
0
47
5
-27
3
eth
58
-1
0
-2
0
-8
0
-4
2
-27
17
mdg
0
0
0
0
0
0
0
0
0
0
0
mwi
0
0
0
0
0
0
0
0
0
0
0
mus
0
0
0
0
0
0
0
0
0
0
0
moz
-9
0
0
3
0
1
6
27
-1
-42
-16
tza
-2
0
0
23
0
1
0
-2
-1
-36
-18
uga
101
-1
-5
-2
-1
0
0
-24
-3
-38
29
zmb
0
0
0
0
0
0
0
0
0
0
0
zwe
0
0
0
0
0
0
0
0
0
0
0
xec
1,115
-15
-78
-61
0
-13
52
-96
-40
-599
281
Total
1,321
-16
-84
-29
-16
-14
57
-10
-24
-943
258
Source: GTAP output. See Table 1 for the country codes used
Reading the table for the welfare contributions by country we find that for South Africa (zaf)
the overwhelming contribution is from tariff reductions into xec (the rest of East Africa
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
aggregation that contains Kenya, $1,115m), with additional contributions from Uganda,
Ethiopia and Egypt. The big loser is non-Africa, with its losses concentrated in the rest of
East Africa as South Africa moves in there, and to a lesser extent in South Africa itself.
Overall losses to both Botswana and the rest of SACU are driven by losses (from being
substituted) in the rest of East Africa, while several countries lose from own liberalisation as
witnessed by the negatives in own cells such as Egypt ($3 million), rest of southern Africa
(xac, $13 million), and rest of East Africa ($96 million). This is unusual in that generally a
country gains from own liberalisation
Table 11 reinforces that the gains are from sugar and manufacturing, with South Africa
capturing these at the expense of non-African countries. Botswana and the rest of SACU
both lose through manufacturing and, for the rest of SACU, through sugar in particular (if
we compare the rest of SACU for sugar in Table 11 and for rest of East Africa in Table 8 we
see that the displacement of sugar from the rest of SACU by South Africa in the rest of the
east African market is hurting the rest of SACU).
Egypt loses through sugar, while
Mozambiques (better exports) and the rest of east Africa (cheaper imports) both gain from
sugar while non-Africa loses from sugar. There are only minor changes to agriculture other
than sugar. For manufacturing, the gains for South Africa at the expense of non-Africa are
reinforced, while Egypt gains and the rest of East Africa loses from manufacturing. Services
are neutral, but there are some individual gains and overall losses from resources as
production shifts around.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Table 11: Welfare decomposition by commodity, 2007 $ million
zaf
vegetable, fruit
other crops
sugar
other foods
beverages,
tobac
Total:
agriculture
resources
manufacturing
services
Total
bwa
xsc
egy
xac
eth
moz
xec
RAfrica
nonAfr
World
14.1
-0.3
-0.4
1.5
-0.7
0.0
-0.3
-0.7
-0.8
-12
1
2.0
0.0
-0.4
0.1
2.9
-0.1
0.0
9.1
10.5
-22
2
325.8
-4.2
-72.0
-51.2
1.7
-8.2
57.4
67.2
-20.9
-138
158
31.5
-0.5
-0.4
2.7
-2.9
-0.9
-0.3
-12.5
-2.4
1
16
28.2
-0.3
-0.2
-11.6
0.1
-0.4
-0.2
-13.9
-1.5
13
13
416.1
-5.5
-73.6
-53.6
1.2
-9.4
56.2
49.6
-16.7
-177
187
17.9
-0.2
0.2
-11.9
7.3
0.7
-0.5
25.7
2.1
-69
-28
887.0
-10.8
-10.4
36.6
-24.5
-5.5
1.3
-84.9
6.7
-697
99
0.3
0.0
0.0
0.1
0.1
0.0
0.1
0.0
0.0
-1
0
1,321
-16
-84
-29
-16
-14
57
-10
-8
-943
258
Source: GTAP output
Section 8: Labour market changes
In this model, the labour market closure is one whereby skilled labour is fixed, but unskilled
labour is a function of the unemployment rate. In a developed country with generally (but
not always) low unemployment rates we would expect that the benefits to unskilled labour
flow through in the form of higher real wages. In a country that has a high unemployment
rate (South Africa’s is an official 25.5 percent but the higher unofficial rate is possibly the
world’s highest among countries at a similar level of development) we would hope that the
changes are reflected in increased employment. Table 12 shows the outcome for
employment, with the closure used in this paper shown in the middle in option (B). The
employment and real wage outcomes are both positive for South Africa: employment
increases by 0.14 percent and real wages by a greater 0.47 percent. At the same time, the
Consumer Price Index (CPI) increases by 0.264 percent, meaning that unskilled workers
would be able to buy more with their wages. Getting unskilled labour into employment is a
real priority for South Africa, and Table 11 provides more confirmation that the tripartite
FTA supports this objective.
The results are effectively neutral for Botswana with
impeceptable increases in employment and wages, but for the rest of SACU results are not
beneficial at all as both employment and wages fall, with wages falling by nearly half a percent
in real terms.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Essentially, South Africa becomes marginally more efficient with better capital utilisation in
response to enhanced exports to non-SADC tripartite countries in both manufacturing and
agriculture (sugar in particular). This increased demand for South African exports leads to an
appreciation of the real exchange rate in South Africa, resulting in a terms of trade gain. The
South African economy gains from this appreciation of the real exchange rate (0.335%), as
the value of total income (sum of factor income and indirect tax receipts) increases by
0.600 percent, while prices ‘only’ increase by 0.270. This translates into a raise in EV of
US$1,321 million when measured in fixed prices.
Table 12: GDP, CPI and employment changes (%)
South Africa
Welfare
US$
Real
million
GDP%
1,046
0.220
(A)
GTAP model
% change
CPI
%
0.279
1,321
0.291
0.264
3,082
0.746
0.171
(B)
(C)
Closure
Botswana
0.00
0.02
Rest
SACU
0.00
-0.49
fixed
Employment
Real wage
RSA
0.00
0.54
U
(1-U)
Employment
Real wage
0.14
0.47
0.00
0.02
-0.12
-0.42
fixed
Employment
Real wage
1.03
0.00
0.04
0.00
-0.84
0.00
Source: GTAP results
As emphasised above, the unskilled labour market closure used in of our version of the
model is a function of a labour supply elasticity which is calculated from initial unemployment
rates.10 There are, of course, two extreme alternatives to this. The first is option (C) above
which makes wages fixed and forces adjustments in the labour market to come through
changes to the employment rate. Here the results are that the welfare gains to South Africa
increase by nearly two and a half times to US$3,082 million or 0.746 percent of GDP as a
result of an increase of 1.03 percent in the workforce. The second alternative is to fix the
number of persons employed – this could be thought of as the COSATU position, as any
adjustments come through as changes to their wage rates (option (A) above). Here the
result of $1,046 million following the increase of 0.54 in the real wage rate is more or less a
20 percent decline in welfare relative to the scenario used . Hence, the policy of promoting
10
For a more detailed explanation see Annex B: Derivation of the labour market assumptions in Sandrey et al.
(2007: 136).
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
employment rather than increasing the wages of those already in employment is clearly a
superior option for South Africa to pursue and a tripartite FTA would make a contribution
to this objective.
Section 9: Tariff reductions and the implications for the SACU revenue pool
Sandrey (2007) explores the implications of SACU trade agreements with respect to changes
in tariff revenues, and highlights that there are large welfare transfers to BLNS which arise
from these countries obtaining revenues over and above what they would have collected at
their own borders if they were not part of SACU. This has been forcefully reinforced by the
recent study by the CIE (2010), as these revenue transfers represent a direct aid support
payment from South Africa to BLNS.11 The objective of this section is to explore the
implications for BLNS countries in particular of the tariff revenue changes that would result
from the FTA.
There are two ways in which tariff revenues would be reduced through an FTA. The first is
the obvious one in that with an FTA the vast majority of merchandise goods from the FTA
partner(s) would now enter SACU duty-free. The second relates to trade diversion. This
takes place when trade is deflected away from previous sources that were paying duty to the
sources which now benefit from duty-free access under the FTA. The overall tariff revenue
effect of an FTA has a much larger impact on BLNS than the direct production and trade
impacts, and of interest is what happens in South Africa rather than what happens in the
individual BLNS countries. The way in which the revenue is distributed is an important and
sensitive issue in SACU. Revenues are effectively collected by South Africa and then
distributed to the BLNS according to a formula that bears no resemblance to the way in
which the revenues are collected. Therefore, given the welfare transfer effects, South
Africa's welfare will be slightly lower than that given in this chapter and the BLNS countries’
welfare will be slightly higher.
11
The levels of these grants are confirmed by the data in IMF (2009: Table SA20), which reports Official Grants
to Lesotho of 37.5 and 32.0 percent of GDP for 2007 and 2008 respectively. The comparable data for
Swaziland was 20.8 and 20.5 percent respectively, while South Africa’s was -1.0 and -1.1 percent for the two
years.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
The tripartite FTA tariff revenue into the pool actually increase marginally as South African
manufacturing imports increase. Table 13 shows this data, expressed in US dollars (millions)
and not in rands.
Table 13: Revenue changes following tripartite FTAs (US$ millions, 2007)
Change in tariff revenue ($m)
South Africa
Botswana
Primary Agriculture
-4
0
Secondary Agriculture
2
0
Resources
0
0
Manufacturing
51
1
Total
49
1
Rest SACU
-1
0
0
0
-1
Total
SACU
-5
2
0
52
49
Source: GTAP results
The table shows that gains to the revenue pool from a tripartite FTA are US$49 million.
Almost all of this derives from the manufacturing sector, and almost all the gains are from
South Africa as there are substantial increases in South African manufacturing imports from
mostly non-tripartite countries. This result is in contrast to the tariff revenue losses from a
SACU-Mercosur and the SACU-China FTA as outlined in Sandrey et al. (2010).
Section 10
‘Proudly Africa’ – modelled as giving preference to African goods
Negotiating and implementing an FTA is a complex and drawn-out process, and this is
especially the case in Africa where many nations have limited technical capacity to undertake
these negotiations and inadequate institutional frameworks to base agreements upon – to
say nothing of political obstacles. Recognising this we explore an alternative option called
“Proudly Africa” that we feel has both political attraction and an ease of implementation
throughout the continent to mutually assist fellow African countries. It can be thought of as
simply a continent-wide campaign to “Buy Africa”. Note that we have extended Proudly
Africa to all African countries and not just tripartite Africa, and also note that our initial
starting point for Proudly Africa is from our baseline that contains 2007 trade data and it is
set in motion after we have the tripartite FTA in operation. However, it makes little
difference whether there is a tripartite FTA or not, as it is not driven by tariff preferences at
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
all but merely a cooperative understanding to increase the share of trade sourced from
within Africa.
Annex 2 contains a discussion on how ‘Proudly Africa’ operates and includes both
mathematical and diagrammatical descriptions. Essentially, we have added a preference shift
variable into the model using the well known Armington equations where the same or
similar goods can be sourced from different exporters. For African countries we increase
the amount of intra-African trade by introducing an equation that tells the GTAP model that
for African countries it is to import ten percent more in total from African sources in each
commodity.
It then calculates endogenously (within the model) the value of the
‘nonafrPREF’ variable which reduces the amount of exports from non African region into
Africa accordingly so that the new total demand for imports in each African country still
remains equal to the old total amount of exports being exported to the country in question
from all the different sources (African and non African countries). It is not an FTA or tariff
preference system as there are no alterations of tariffs. In effect, we are moving demand
curves outwards for African commodities and inwards for non-African commodities in each
African country. This is a simple simulation from the same baseline as used in the main FTA
simulation, and as always there are some assumptions:
•
There is no quality difference in goods produced in Africa compared to the rest of the
world;
•
Africa can produce all the extra goods in question and there are no capacity
constraints such as patents or lack of technology or knowhow (skilled labour);
•
The rest of the world is still willing to increase its investment in Africa (therefore
Africa’s increased capital stock);
•
We have not modelled payment of rents to foreign capital owners abroad which
reduces African welfare gains, nor have we considered that transfers of income to
Africa in the form of development aid from developed countries could be reduced if
Africa embarks on a ‘Buy African’ campaign. This would, of course, be somewhat
ironical and hypocritical as foreign direct aid is often given to Africa and others with
‘buy donor country’ strings attached; and
•
We have added filters so that ‘Proudly Africa’ is only activated when (a) the nonAfrican import share for the commodity into the country under examination is larger
than 10 percent (‘Proudly Africa’ is already in operation) and (b) similarly, total imports
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
are at least $10 as the model has problems converging on very small or meaningless
numbers.
We would hypothesise that (1) the losers would be non-African sources, (2) that while
Africa may gain in total these gains may not be evenly distributed within Africa, and (3) that
world welfare will reduce as we introduce some African inefficiencies. Note also that we
have excluded the non-traded agricultural sectors of paddy (non-processed) rice, cane and
beet production and raw milk (for processed dairy) from changes.
The welfare results are given in Table 14. Gains to Africa are a significant $3,871 million.
These gains accrue to all African countries/regions except Botswana, where a loss of
$75 million occurs, and Angola/DRC where an even larger $195 million dollar loss occurs.
Again, the big winner is the continent’s powerhouse, South Africa, although this time we are
seeing big gains also from non-tripartite countries such as rest of West Africa ($821 m) and
Nigeria ($207m). As hypothesised, (a) all non-African countries lose and (b) world welfare
declines by $2,088 million as Africa marginally turns away from the global low-cost
producers. This is reflected in the composition of welfare changes, as the now sub-optimal
allocation of global capital makes up most of both the global losses on the one hand and
African gains on the other as it shifts to Africa. African gains derive mostly (around half)
from better capital allocation with both allocative efficiency and terms of trade making up a
large portion of the rest, although there are pleasing but more modest gains to labour. Most
of the loss to the non-African countries and the world as a whole derives from an inferior
allocation of global capital in favour of Africa. Thus, ‘Proudly Africa’ is favourable for Africa
but not the world.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Table 14: Welfare results from ‘Proudly Africa”, 2007 $ million at 2020
South Africa
Botswana
Rest SACU
Egypt
Morocco
Tunisia
Libya/Algeria
Nigeria
Senegal
Rest West Africa
Rest Central Africa
Angola/DRC
Ethiopia
Madagascar
Malawi
Mauritius
Mozambique
Tanzania
Uganda
Zambia
Zimbabwe
Rest East Africa
Total Africa
All non-Africa
Total
Total $m
1,898
-75
43
240
90
108
43
207
144
821
43
-195
27
2
-2
18
24
129
97
29
0
336
4,028
-6,116
-2,088
Allocative
Efficiency
448
-44
-32
24
16
27
25
73
33
236
19
-83
-1
-2
-11
-1
-26
2
0
-17
-22
24
687
-1,266
-579
Labour
192
3
13
17
7
5
9
28
5
31
8
-5
3
0
2
1
6
5
21
5
0
43
398
-208
191
Capital
888
-15
40
93
30
42
12
40
76
465
12
-92
10
0
4
9
29
83
50
19
11
176
1,983
-3,681
-1,698
Term of
Trade
370
-18
23
107
37
34
-2
65
30
90
4
-15
14
4
4
8
15
39
26
22
11
93
960
-962
-2
Source: GTAP output
Following on from the welfare results Table 15 indicates the impacts on production in
SACU. The data for changes to output, exports and imports and the associated output price
increases are shown in percentage form. Agricultural output or farm prices rise by 0.4 to
0.6 percent for almost all sectors in South Africa, and this result is even better for rest of
SACU (but not for Botswana, which is not shown). For the main secondary agricultural
products, both exports and imports generally increase for SACU, although in the case of
Botswana (not shown) these may be off low bases. Manufacturing output rises by 0.5 percent
in South Africa and 0.6 percent in the rest of SACU, while the increase in exports and
imports of between 1.4 and 2.5 percent and the output value increases of 0.5 and
0.6 percent are also similar for both.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Table 15: Percentages changes to output, trade and prices with ‘Proudly Africa’
wheat
other grain
Vegetables, fruit
oil seeds
other crop
live animals
other animal
products
wool
beef/mutton
other meat
vegetable oils
dairy
sugar
other food
beverages,
tobacco
manufacturing
services
South Africa % change in
output exports imports prices
-0.2
3.3
1.2
0.5
0.4
2.3
0.7
0.6
-0.1
-0.3
0.5
0.5
0.5
1.3
0.9
0.6
0.6
1.9
1.7
0.7
0.4
0.9
-0.7
0.6
Rest SACU % change in
output exports imports prices
-0.6
-6.7
3.6
1.0
0.3
4.1
0.9
1.3
0.8
1.7
0.7
1.4
0.8
4.5
1.5
1.5
0.3
0.6
3.5
1.6
2.3
7.9
0.7
1.5
0.2
-1.4
0.3
0.3
0.5
0.2
0.6
0.5
-0.9
-2.4
3.3
2.8
4.0
1.1
1.3
2.0
0.9
1.0
1.7
2.3
1.2
1.9
1.5
1.2
0.5
0.3
0.5
0.5
0.4
0.5
0.5
0.5
0.2
-0.2
-1.2
-0.3
0.6
0.0
0.9
1.1
2.3
-11.1
-2.1
-0.3
4.8
0.8
1.0
2.6
0.6
6.1
3.1
1.6
1.2
3.2
0.8
0.6
1.0
1.1
0.9
0.8
0.7
0.6
0.7
0.6
0.8
0.5
0.4
3.6
2.0
-1.3
0.9
1.4
1.4
0.5
0.4
0.5
2.1
0.6
0.2
7.4
2.5
-1.4
0.8
1.4
1.1
0.7
0.5
0.5
Source: GTAP output
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
The changes to production in SACU are shown in Table 16, expressed in dollar values
rather than in the percentages above. As expected, South Africa and South African
manufacturing dominate the merchandise output increases. There are also solid gains to
manufacturing in both the rest of SACU and Botswana and in agriculture in both South
Africa and the rest of SACU. The big gains to sugar that we saw earlier from the tripartite
FTA do not feature, and wool production actually declines. Services output changes as
resources move around the economy and a small change makes a large difference given the
dominance of services in the modern economy.
Table 16: Changes to SACU production with ‘Proudly Africa’, $ million
other grain
vegetables, fruit
oil seeds
cane production
other crop
animal
other animal
products
wool
beef/mutton
other meat
vegetable oils
dairy
sugar
other processed food
beverage, tobacco
Total: agriculture
resources
manufacturing
services
Total
South Africa
$m
%
25
0.4
17
-0.1
4
0.5
7
0.3
48
0.6
34
0.4
33
-44
49
36
19
33
32
195
187
700
-6
2,201
4,882
7,778
0.2
-1.4
0.3
0.3
0.5
0.2
0.6
0.5
0.8
6
-0.1
0.5
0.4
Rest SACU
$m
%
4
0.3
5
0.8
1
0.8
4
0.7
8
0.3
16
2.3
3
1
-2
1
2
1
12
29
26
113
7
107
186
412
0.2
-0.2
-1.2
-0.3
0.6
0.0
0.9
1.1
2.1
8
-0.1
0.6
0.2
Botswana
$m
%
0
-0.1
0
0.2
0
2.1
0
-0.1
0
0.5
1
-0.1
2
0
1
0
0
1
0
3
1
10
-3
37
-14
29
1.1
0.1
-0.1
-0.2
3.9
0.0
1.2
0.8
-0.1
18
-0.1
2.1
-0.3
SACU
$m
30
22
5
11
56
52
37
-43
48
38
21
35
44
227
213
822
-2
2,345
5,054
8,219
Source: GTAP output
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Table 17 shows the results for employment. In the centre of the table we have the standard
GTAP labour closure of real wage changes calculated as a function of unemployment. Here,
with welfare gains of $1,898 million or 0.42 percent of GDP for South Africa we see that
employment increases by 0.216 percent and real wages by 0.728 percent. Again, the lower
part of the table shows that overwhelmingly the priority for South Africa must be
employment, as obtaining jobs more than doubles the welfare increase to $4.7 billion.
Conversely, the ‘Cosatu’ option of current workers capturing all the gains reduces the
welfare gain relative to our standard closure. ‘Proudly Africa’ is favourable to South Africa
and coupled with sound employment policies it can be even better.
Table 17: Changes to employment from ‘Proudly Africa’
South Africa
EV
GDP
US$
million
%
1,471
0.31
CPI
%
0.45 fixed
1,898
0.42
0.42
4,674
1.13
0.28
U
(1-U)
fixed
Employment
Real wage
Employment
Real wage
Employment
Real wage
South
Africa
0.000
0.842
0.216
0.728
1.620
0.000
Botswana
0.000
0.292
0.055
0.260
0.486
0.000
Rest
SACU
0.000
0.815
0.197
0.706
1.476
0.000
Source: GTAP output
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Always of concern with respect to trade agreements is the SACU tariff pool revenue, and
here Table 18 shows that there are virtually no changes from ‘Proudly Africa’. Overall,
there is a small increase of $10 million, with the increase in South Africa offset by declines of
$31 million in the rest of SACU and $35 million in Botswana.12
Table 18: Change in SACU tariff pool revenue, 2007 $ million
Revenue
Pool
Primary agriculture
Secondary
Resources
Manufacturing
Total
0
2
0
8
10
South Africa
0
4
0
72
76
Botswana
0
0
0
-35
-35
Rest SACU
0
-2
0
-29
-31
Source: GTAP output
Conclusions and Policy implications from ‘Proudly Africa’
This is an Africa-wide simulation, and ‘Proudly Africa’ presents an interesting study which
shows that large gains can be made in the absence of a formal negotiation that undoubtedly
would require difficult and protracted negotiations. It is simply a non-binding agreement to
encourage the purchase of African merchandise goods, and is modelled by increasing imports
of African products by ten percent as applicable. As expected, the big gainer is South Africa,
as a considerable percentage of the continent’s industrial capacity is in South Africa, and
most of the ‘Proudly Africa’ gains are from manufacturing. It clearly shows that the way
forward for Africa is largely in African hands, and insight into why ‘Proudly Africa’ is
potentially an excellent policy can be gained by turning to Table 1 of this chapter which
shows estimates of GDP growth through to 2020. Here the World Bank reinforces the
current slogan that this century may belong to Africa. ‘Proudly Africa’ capitalises on this. Its
beauty lies in its simplicity – and of course not all countries are obliged to join as long as the
majority of larger economies participate. In the final analysis, the welfare gains are significant
and relatively evenly spread.
12
Note that these declines for Botswana and the rest of SACU are not relative to the current revenue pool
sharing formula but rather an overall assessment. Therefore the crucial column is the one that relates to
‘Revenue Pool’.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
References
Badri, N.G. and Walmsley, T.L. (eds.). 2008. Global Trade, Assistance, and Production: The GTAP
7 Data Base. West Lafayette: Center for Global Trade Analysis, Purdue University.
Bouët, A. et al. 2005. A consistent, ad-valorem equivalent measure of applied protection across the
world: The MAcMap-HS6 database. CEPII Working Paper No. 2004 – 22 December (updated
September 2005).
[Online]. Available: http://www.cepii.fr/anglaisgraph/workpap/pdf/2004/wp04-22.pdf.
CIE. 2010. Study on the Review of the Revenue Sharing Arrangement for SAC. Paper prepared for
the SACU Secretariat. Sydney and Canberra: Centre for International Economics.
IMF. 2009. Regional Economic Outlook: Sub-Saharan Africa. Washington: International Monetary
Fund.
Nielsen, C. and Anderson, K. 2000. GMOs, Trade Policy, and Welfare in Rich and Poor Countries.
Paper presented at the World Bank Workshop on Standards, Regulation and Trade,
Washington, D.C., 27 April 2000.
Sandrey, R. 2007. Living in the shadow of the mountain. In Bösl, A. et al. (eds.), Monitoring
Regional Integration in Southern Africa Yearbook Volume 7. Stellenbosch: tralac. [Online].
Available:
http://www.nepru.org.na/fileadmin/download/books/MRIY_07_total_book.pdf.
Sandrey, R. et al. 2010. South Africa’s way ahead: Shall we samba?. Stellenbosch: tralac.
Walmsley, T.L. 2006. A Baseline Scenario for Dynamic GTAP Model. Revised March 2006 for
GTAP 6 Database. West Lafayette: Center for Global Trade Analysis, Purdue University.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Annex 1
Country
SADC
EAC
COMESA
Angola
X
Botswana
X
DRC
X
Lesotho
X
Madagascar
X
X
Malawi
X
X
Mauritius
X
X
Mozambique
X
Namibia
X
Seychelles
X
South Africa
X
Swaziland
X
Tanzania
X
Zambia
X
X
Zimbabwe
X
X
X
X
X
Burundi
X
X
Kenya
X
X
Rwanda
X
X
Uganda
X
X
Comoros
X
Djibouti
X
Egypt
X
Eritria
X
Libya
X
Sudan (now 2 countries)
X
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Annex 2: 'Proudly African'.
Incorporating a preference shift in favour of African imports into the GTAP
model.
The scenario ‘Proudly African’ analyses a preference shift in African countries in favour of
sourcing imports from fellow African countries at the expense of non- African countries. In
the GTAP model a preference shift variable is added to the behavioural relations of a
country’s demand for disaggregated imported commodities by source.13 The GTAP model
has a set of equations that describe an individual country’s demand for imported goods. This
set of equations is given below, for an African importing country (s):
(1) qxs(i,a,s) = qim(i,s) - ESUBM(i) * [pms(i,a,s) - pim(i,s)]
+ IF (ROWSHR(i,s)> 0.1 and VIMS(i,a,s) >0.00001, AFRpref(i,s))
(2) qxs(i,n,s) = qim(i,s) - ESUBM(i) * [pms(i,n,s) - pim(i,s)]
+ IF (ROWSHR(i,s)> 0.1 and VIMS(i,n,s) >0.00001, nonAFRApref(i,n,s))
(3) pim(i,s) = sum(k,REG, MSHRS(i,k,s) * pms(i,k,s)).
An African country s imports commodity i from countries a and n. This commodity can
either be imported from an African country a or a non-African country n. Equations (1) and
(2) determine the demand for imported commodities qxs(i,a,s) from other African countries
and qxs(i,n,s) from non-African countries respectively. Both these questions are determined
by the overall demand for imports qim(i,s) irrespective of the country of origin. This is the
first term of the right-hand side of both equations (1) and (2) and may be termed the
expansion effect. There is also a substitution effect – then second term in equation (1) and
13 The modelling approach used in the ‘Proudly Africa’ scenario builds on the method used by Nielsenand
Anderson (2000).
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
(2) – which determines the sourcing of imports between different countries. This effect is a
combination of the elasticity of substitution ESUBM(i) for individual commodities and the
difference between the imported price pms(i,a,s) for African commodities (pms(i,n,s) for nonAfrican commodities) and the composite input price pim(i,s). The composite price pim(i,s) is
the average import price (weighted by market share in country s) of commodity i imported
into country s from all other countries in the world. Note that the equations in the GTAP
model – and thereby also equations (1) (2) and (3) above – describe the relative changes.
That means that equation (1) and (2) describe the relative change in demand for imports as a
function of relative change in the overall demand for imports (expansion effect) and the
difference between the relative price changes (substitution effect).
Compared to the original GTAP model, equations (1) and (2) include preference shift
variables AFRpref(i,s) and nonAFRApref(i,n,s). In the ‘Proudly Africa’ scenario a value of
AFRpref(i,s) = 10 therefore represents a 10 percent increase in demand for imports i in the
African country s from other African countries (equation (1)). But a 10 percent increase in
demand for African imports is not equal to a 10 percent decrease in the demand for nonAfrican imports, because the share of imports from African and no- African countries is
typically different. Therefore equation (4) is added to the model which calculates the import
share weighted negative non-African preference shift for non-African imports nonAFRpref
(i,n,s). Finally, in equation (1) and (2) an IF statement is included so that the non-African
import shares in a given country have to be greater than 10 percent and the import value
has to be larger than $10 before the preference shift is activated. This is done so that the
model does not have problems in converging if the import values are very small and, of
course, if the non-African import share is already 10 percent or lower, then the sector is
already ‘Proudly Africa’.
(4) nonAFRpref (i,n,s) = - sum(a, African, VIMS(i,a,s))*AFRpref (i,s)
/ sum(n, non-African, VIMS(i,n,s))
Taking Tanzania’s changes in sourcing of manufacturing imports from South Africa and the
EU as a case example, Figure 1 illustrates the effect of a 10 percent preference shift in favour
of African imports in Tanzania at the expense of non-African countries.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Figure 1(a) illustrates the 10 percent preference shift in the demand for South African
manufacturing imports in Tanzania by the movement of the demand curve D out to D1.
Initially, Tanzania imported q amount of manufacturing from South Africa but after the
preference shift is now asking South Africa to export the quantity q1 to Tanzania, a !0
percent increase (expansion effect). This increased demand for manufacturing results in an
expansion of the quantity of manufacturing produced in South Africa but at the same time
the price of manufacturing increases as the industry has to compete with other sectors of
the economy for increased amounts of endowments (labour and capital) needed to expand
production. The increased cost of production pushes up the price of manufacturing P
produced in South Africa which in turn dampens the import demand in Tanzania to q2
(substitution effect). The sum of the expansion and substitution effect results in an
8.7 percent increase in the amount of South African manufacturing being exported to
Tanzania.
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Chapter 4 – The tripartite Free Trade Agreement: A computer analysis of the impacts
Figure 1. Proudly Africa, increasing African intra-trade.
(a) African country
(b) Non-African
Price
Price
D1
D1
p
p
D
D
q
q2 q1
Quantity
q1 q2 q
Quantity
In figure 1 (b) we can see the opposite situation. The demand curve is contracting inwards
reducing the amount of imports from non-African countries. In the case of manufacturing
imports into Tanzania, equation (4) calculates that a 10 percent increase in the amount of
African imports is equivalent to a 2.7 percent negative preference shift in the demand curve
D for non-African countries, moving it down to D1. This initially reduces the amount of nonAfrican imports from q to q1, but the reduction in the demand for non-African
manufacturing reduces its price P relative to the average import price increasing demand
from q1 to q2. In the case of the EU, manufacturing exports to Tanzania are reduced by
1.13 percent due to the combined effects of the negative preference shift and the positive
substitution effects.
Initially, African countries accounted for 23.0 percent of manufacturing imports entering
Tanzania which increases to 24.9% after the ‘Proudly African’ 10 percent preference shift is
implemented in favour of African produced imports. For the African continent as a whole,
the ‘Proudly African’ scenario increases the quantity of intra-African trade by 8.3 percent,
which falls well in line with the positive 10 percent preference shift we have imposed.14 In
comparison the tripartite FTA scenario increases intra-African trade by 2.2 percent.
14
The substitution effects of price changes together with the requirement that the initial non-African trade
share have to be larger than 10 percent before the preference shift is activated; this explains why intra-trade in
Africa does not increase by 10 percent.
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Chapter 5
Tripartite Agribusiness Opportunities for South Africa
Ron Sandrey
1.
Introduction
The modern focus of trade is to move past commodity trade and try to search out more
value-added products that offer opportunities for producers, processors and traders. The
objective of this chapter is to start with an examination of the implications of regional
integration for South Africa (RSA). This has been done using the Food and Agricultural
Organisation (FAO) trade database to look at what the tripartite region is importing and
what South Africa is exporting in order to get a preliminary idea of where opportunities
exist. The emphasis is on opportunities in the more distant East African Community (EAC)
and the Common Market for Eastern and Southern Africa (COMESA) markets because the
Southern African Customs Union (SACU) market is already integrated and the Southern
African Development Community (SADC) is hopefully moving in that direction with its own
Free Trade Agreement (FTA). Conscious that the FAO database provides trade data only
for agricultural outputs, World Trade Atlas data has been used for an indication of where
opportunities may lie in the wider agribusiness input sectors.
2.
Setting the scene
There is a need to look at all aspects of trade opportunities for South Africa, and in
particular to examine exports that may further enhance the economy through value-adding
throughout the supply chain. However, a focus on further processed goods only is too
narrow, as, for example, in the case of fruit and vegetables the best overall return is from
quality fresh produce. Notwithstanding these possible exceptions the generalisation holds:
further value-adding through the agribusiness chain is better for the economy.
The analysis is based on the FAOSTAT database, which lists 484 agricultural product lines
with the latest complete data from 2007. In the initial round of analysis, South African
imports and exports and tripartite imports for all these product lines were examined. The
South African export data is to assess potential export capacity in each line, the
South African import data is to assess both the extent to which South Africa is influencing
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
regional imports and how these imports relate to South African exports, while the regional
import data is to assess where the opportunities may be for South Africa if the country does
indeed have export capacity in these lines. A major limitation of the analysis is that the FAO
does not report bilateral trade flows.
Furthermore, using the FAO data means that the analysis is restricted to agribusiness
opportunities for direct agricultural outputs, at the expense of a wider definition of
agribusiness products that would include general farm inputs such as fertilisers, pesticides,
machinery and a host of others that may also be considered agribusiness. To make up for
this omission, a preliminary examination of a selection of these inputs based upon
South African exports both globally and to Africa is included at the end of the chapter.
It is, of course, also worth noting that South Africa can and does import and export specific
products simultaneously for a number of reasons: as a gateway to Southern Africa, this
country re-exports products; furthermore, as a relatively large country with a highly diverse
climate, one part of the country could be exporting while another part is importing (e.g.
maize grown in the Free State is exported while poultry farmers near Durban and
Cape Town import from elsewhere) – this example could be both because of the long road
distances in South Africa and because of counter-seasonal production in the Northern
Hemisphere. Modern trade theory recognises the presence of simultaneously exporting and
importing similar products and introduces the concept of intra-industry trade to both
explain and measure such trade. Based on the FAO data, the South African intra-industry
trade index is 0.227, as measured by the Grubel-Lloyd index. This is relatively high and
suggests significant levels of trade in similar products.
In order to calculate regional shares, countries were grouped as follows:
•
SACU
•
The rest of SADC (excluding Tanzania) (RSADC);
•
The East Africa Community, which includes Tanzania;
•
The COMESA region, which excludes any country already in SACU, SADC or EAC;
•
The rest of Africa (RAFRICA)
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
The emphasis in the analysis is on trade with the EAC, COMESA and the rest of Africa, as
both SACU and SADC represent relatively free trading regimes.1
3.
The first cut
In order to make the analysis tractable it is necessary to narrow the list down. South Africa
exported at least $100,000 worth in 249 of the 484 product lines listed by the FAO and
these 249 lines accounted for South African exports of $8,145 million or some 99.9 percent
of South African agricultural exports in 2007. To demonstrate that intra-industry trade or
simultaneous export and import of like products is an issue for South Africa, these same
lines also accounted for imports of $8,578 million or 97.2 percent of total imports in 2007.
Despite the opportunities for intra-industry trade, South Africa most likely does not have
unrealised export potential in products where it is a significant importer, as measured by
both (a) the relative levels of South African imports to exports in any given line and (b) the
dominance of South Africa as an importer of this line regionally. This latter point is
important, as we can assume that if South Africa is a major importer by value and if at the
same time these imports account for a large percentage of African imports, it is difficult to
argue that ‘real’ export opportunities exist in that line. As a result, all lines where
South Africa is responsible for at least 40 percent of the imports for the entire
tripartite region and South African imports are at least three times the level of
South African exports, were excluded. This led to the deletion of another 102 lines.
The larger export lines excluded are game meats, grapefruit, distilled beverages, other nuts,
fruit juices, plums and hair, fine carded.2 Consequently 146 lines remain where there is some
export potential.
4.
The detailed analysis
Examining these 146 lines, we find that South African exports were $3,477 million but
imports were still $2,828 million. These are lines where South Africa has demonstrated
export capacity on the one hand but may well also have considerable imports at the same
time – although these South African imports are less than 40 percent of the regional total –
1
SADC and EAC countries that are also members of COMESA are therefore excluded, but Tanzania has been
included as part of the EAC and not SADC.
2
This gives us an aggregation problem. For example, just because South Africa imports large quantities of
whiskey does not mean that it has no potential in brandy exports! Sometimes the ‘devil is in the detail’.
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
otherwise they would not have progressed to this point. It would seem logical to assess
these lines as having agribusiness potential despite also being major import lines.
For the purpose of this analysis, recall that SADC is defined as excluding Tanzania; this is
because Tanzania is regarded as being firmly in the EAC ‘camp’ and may at some stage have
to break with SADC. Other countries such as the Democratic Republic of Congo (DRC),
which nominally belongs to both SADC and COMESA, and Swaziland, which belongs to both
SACU/SADC and COMESA, have been placed in SADC and SACU respectively. The real
policy issue that arises is that if a SADC FTA is made operational before a tripartite
agreement can be realistically proposed, then South Africa is unlikely to gain market
advantages over and above the SADC FTA for these products. Conversely, if SADC does
not progress to a meaningful FTA it is inconceivable that the tripartite agreement would
eventuate as proposed. Furthermore, the BLNS (Botswana, Lesotho, Namibia and Swaziland)
countries are not considered as potential sources of new agribusiness opportunities. Given
the close relationships that already exist within SACU, it is unlikely that wider integration
can offer anything extra. Hence, the focus is really on assessing market opportunities for
South Africa in the relatively distant EAC and COMESA markets.
We will now sequentially examine these 146 lines in more detail to make the analysis
tractable.
Group 1
The top 12 of these 146 lines by import value are shown below in Table 1. This and
subsequent tables should be read as follows:
•
Column 1 is the FAO product definition;
•
Columns 2 and 3 show South African exports and imports in 2007 respectively
expressed in $1000s;
•
Column 4 shows South Africa’s share of tripartite imports, while column 5 shows the
SACU share, a share that includes South Africa’s;
•
Columns 6, 7 and 8 show the share of tripartite imports into the rest of SADC,
COMESA and EAC respectively;
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
•
Columns 9 and 10 show the values of imports into tripartite and Rest of Africa
respectively.
Imports shown in Table 1 represent some $1,940 million of the South African imports and
$340 million in exports. Included are rice, wheat, soybean oil, palm oil, chicken meat, beer,
dried beans and cotton lint where imports clearly dominate exports. South Africa’s import
share for the tripartite region for these particular products is at least 30 percent for all but
wheat and palm oil (although, as reported earlier, there may be a transhipment problem with
palm oil, and, adding Namibia’s imports, brings the RSA regional total to 27 percent).
This leaves maize, food preparations not elsewhere specified (n.e.s.), crude materials, beer
and raw tobacco. Maize would appear to have possibilities, but South Africa is a major
importer. In the absence of detailed concordance tables it is not possible to assess exactly
what food preparations n.e.s. and crude materials actually are, and, moreover, while South
African exports are significant, we also need to note that in both instances imports are
greater.3 We also note that around 80 percent of the tripartite imports in both are destined
for SADC.
3
The problem here is one of data aggregation. The FAO gives food preps as: Including both crop and
livestock products; inter alia: homogenised composite food preparations; soups and broths; ketchup and other
sauces; mixed condiments and seasonings; vinegar and substitutes; yeast and baking powders; stuffed pasta,
whether or not cooked; couscous; and protein concentrates. It includes turtle egg and birds' nests. And crude
materials as: including, of vegetable origin: bulbs, tubers, tuberous roots, corms, crowns and rhizomes; live
plants, cuttings and slips; mushroom spawn; cut flowers and flower buds; foliage, branches and grasses, mosses
and lichens; plants and parts used primarily in perfumes, pharmaceuticals, insecticides, fungicides, or for similar
purposes; seaweeds and other algae; vegetable saps and extracts; materials used for plaiting, stuffing or padding;
materials used primarily in brooms or brushes; and materials used primarily in dyeing and tanning. Includes of
animal origin: human hair, unworked and waste; pigs bristles and hair; badger hair and other brush- making hair
and waste; guts, bladders and stomachs of animals (o/t fish); skins and other parts of birds with their feathers
or down; bones and horn-cores, unworked, defatted, simply prepared; powder and waste; ivory, tortoiseshell,
whalebone, claws and beaks; coral and shells of molluscs and crustaceans; sponges of animal origin, ambergris,
castoreum, civet and musk; cantharides, bile glands and other animal products used in pharmaceuticals.
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Table 1: Large imports into South Africa, 2007
South Africa
($1000)
Relative shares of imports
African Imports
($m)
TriRest
partite
Africa
148
74
Exports
1,134
Imports
56,341
RSA
38
SACU
42
RSADC
37
COMESA
5
EAC
16
Beer of barley
9,786
90,545
32
39
48
8
6
279
111
Chicken meat
3,870
173,840
40
43
50
7
0
433
246
Cotton lint
6,002
53,325
31
33
24
41
2
170
98
Crude material
99,364
140,313
40
42
10
36
12
352
231
Food prep n.e.s.
129,522
180,141
31
41
25
23
10
574
699
32,429
207,578
13
17
11
69
3
1,539
1,039
930
196,081
12
27
12
21
41
1,609
748
Rice milled
2,153
290,641
35
37
35
22
6
837
1,230
Soybean oil
551
209,182
41
41
37
18
4
510
831
Tobacco, raw
41,496
81,343
21
21
13
53
13
385
210
Wheat
12,636
260,662
8
9
9
68
15
3,450
4,586
Subtotal
$million
339.9
1,940.0
19
23
18
45
14
10,286
10,103
Beans, dry
Maize
Palm oil
Source: FAO data, tralac calculations.
We note that maize is included in the above table, and this highlights a problem with taking a
snapshot of just one year because of the variation in annual imports. For the 2005 year
maize imports were just $7.781 million and for 2008 they were $27.009 million, figures little
more than 10 percent of the 2007 value even at the 2008 level.
From this point we will break the lines into product groups that seem to fit naturally
together. These will be examined by product group, with the lines ranked by the combined
percentage shares of regional trade that destined for the ‘Northern‘ markets of the EAC and
COMESA. Recall that we have discussed the issue of where the recalcitrant SADC states
may fit into our analysis, and decided that since the focus of this book is on ‘Cape to Cairo’
we must assume that SADC integration is in place. Logically, for a variety of reasons, the
greatest regional opportunities for agribusiness will be in the countries with closer
proximity; these include presumably lower transaction costs, but of course this is not always
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
the case! Products from this point on are ranked by their export value from South Africa in
the respective tables to indicate where South Africa has real export potential.
We start with the dairy products group, where South Africa exports $28.4 million against
imports of $89.1 million. These are shown in Table 2. There are possible opportunities here
for ghee and butter oil, fresh cream and yoghurt, as exports are significantly higher than
imports in these more ‘niche’, non-commodity products.
Table 2: Dairy products
RSA $1000
Relative % shares of tripartite imports
African Imp $m
EAC
Tripartite
Rest
Africa
63%
2%
269
1,177
58%
13%
1%
23
4
21%
24%
54%
1%
151
168
20%
23%
21%
53%
3%
204
540
1,624
12%
21%
48%
22%
8%
14
30
2,079
913
7%
30%
17%
47%
6%
13
6
Butter cow milk
1,893
9,565
13%
15%
17%
68%
1%
71
146
Ghee, butter oil
456
34
0%
0%
1%
98%
0%
20
2
Subtotals ($m)
28.4
89.1
12%
16%
24%
57%
2%
766
2,073
Dairy products
Exp
Imp
RSA
SACU
RSADC
Milk whole dried
5,589
8,613
3%
8%
27%
Yoghurt conc. or
not
5,107
141
1%
28%
Cheese cow
4,644
26,905
18%
Milk skimmed dry
4,612
41,314
Milk whole
condensed
3,979
Cream fresh
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Next is the grouping of fruit and vegetables that has survived through to this point (Table 3).
While South Africa is a significant exporter in products such as citrus products, grapes,
apples and pears, the region is not generally a big importer in these products. Grapes are an
interesting case study, as, although South Africa is a major global exporter, some 27 percent
of the total regional imports of $12 million are coming into South Africa. Note that potatoes
are a significant export from South Africa and a major import into the region. In several of
the minor lines shown such as ginger, chick peas, garlic, green peas and green beans South
Africa is a net importer.
Table 3: Fruit and vegetable lines
RSA $1000
Relative % shares of Tripartite imports
Fresh
fruit/vegetables
Exp
Oranges
390,211
673
Grapes
312,612
Apples
Pears
SACU
RSADC
3%
12%
63%
21%
5%
23
9
3,279
27%
37%
36%
20%
6%
12
7
212,304
123
0%
12%
17%
67%
4%
94
102
118,189
0
0%
18%
22%
60%
1%
12
4
Tangerines, etc.
60,386
706
27%
36%
41%
18%
5%
3
4
Raisins
55,698
164
5%
24%
24%
46%
6%
3
12
Lemons/limes
Vegetable produce,
other
54,389
0
0%
8%
26%
64%
2%
2
1
25,080
100
4%
19%
2%
75%
4%
3
4
Fruit fresh, n.e.s.
14,642
440
6%
32%
13%
49%
6%
7
2
Potatoes
12,245
7
0%
11%
18%
72%
0%
111
223
Dates
4,444
3,051
38%
38%
5%
41%
16%
8
50
Pineapples
4,032
185
11%
49%
3%
47%
1%
2
1
Hops
1,561
2,150
9%
12%
13%
62%
14%
24
6
Other melons
1,436
41
6%
20%
11%
70%
0%
1
1
Leeks, etc.
776
10
3%
41%
31%
28%
0%
0
0
Chillies/peppers
704
6
0%
36%
27%
36%
1%
1
0
Watermelons
589
48
5%
27%
73%
0%
0%
1
0
Garlic
382
1,093
14%
18%
14%
52%
15%
8
24
Chick peas
283
1,685
12%
13%
10%
77%
0%
14
50
Ginger
235
1,275
17%
21%
1%
78%
1%
8
4
Beans, green
225
1,338
16%
39%
17%
2%
42%
8
0
Peas, green
171
1,503
39%
42%
12%
21%
26%
4
0
119
573
34%
34%
18%
47%
1%
2
1
1,270.7
18.5
5%
16%
20%
59%
5%
349
505
Figs dried
Subtotals ($m)
Imp
RSA
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EAC
African Imp $m
TriRest
partite Africa
159
Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
The next grouping is meat and related animal products, including live animals, as shown in
Table 4. Beef and veal are major imports into COMESA in particular, but since South Africa
is a significant net importer, opportunities may be limited. We would also note that the
fellow SACU members of Namibia and Botswana are also significant beef exporters, but
analysis shows that this trade is exclusively destined for the EU where it enjoys significant
tariff preferences that enable the trade to exist at all. Few real opportunities are obvious.
Table 4: Meat and similar livestock products
RSA $1000
Meat/animal prod
Exp
Skins, dry sheep
37,199
Hides, cattle
19,406
Skins with wool
Beef & veal
Relative % shares of imports
RSA
SACU
RSADC
Com
EAC
2
1%
1%
11%
84%
4%
0
0
1,027
26%
38%
6%
16%
40%
4
1
18,966
264
17%
27%
0%
70%
3%
2
0
10,577
23,665
4%
5%
2%
93%
0%
594
0
Cattle
9,255
27,000
42%
45%
27%
25%
3%
65
218
Animal live n.e.s.
5,511
1,690
19%
23%
3%
71%
3%
9
5
Cattle meat
4,755
1,393
10%
21%
59%
20%
0%
13
34
Pork sausages
1,399
668
1%
2%
97%
1%
1%
84
0
Meal. meat
833
5,087
14%
15%
3%
82%
0%
36
0
Fat prep n.e.s.
584
20,143
26%
26%
45%
27%
2%
79
28
Offal, cattle
377
13,174
14%
15%
6%
79%
0%
91
46
Leather waste
372
8
21%
44%
15%
41%
0%
0
0
Horse meat
365
0
0%
8%
10%
83%
0%
0
0
Bacon and ham
350
1,166
10%
13%
83%
3%
1%
11
3
Oils, fats n.e.s.
269
103
19%
35%
34%
30%
1%
1
0
Offal. pigs
254
4,113
43%
44%
51%
5%
0%
10
32
Meat n.e.s.
222
0
0%
25%
40%
35%
0%
0
0
Dried beef
141
1
0%
0%
97%
2%
0%
14
0
110.8
99.5
10%
11%
19%
69%
1%
1,013
367
Subtotals ($m)
Imp
African Imp $m
Rest
Tripartite Africa
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Table 5 shows the regional sugar profile as reported by FAO. The region is a significant
sugar importer and South Africa is a global player in sugar exports. There are considerable
distortions to the sugar markets in many countries, and it is conceivable that closer regional
integration would provide a major boost to South African regional exports. Note in
particular that three-quarters of regional imports are delivered to COMESA and EAC
countries, accentuating possible benefits from a tripartite regional integration. South Africa
is, however, a big importer of sugar confectionary (which may well include chocolate
products). The World Trade Atlas (WTA) data for South African exports of sugar shows
that of the $387 million total to the world during 2009 some $171 million went to Africa,
with Mozambique ($64m), Zimbabwe (S24m) and Sudan ($18m) being the main African
destinations. They were followed by Angola ($17m), Kenya ($13m) and (Madagascar ($12m)
in Africa.
Table 5: The sugar profile
RSA $1000
Exp
Sugar
Sugar centrifugal
Imp
Relative % shares of imports
RSA
SACU
RSADC
Com
African Imp $m
EAC
Tripartite
RestAf
200,429
32,019
10%
14%
11%
52%
23%
319
859
Sugar refined
79,873
4,688
1%
3%
21%
50%
26%
697
782
Confectionery
13,494
50,527
27%
39%
33%
18%
10%
187
86
Subtotals ($m)
293.8
87.2
7%
11%
20%
46%
23%
1,204
1,727
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Those cereals and related commodities that have not been rejected from analysis earlier
(such as rice and wheat) are now examined in Table 6. South Africa is a big importer of
soybeans in particular, as is the region, but exports are trivial.
Table 6: Cereals and related products
RSA $1000
Cereals
Exp
Relative % shares of imports
Imp
African Imp $m
RSA
SACU
RSADC
Com
EAC
Tripart
Rest
Africa
Flour of maize
8,721
2,559
2%
13%
63%
5%
19%
140
37
Flour of oilseeds
3,488
183
1%
13%
18%
7%
62%
19
11
Flour of wheat
2,474
5,440
1%
2%
37%
52%
8%
527
222
Flour of cereals
1,308
160
1%
16%
12%
69%
4%
13
2
Sorghum
1,294
4,558
5%
15%
10%
53%
22%
92
23
Oilseeds, n.e.s.
1,258
301
9%
39%
4%
41%
16%
3
13
Rice broken
1,211
3,036
4%
16%
8%
9%
67%
76
637
Cereals, nes
733
49
1%
4%
22%
52%
23%
8
1
Millet
297
2,880
24%
30%
7%
3%
60%
12
7
Bran of maize
295
1,127
11%
20%
7%
67%
6%
10
0
Soybeans
183
34,217
7%
7%
0%
90%
3%
479
210
Pulses, n.e.s.
120
351
4%
6%
1%
92%
2%
10
5
Subtotals ($m)
21.4
54.9
4%
7%
22%
57%
13%
1,391
1,167
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Again, beverages (as we define them) present a very mixed profile, as shown in Table 7. The
fruit and vegetable juices are important exports from South Africa, but regional imports of
these products are negligible. Wine is another major South African export, and a major
regional import. Note that a significant percentage of these imports are destined for SACU,
with COMESA and EAC much less important. This is almost certainly a case where former
colonial ties strongly influence trade patterns. Both tea and coffee are major regional
imports, but South Africa itself is a significant player in these imports and therefore would
seem to have limited export opportunities. There is, however, a small potential niche in
coffee substitutes (although one is reluctant to speculate about the reception such products
may get in the traditional coffee-cultured markets further north!). Other than these
products, only non-alcoholic beverages stand out from the table.
Table 7: Beverages
RSA $1000
Beverages
Wine
Relative % shares of imports
African Imp $m
Rest
Tripartite Africa
210
178
Exp
668,629
Imp
18,454
RSA
9%
SACU
16%
RSADC
70%
Com
5%
EAC
8%
50,963
52,864
24%
31%
41%
20%
7%
216
130
Grapefruit juice
21,665
21
4%
13%
31%
3%
52%
0
0
Orange juice
16,526
911
6%
34%
24%
25%
17%
16
16
Tea
9,987
25,699
20%
26%
7%
61%
7%
127
235
Citrus juice
5,913
115
5%
22%
56%
18%
5%
2
0
Apple juice
4,744
587
18%
45%
6%
45%
5%
3
4
Coffee, green
4,288
38,480
29%
43%
1%
56%
0%
133
238
Juice. grapefruit
1,867
1
0%
25%
28%
38%
9%
0
0
Waters, ice, etc.
1,500
2,391
8%
14%
58%
24%
3%
29
12
Coffee subst
1,438
22
1%
46%
41%
10%
3%
3
1
Vermouths
452
1,149
23%
38%
41%
9%
11%
5
6
Juice, pineapples
371
0
0%
8%
22%
69%
2%
1
1
Juice,
n.e.s.
179
289
41%
41%
0%
58%
0%
1
2
788.5
141.0
19%
28%
37%
29%
6%
746
823
Beverage
Alcholic
non-
Vegetable
Subtotals( $m)
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
The catch-all selection of fibres and non-food products is a miscellaneous category that
ranges from South Africa’s major global exports of wool to significant imports of food waste
(probably fish meal for animal feed). These are shown in Table 8. Tobacco products stand
out as being major candidates for agribusiness opportunities should preferential access be
granted further north, as both tobacco products not elsewhere specified and cigarettes are
major regional imports. There are no other obvious matchups visible.
Table 8: Fibres and other non-foods
RSA $1000
Fibre/non-food
Wool, greasy
Exp
Relative % shares of imports
Imp
RSA
African Imp $m
SACU
RSADC
Com
EAC
Tripartite
Rest
Africa
145,655
348
21%
22%
54%
23%
0%
2
0
Cigarettes
70,725
15,673
11%
33%
34%
23%
10%
143
397
Tobacco products,
n.e.s.
54,954
8,051
10%
12%
4%
83%
1%
77
89
8,577
39,585
23%
40%
23%
36%
1%
173
85
Forage products
306
34
5%
18%
17%
61%
4%
1
0
Jute
300
295
12%
12%
4%
72%
12%
3
6
Cotton waste
256
1,388
30%
34%
8%
55%
3%
5
1
Natural rubber
143
4,196
34%
45%
2%
47%
6%
12
3
Alfalfa meal/Pellets
126
8
0%
12%
1%
87%
0%
3
22
281.0
69.6
17%
32%
22%
41%
4%
419
603
Food wastes
Subtotals ($m)
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
The processed fruit and vegetable sector is a classical agribusiness sector in the true sense
where South Africa has a significant competitive advantage and the region is a big importer.
While many of these product lines may represent niche markets they are nonetheless
important opportunities, and are shown in Table 9. Note in particular the balances between
South African exports of fruit preparations n.e.s., vegetables in vinegar and dried onions and
regional imports in these products (accept of course that South African trade is given in
$1000s while regional imports are in $ millions).
Table 9: Processed fruit and vegetable products
Process
fruit/vegetable
RSA $1000
Exp
Relative % shares of imports
Imp
RSA
African Imp $m
SACU
RSADC
Com
EAC
Tripart
RestAf
Fruit prepared, n.e.s.
172,199
16,427
26%
34%
20%
44%
2%
63
34
Vegetables in vinegar
11,412
1,652
40%
47%
26%
21%
6%
4
3
Onions, dry
9,308
371
2%
15%
66%
15%
4%
20
42
Groundnut, shelled
6,642
9,433
40%
47%
21%
6%
26%
24
38
Vegetable preserve
6,106
13,349
22%
40%
39%
20%
2%
60
52
Pepper
5,604
8,085
29%
35%
3%
61%
0%
28
8
Fruit, dried, n.e.s.
5,228
1,593
30%
47%
19%
33%
1%
5
1
Pineapple, candied
4,776
481
11%
16%
3%
81%
0%
4
3
Chillies/peppers, dry
3,657
5,664
46%
47%
8%
45%
1%
12
2
Oil of vegetable
3,209
2,710
2%
4%
16%
59%
22%
131
0
Dry apricots
3,160
642
36%
37%
3%
59%
1%
2
3
Can mushrooms
1,095
1,294
22%
23%
25%
46%
6%
6
10
Olive oil, virgin
1,028
23,499
47%
49%
31%
18%
3%
50
44
Sweet corn, prep.
379
1,235
40%
43%
35%
20%
3%
3
6
Vegetable, prep. Pres,
Frozen
361
300
8%
33%
56%
11%
1%
4
5
Paste, tomatoes
254
5,943
5%
5%
34%
58%
3%
123
256
Olives, preserved
247
2,875
31%
33%
9%
57%
2%
9
0
Coconut, desiccated
194
5,469
41%
48%
4%
47%
1%
13
2
Peas, dry
168
7,839
8%
10%
19%
20%
51%
94
14
Dried mushroom
134
374
43%
44%
43%
5%
8%
1
0
235.2
109.2
17%
21%
24%
40%
14%
657
524
Subtotals ($m)
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Vegetable oils are a small category where only really hydrogenated oil stands out as a major
opportunity, although again South Africa is a net importer (Table 10). The other products
seem to be either niche opportunities and/or products where regional imports are very low.
Table 10: Vegetable oil products
RSA $1000
Relative % shares of imports
Vegetable oils
Exp
Imp
Oil, hydrogenated
6,443
Lard, stearine oil
African Imp $m
Rest
Africa
RSA
SACU
RSADC
Com
EAC
Tripartite
17,263
16%
32%
20%
21%
27%
108
74
1,061
13
1%
1%
96%
3%
0%
1
0
Coconut oil
981
1,628
11%
27%
20%
42%
12%
15
5
Groundnut oil
523
222
21%
29%
34%
35%
2%
1
3
Linseed oil
373
3,153
38%
39%
3%
44%
14%
8
20
Subtotals ($m)
9.4
22.3
17%
31%
19%
25%
24%
133
102
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Table 11 shows the final category that is a mixed collection of mostly classical processed
products that have not fitted into earlier categories but collectively represent significant
regional trade. The first point to note, however, perhaps dampening South African export
opportunities, is that in the top four regional imports by value, South Africa is a significant
net importer (infant food, chocolate prsnes, pastry and malt). There are nonetheless some
pointers as to where opportunities may lie, such as cereal preparations and breakfast
cereals.
Table 11: Remaining direct agricultural products
RSA $1000
Relative % shares of imports
African Imp $m
SACU
RSADC
Com
EAC
Tripartite
30%
34%
23%
37%
5%
144
93
2,273
20%
48%
30%
16%
6%
11
11
10,572
8,428
10%
28%
49%
8%
15%
85
42
10,555
35,371
20%
27%
34%
34%
5%
173
139
Infant food
8,878
32,445
24%
25%
21%
51%
2%
135
188
Margarine short
7,253
2,255
3%
26%
52%
15%
7%
78
93
Cereal preps, n.e.s.
5,652
1,837
14%
23%
37%
28%
12%
13
1
Glucose dextrose
4,086
10,022
30%
34%
12%
32%
22%
34
31
Meat extracts
2,454
180
6%
11%
88%
2%
0%
3
0
Prep, flour, malt
extract
2,382
8,604
17%
22%
39%
21%
17%
50
0
Malt
2,218
25,854
15%
27%
35%
15%
23%
176
163
Cooked fruit prp
2,098
70
3%
29%
17%
43%
10%
2
0
Hen eggs, in shell
1,489
621
1%
8%
25%
66%
1%
76
22
Preps of beef
1,435
1,033
2%
13%
67%
20%
0%
44
17
Macaroni
1,429
11,232
13%
16%
53%
27%
5%
86
62
Bread
1,221
2,280
14%
26%
63%
5%
5%
16
2
Vanilla
277
180
20%
25%
49%
22%
4%
1
0
Bran of wheat
275
7,904
34%
37%
0%
50%
13%
23
29
Gums, natural
214
220
16%
19%
19%
46%
15%
1
1
Natural honey
156
2,296
36%
41%
13%
40%
6%
6
8
Liquid margarine
126
38
17%
17%
0%
83%
0%
0
0
Wafers
109
1,085
12%
12%
73%
15%
0%
9
2
Subtotals ($m)
97.7
196.9
17%
25%
35%
30%
9%
1,169
905
Other products
Exp
Imp
Chocolate Prsnes
22,981
42,711
Ice cream
11,869
Breakfast cereals
Pastry
RSA
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Africa
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
The overwhelming conclusion after sequentially examining the regional trade profile is that
there are indeed some opportunities for South African agribusiness as defined here and that
many of these opportunities may well be niche ones. We have not examined tariffs and
market access conditions in detail.
Non-food opportunities
Another major aspect of agribusiness exports and one where Africa offers important
opportunities is to extend the definitions to include goods generally associated with
agricultural production, processing and marketing. To our knowledge a detailed HS code
listing of these products is not available, so we have examined the South African exports
trade data and offer the suggestions in Table 12 as probably being at least a representation of
these products. We hasten to add that there are no assumptions made in this listing. The
data is shown for exports during 2009, denoted in $million, both globally and to Africa. Also
shown are the associated growth rates at 2009 from 2000 and the percentage of that line
destined for Africa. The table shows global exports of $464 million and exports to Africa of
$249 million, meaning that Africa is taking exactly three-quarters of the total. There is a
representation of fertilisers, herbicides, insecticides, assorted chemicals, machinery and
products such as wire shown which we assumed were largely agricultural based. These may
well turn into major opportunities from closer regional integration in the future for these
and other products associated with agribusiness in the broader sense.
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Chapter 5 – Tripartite Agribusiness Opportunities for South Africa
Table 12: Sample of agricultural related exports from RSA, 2009,
global and to Africa, $m
Global
$m 2009
RSA to Africa
HS code
Definition
Growth
$m 2009
Growth
%Africa
310520
Fertilisers
86.13
10.3%
84.98
10.1%
99%
380893
Herbicide, anti-sprouting
57.70
870190
Wheeled tractors, n.e.s.
20.95
310230
Ammonium nitrate
310590
Fertilisers, n.e.s.
380891
Insecticides
19.18
310420
Potassium chloride
16.68
30.6%
16.64
30.6%
100%
843890
Pts of mach prep/mfr food
17.12
8.5%
15.64
10.6%
91%
843780
Mach milling cereals
14.05
21.3%
14.03
24.8%
100%
310240
Ammonium nitrate
11.33
4.2%
11.13
4.4%
98%
721720
Wire
33.67
0.3%
10.96
16.3%
33%
843810
Bakery mach
11.14
2.0%
8.76
5.1%
79%
310210
Urea
7.87
1.5%
7.86
1.5%
100%
310490
Mineral or chemical fertiliser
6.85
41.3%
6.68
43.2%
97%
380899
Pesticides
7.75
310260
Calcium nitrate
843830
Machinery sugar man
380892
Fungicides
11.58
310540
Phosphate
11.79
1.4%
5.37
843880
Mach food/ drink
10.51
14.0%
843610
Machinery, feeding stuffs
4.06
18.0%
721710
Wire
17.74
310310
Superphosphates
310430
26.26
46%
15.7%
20.56
16.3%
98%
26.90
3.8%
20.30
16.7%
75%
19.59
20.9%
18.07
20.8%
92%
17.27
90%
6.47
84%
10.94
21.9%
6.27
16.1%
57%
5.78
27.7%
5.75
29.4%
99%
5.65
49%
10.9%
46%
4.40
7.3%
42%
4.02
31.4%
99%
17.6%
4.01
4.0%
23%
3.63
-24.0%
3.63
17.0%
100%
Potassium sulphate
5.88
33.8%
3.56
28.4%
60%
310290
Fertilisers
4.13
21.1%
3.44
21.9%
83%
843710
Machines, sorting seed
3.45
30.5%
3.30
30.0%
96%
843790
Pts mach seed/grn
4.39
7.7%
3.05
8.9%
69%
843351
Combine harvester
2.69
29.7%
2.53
35.9%
94%
310221
Ammonium sulphate
2.54
1.1%
2.49
1.0%
98%
310229
Ammonium sulphate
2.27
23.5%
2.19
23.1%
97%
721790
Wire
3.85
0.1%
2.16
0.6%
56%
843280
Rollers, soil preparation
2.00
11.6%
1.90
13.6%
95%
Subtotal $m and % to Africa
464.14
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Chapter 6 – An assessment of agricultural sensitive products
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Chapter 6
An assessment of agricultural sensitive products
within the Cape to Cairo Tripartite Region
Taku Fundira
Introduction
In an effort to enhance market access, harmonise policies in areas of common interest and
address the issue of multiple membership among other issues, Heads of States from the
regional economic communities (RECs) of COMESA, EAC and SADC agreed in 2008 to
establish an FTA among the three RECs. Despite this drive to deepen integration among the
tripartite member countries, it is also equally important to note that trade liberalisation can
have negative consequences for the economy of a country. This may therefore partly explain
why countries have tended to prolong liberalisation for or even exclude from liberalisation
certain products of strategic social and economic significance.
In trade policy analysis, such products can be considered and classified as special or sensitive
products. However, what constitutes a sensitive product differs from country to country
and there seem to be no common criteria used to determine these sensitive products.
Despite this shortcoming, there seems to be a common understanding that a product is
deemed sensitive if trade liberalisation impacts negatively on the production or trade of the
said product. Such products are considered to be vulnerable to a trade policy shock that
negatively affects the product’s production, consumption and revenue-earning capacity.
We note that the issue of sensitive products for exemption from tariff liberalisation in the
different countries/regional groupings may become an area of contention in the Tripartite
FTA negotiations, simply because much of the basis for this exemption designation is likely
to be arbitrary, and the sensitive products are more likely to reflect protectionist interests
or rent-seeking behaviour, both of which will perpetuate inefficiencies. There have been
discussions around the issue of sensitive products within the individual RECs and within the
Tripartite FTA itself thus emphasising the significance of this issue and therefore we urge
countries to base their selection on genuine public policy objectives.
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The fact that this envisaged FTA brings together three RECs that have either attained or
intend to establish a customs union (CU) territory complicates this process because of the
common external tariffs regimes that need to be or have been established among the
members of each CU. Currently, COMESA and the EAC have attained customs union status
(in theory COMESA is a customs union, but in practice this has not yet been implemented)
while SADC is still an FTA also aiming at attaining CU status. For the proposed Tripartite
FTA to function there is a need for the rationalisation of tariff structures and the
development of common criteria for sensitive products among other issues.
For the two CUs (COMESA and the EAC), rationalisation of tariff structures is not an issue
as both have agreed and established Common External Tariff (CET) duty rates that have
similar applied duties on capital goods (0%), raw materials (0%), intermediate goods (10%),
and finished goods (25%). However, SADC, which is still an FTA, will need to agree on a
CET and because of the multiple membership conundrum that its members face, may well
rationalise its envisaged CET duties in a manner similar to those of the other RECs (i.e.
COMESA and EAC). The fact that within SADC there is a CU, SACU, with a rather complex
tariff structure in terms of the number of tariff lines and bands, also complicates this process.
Apart from the need to rationalise the tariff structures, there is no indication or
transparency on how the process of determining sensitive products is currently being
conducted in the different RECs. At the moment, only the EAC members have agreed and
developed a single list of sensitive products while the other RECs of COMESA and SADC
have yet to conclude this process. It is yet to be established whether the creation of this
enlarged FTA will create urgency among the members of COMESA and SADC to complete
this process.
Aim of the study
The aim of this chapter is to provide an overview of the rationale behind sensitive products
during trade liberalisation. We attempt to achieve this by providing an overview on what
motivates countries to seek for flexibilities from full liberalisation for certain products. We
do this by highlighting some of the common indicators that are generally viewed as
instruments for selecting sensitive products. This discussion is followed by a review of how
the issue of sensitive products is being handled at the multilateral and regional level. Here
our focus will be on the provisions within the World Trade Organisation (WTO)
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Agreement and also within the COMESA, EAC and SADC agreements as individual RECs as
well as an expanded Tripartite FTA. We also discuss how this affects the conclusion of
negotiations by viewing it in the perspective of the current negotiations both at the regional
and multilateral level.
Our discussion then becomes more focused and specific to agricultural trade liberalisation
within the Tripartite FTA and the extent to which agricultural products form part of the
sensitive products list. The underlying assumption here is that agriculture forms the
backbone of any economic and social development in most of the member countries’
economies and as such it is a sector of significant importance that may need some form of
support. We conclude the paper by highlighting some challenges and possible
recommendations for developing a common approach to identifying sensitive products.
Criteria for selecting sensitive products1
In general, there is a wide selection of criteria which have been used to select sensitive
products and which include the following among others: revenue contribution, importance
of the sector to a country’s economy, and the potential of the sector to regional economic
development.
The importance of a sector to a country’s economy can be measured using the following
indicators:
•
Contribution to employment;
•
Contribution to Gross Domestic Product (GDP);
•
Value-added;
•
Exports earnings;
•
Outputs/Inputs for differentiated tariff treatment;
•
Stage of sector development/infant industry; and
•
Current level of the support given to the sector (incentives).
1
Excerpt taken from COMESA Customs Union (2009).
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The potential of sector to regional economic development is measured using the following
indicators:
•
Existing market size;
•
Potential market size;
•
Current production/potential production/investment plan;
•
Preference agreement;
•
Outputs/inputs for differentiated tariff treatment; and
•
Social, health, cultural and religious reasons.
As highlighted above, the grounds on which goods could be designated as sensitive are quite
broad and this could result in long lists of sensitive products being produced by member
states of the three RECs. Such a scenario would effectively render the process of
liberalisation ineffective.
Provisions regarding sensitive products in trade agreements
The success of any trade negotiations lies in the extent to which flexibilities are
accommodated. Therefore, support to sectors deemed strategic in enhancing economic
growth is important in the development of market access liberalisation schedules. In
multilateral, regional or even bilateral negotiations, it is necessary to include exemptions to
accommodate especially politically sensitive sectors. The role played by sensitive products in
improving market access is such that the number and treatment of eligible tariff lines will be
vital components of any outcome that delivers substantial benefits for both trade and
development.
However, too many exceptions put at risk the objectives of any trade liberalising negotiation.
It is therefore important to note that although such exemptions are allowed, efforts must be
made to either establish agreements without sensitive product lists or at least ensure that
countries are restricted and only allowed to develop short sensitive products lists. In the
sections to follow we highlight how the issue of sensitive products is handled a) at the
multilateral level (WTO); and b) at the regional level (COMESA-EAC-SADC).
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a)
Global context – WTO framework
In general, the objective of the World Trade Organisation is to provide a platform for
negotiations amongst members in order to set general rules for reducing trade barriers. For
the success of this rules based system, transparency, predictability and enforceability is
crucial, which implies a political commitment for all members to abide by the set and agreed
rules.
In the context of the WTO, reference to sensitive products is accorded to agricultural
products that are identified by the members and which are not subject to full tariff cuts.
Therefore both developed and developing countries can declare a product sensitive
especially for political reasons, in which case allow for flexibilities to the liberalisation norm.
According to Gouel et al. (2010), flexible treatment of these products is allowed, more in
relation to market access, and are subject to lower tariff cuts than specified by the formula;
however, in exchange, tariff rate quotas (TRQ) will be opened to ensure (at least some)
‘substantial’ improvements in market access for each product.
In the current Doha Round of negotiations several proposals have been made regarding the
issue of sensitive products. Among others, the key to the success of the Doha Round will be
the manner in which this issue will be handled during the negotiations. A concern which is
regarded as likely to be a major source of conflict is expected to arise from the definition
and application of the concept of 'sensitive products’. Developed countries will be under
pressure to ensure that the sensitive products that they have identified are not concentrated
in products that will render the market access endeavour meaningless. This is because in
agriculture, the tariff structures of most (particularly developed) countries show remarkable
degrees of dispersion, with tariff peaks concentrated in a very small number of agricultural
tariff lines, which explains why even a small percentage of sensitive products can significantly
limit market access improvements’ (Gouel et al 2010).
b)
Regional Context – COMESA-EAC-SADC configuration
Our general assumption is that the main variables, which have been selected as key
indicators for determining a product’s sensitivity by the three RECs are the same as those
used globally. The basket of indicators includes, among others, the following: fiscal,
contributions to output and other policy imperatives of member states.
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We further note that support to sectors deemed strategic in enhancing economic growth is
important in the development of market access liberalisation schedules. This assumption is
supported by SADC and COMESA countries’ selection criteria. Both SADC and COMESA
have broadly considered revenue and infant industry arguments as key criteria for assessing
commodities’ level or extent of sensitivity. The EAC, on the other hand, has identified a
common list of sensitive products although the criteria for the selection of the products are
unclear. Under the EAC Customs Union Protocol, exemptions were made for 58 products
which do not conform to general CET structure and are regarded as sensitive, thus
attracting higher tariffs.
As already noted, the EAC is the only REC that has managed to identify and agree on a
common product list that is designated ‘sensitive’. To date, COMESA and SADC have been
unable to complete the exercise of sensitive products designation. At the launch of the
COMESA CU, it was noted that only 11 of the 19-member group had identified their
sensitive products list. However, the lists of the individual countries and the products
identified remain unclear. For SADC, there seems to be no indication of how far this
exercise has progressed, bearing in mind the fact that SADC is already lagging behind on its
implementation deadlines as set out in SADC’s Regional Integration Strategic Development
Plan (RISDP).
Country analysis of agricultural sensitive products
The big picture
Agricultural plays a crucial role in the economy of most developing countries and provides
the main source of food, income and employment to their rural populations. In most African
countries agriculture contributes over 20 percent to GDP, 70 percent to employment, over
50 percent to export earnings and about 40 percent to government revenue. The
assumption we therefore make from a developing country perspective, is that agriculture is a
pivotal sector both from an economic and a social perspective. Thus, protection or the need
to designate some agricultural products as sensitive can be justified by the fact that in general
for most developing countries (Waiyaki, undated):
•
The domestic market is considered as an initial outlet for poor small farmers;
•
Farmers are not able to compete in export markets or against imported products; and
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•
Poor investment in infrastructure, particularly roads, communication, irrigation and
technology development limits the productive capacity of domestic farmers.
Measures are therefore required to ensure that local poor farmers are not adversely
affected by liberalisation while at the same time efforts to enhance productive capacity,
competitiveness and economic growth are underway.
Depending on each member country’s national strategic plan, countries may either seek to
have the sector heavily protected or maintain some form of protection (i.e. partial
liberalisation). For each option chosen from the scenarios highlighted here, there are
consequences for the producers, the consumers and the economy in general.
Scenario 1: Heavily protected sectors
Governments usually apply high tariffs on certain goods to limit international competition, a
global phenomenon that is deeply rooted in the politics of nations. The high tariff may imply
that the products are sensitive and therefore of strategic importance. However, applying
high tariffs is a protectionist policy that is trade restrictive and comes at great economic
cost. This is simply because it favours the domestic (often inefficient) producers at the
expense of the consumers who pay a high price for domestically produced goods. For
example, an increase in the protection of basic products such as wheat and maize flour,
cigarettes, matches, rice and milk results in price increases for end consumers, hitting poor
people hardest. Therefore such a strategy may not be ideal, especially when poverty
alleviation concerns are part of a country’s national policy strategy.
On the other hand, such protectionist measures may provide incentives for some infant
industries for which prospects exist for the domestic producers to become competitive
over the medium to long term. The only problem with infant industry protection is that it
may lead to the promotion of inefficient industries, which otherwise cannot survive without
the protection. According to Mudungwe (2010), a rapid cut in tariffs for a sector such as
agriculture, without complementary policies and programmes to strengthen internal
production, can result in the collapse of the sector. This can have a negative impact on
employment, effectively raising unemployment in the sector, as it is usually a major
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employment section in the rural areas. This indicator has been identified as very relevant in
measuring product sensitivity in the three RECs.2
Scenario 2: Partially liberalised sectors
Most developing countries including the tripartite members, with the exception of a few, rely
heavily on tariffs for government revenue, and therefore tariff revenue concerns are likely to
play an influential role in trade policy formulation in the medium to long term. This is
because a sharp decline in revenue would impact in the short run on the ability of a
particular government to function effectively in the provision of infrastructure, health and
other social services. Therefore the products that countries may need to exclude from
liberalisation to prevent loss of revenue are often the partially liberalised sectors (attracting
medium-level tariffs) as they yield the most revenue. This is because high tariffs as already
highlighted are restrictive and allow only a few imports from which to collect the tax. Thus,
countries may find that excluding highest-tariff products from liberalisation will force them
to liberalise on their key revenue-generating products that would have negative economic
consequences for the government in the short run. It is in this instance that governments
may consider designating products attracting medium-level tariffs as sensitive purely for
revenue concern, as they find alternatives to help reduce dependency on trade revenue.
Methodology
As noted in previous sections, designation of sensitive products may be for specific reasons
or certain objectives. Some considerations for agricultural sensitive products include those
•
most negatively affected by imports;
•
with very high tariffs;
•
with greatest reductions in tariff revenues;
•
employing large numbers; and
•
where the country has production capacity.
2
We caution that at the time of publication of this statement, it was unclear as to which criteria the EAC had
based its sensitive products list on.
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In our attempt to identify sensitive products for the countries in the proposed tripartite FTA
configuration, in the absence of publicly available sensitive product lists (with the exception
of EAC and SACU), several assumptions were made and are listed below.
i)
For COMESA members, any product traded among member countries (intra-regional
trade) and still attracting a tariff should be designated sensitive. The underlying
assumption is the fact that within a CU, member countries have duty-free access to
each other’s markets and the only exemption is when products have been deemed
sensitive, or in this case, tariffs applied to third-party countries (i.e. to non-COMESA
tripartite members).
ii)
Within SADC, products that currently attract tariffs (with the exception of Angola,
DRC and Seychelles)3, are classified in Category C –‘Sensitive Product’ or Category E
– ‘Exclusion List’. Due to the fact that SADC recently attained FTA status, the two
categories highlighted here are the ones that are permissible for levying tariffs. We
further assume that any agricultural products still attracting tariff should therefore fall
under Category C.
iii)
For SACU countries comprising South Africa and the BLNS (Botswana, Lesotho,
Namibia, and Swaziland), who are also members of SADC, SACU’s CET is applied to
third countries and no internal tariffs exist between member states. SACU’s
membership to SADC implies that the above assumption for SADC is applicable.
However, there are separate offers for South Africa by the other non-SACU SADC
members. These will be analysed separately. We further note that SACU has fully
liberalised its tariffs for agricultural products originating from SADC and hence no
sensitive products lists are in order here.
Caveat
The main disadvantages of this methodology are that:
a)
It uses proposed SADC tariff phase-down schedules which were notified in 2000 when
the SADC Trade Protocol was signed. The problem with this is that the schedules are
only indicative and do not reflect whether countries have kept to their commitments
and implemented the phase-down schedules accordingly. To highlight this predicament
3
These countries have not yet submitted their phase-down schedules with Seychelles having recently rejoined
the regional bloc.
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Chapter 6 – An assessment of agricultural sensitive products
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a case study analysis to support the above notion was conducted for South African
exports to the tripartite countries (see Box 1); and
b)
It is difficult to analyse inter-regional tariffs as these usually reflect averages of tariffs
applied by the member countries of the particular REC under review and not
necessarily for individual countries. Individual country-country analysis was not feasible
due to lack of time and unavailability of data.
Results of the analysis
A total of 16 countries,4 the bulk of which are SADC members, were analysed. Non-SADC
COMESA members (especially the northern countries) were not included due to limitations
of data availability and for Libya - all agricultural products are duty-free at Most Favoured
Nation (MFN) levels. We also note that South Africa is treated differently to other SADC
countries by some SADC members5.
Our analysis reveals the following:
•
Among the countries analysed, market access of agricultural products is generally
relatively open as very few product lines still face high tariffs. This supports the notion
that tariff is no longer the main trade barrier, but that other non-tariff barriers are
inhibiting trade.
•
We also note that certain sectors such as HS04 – dairy, HS10 – cereals, HS17 – sugar,
HS22 beverages and HS24 tobacco products are key sectors for different countries
that are regarded as sensitive and therefore still attract high tariffs.
•
Of the countries analysed, Malawi, has the highest number of product lines which still
face tariffs of 10 percent and above. This may be attributed to the fact that Malawi has
been lagging in its phase-down commitments to the SADC Protocol and also to its
recent request for derogation within the context of the SADC FTA. On the other
hand, this may be a strategy for maximising on tariff revenues which Malawi depends
upon.
4
These were: Botswana, Burundi, Kenya, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia,
Rwanda, South Africa, Swaziland, Tanzania Uganda, Zambia and Zimbabwe.
5
These include Madagascar, Malawi, Mozambique and Zimbabwe.
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•
SACU has fully liberalised agricultural products to its SADC counterparts; however,
separate tariff offers for COMESA countries are in place and will be discussed
separately.
•
Mauritius still applies tariffs to a number of products albeit at relatively low rates with
the exception of HS24 – beverages, spirits and vinegar which has a tariff of 24 percent.
•
As the analysis is based on tariff phase-down offers by individual countries and not by
the current applied tariff regimes, as already highlighted, it is difficult to establish
whether or not all countries have implemented and reformed their tariff regimes
accordingly, and we therefore note with caution the tariffs applied especially in the
case of Zimbabwe which has the majority of products duty-free with the exception of
HS24 – beverages, spirits and vinegar (97%) and HS24 – tobacco and manufactured
tobacco substitutes (28%).
•
Zimbabwe is an interesting case: because of the decade-long economic collapse that
the country has been experiencing recently, the country has allowed imports of basic
foodstuffs (the bulk of agricultural products) duty-free. However, with transparency
concerns regarding the current tariff schedules applied, the case may have been
reversed and tariffs applied.
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Table 1 below summarises the agricultural products at the HS2 level that are currently facing
tariffs, showing the average and (maximum) tariff applied for all 16 countries.
Table 1: Products currently attracting tariffs for select countries (SADC offers)
HS2
H2Des
EAC
Mau
Moz
Mad
Mal
Zam
Zim
01
Live animals
0
0
0
3 (5)
1 (5)
0
0
02
0
3 (32)
4 (10)
5 (5)
5 (15)
2 (10)
0
04
Meat and meat products
Dairy, eggs, honey, edible
animal products n.e.s.*
3 (22)
5 (10)
5 (5)
9 (15)
1 (10)
0
05
animal products n.e.s.
0
0
0
0
5 (5)
0
0
06
Live plants
0
0
0
0 (5)
4 (15)
0
0
07
Edible vegetables
0
0
4 (10)
3 (5)
7 (15)
0 (10)
0 (10)
08
Edible fruits, melons
0
1 (16)
0 (10)
3 (5)
12 (15)
0
0
09
Coffee, tea, mate, spices
0
3 (16)
0
4 (5)
6 15)
1 (10)
0
10
Cereals
24 (75)
0
0 (3)
0
3 (5)
0
0
11
Milling products
4 (60)
0 (6)
0 (10)
0
12 (15)
1 (10)
0 (10)
12
Oil seed, etc. n.e.s.
0
0
0
0
4 (15)
0
0
13
Vegetable extracts n.e.s.
0
0
0
0
2 (15)
0
0
14
0
0
0
0
2 (15)
0
0
0
1 (6)
3 (10)
2 (5)
8 (15)
0
0
16
Vegetable products n.e.s.
Animal, vegetable fats and oils,
etc.
Meat, fish and seafood, food
preparations n.e.s.
0
13 (22)
0
5 (5)
15 (15)
0
0
17
Sugars and sugar confectionery
17 (100)
3 (22)
2 (10)
0
9 (15)
1 (10)
0
18
0
4 (22)
0
0
8 (15)
0
0
0
5 (22)
1 (10)
0
10 (15)
0
3 (10)
0
5 (32)
1 (10)
0
11 (15)
0
0
21
Cocoa and cocoa preparations
Cereal, flour, starch, milk prep
and products
Vegetable, fruit, nut, etc. food
preparations
Miscellaneous edible
preparations
0
6 (22)
0
0
13 (15)
0
0
22
Beverages, spirits and vinegar
0
24 (32)
0 (10)
0
9 (10)
0
97 (1210)
23
Food residues, animal fodder
Tobacco and manufactured
tobacco substitutes
0
1 (22)
0 (8)
0
5 (15)
0
0
8 (35)
7 (32)
1 (10)
0
15 (15)
0
28 (50)
15
19
20
24
22 (60)
* n.e.s. not elsewhere specified
Source: tralac calculations based on SADC tariff offers
During the analysis we noted that some countries (Madagascar, Malawi, Mozambique and
Zimbabwe) had separate offers for products originating from South Africa. The applied tariff,
although not trade restrictive, points to the fact that they may be in place merely as key
revenue generating products or, in fact, accord special preference to goods originating from
South Africa. This is because in the region, South Africa which is a net food exporter is the
major trading partner of most countries as very few countries trade amongst each other.
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Therefore it makes economic sense, from a revenue perspective, to target imports from
South Africa as they form the bulk of trade with these countries.
Table 2 summarises some of the products originating from South Africa that attract duties
(Note that products shaded in [grey] all enter duty-free into the respective markets). Two
points can be raised from the analysis of treatment accorded to goods originating from
South Africa in comparison to products originating from other SADC members. These are:
i)
Either South African products are levied higher tariffs than other SADC members as
reflected by the higher average applied rates for certain products (the case of
Madagascar and to a certain extent Mozambique), this may be the case for revenue
collection purposes;
ii)
or South African products are given special preference in comparison to products
from other SADC members (in the case of Malawi and Zimbabwe). Furthermore, this
observation can be seen from the number of agricultural products from South Africa
that are entering duty- free (see products with area shaded in grey in the table below).
As can be noted, Malawi is the most easily accessed market for products from
South Africa. This can in part be attributed to the long history and bilateral relations
that South Africa has had with Malawi.
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Table 2: Products with different tariffs applied (%) for South Africa
(SADC offers)
HS2
H2Des
01
Live animals
02
04
Meat and edible meat offal
Dairy products, eggs, honey, edible animal products
n.e.s.
06
Mad
Mal
Moz
Zim
5 (10)
0
0
0
10 (10)
0
7 (15)
3 (5)
10 (10)
0
7 (15)
3 (5)
Live trees, plants, bulbs, roots, cut flowers, etc.
1 (10)
0
0
0
07
Edible vegetables and certain roots and tubers
7 (10)
0
6 (15)
3 (5)
08
Edible fruit, nuts, peel of citrus fruit, melons
5 (10)
0 (15)
1 (15)
2 (5)
09
Coffee, tea, mate and spices
8 (10)
0
0
0
10
Cereals
0 (5)
0
0 (3)
0
11
Milling products, malt, starches, inulin, wheat gluten
3 (5)
0
1 (15)
0
12
Oil seed, oleagic fruits, grain, seed, fruit, etc. n.e.s.
1 (5)
0
0
0
13
Lac, gums, resins, vegetable saps and extracts n.e.s.
1 (5)
0
0
0
15
Animal, vegetable fats and oils, cleavage products, etc.
4 (10
0
4 (15)
2 (5)
16
Meat, fish and seafood food preparations n.e.s.
10 (10
0
0
5 (5)
17
Sugars and sugar confectionery
0
0
1 (15)
1 (5)
18
Cocoa and cocoa preparations
0
0
0
2 (5)
19
Cereal, flour, starch, milk preparations and products
0
0
1 (15)
3 (5)
20
Vegetable, fruit, nut, etc., food preparations
0
0
6 (15)
0 (5)
21
Miscellaneous edible preparations
0
0
0 (8)
3 (5)
22
Beverages, spirits and vinegar
0
11 (15)
1 (15)
1 (15)
23
Residues, wastes of food industry, animal fodder
0
0
2 (8)
0 (5)
24
Tobacco and manufactured tobacco substitutes
0
0
2 (15)
8 (25)
Source: tralac calculations based on SADC tariff offers
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A review of SACU’s offer to COMESA (see Table 3) reveals the following:
•
SACU’s offer to COMESA as depicted in the table below highlights that the bulk of
agricultural products from COMESA members face a tariff into the South African
market; thus the creation of an FTA will significantly open new opportunities for the
affected COMESA members;
•
HS24 – tobacco products face the highest average tariff of 24 percent followed by
HS19 – cereal products with a 10 percent tariff. The rest of the agricultural products
face tariff rates of lower than the average of 10 percent.
Table 3: Tariff (%) barriers applied by SACU to COMESA
(SACU – COMESA offers)
HS2
02
04
06
07
08
09
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
29
33
35
38
51
52
H2Des
Meat and meat products
Dairy, eggs, honey, edible animal products n.e.s.
Live plants
Edible vegetables
Edible fruits, melons
Coffee, tea, mate, spices
Cereals
Milling products
Oil seed, etc., n.e.s.
Vegetable extracts n.e.s.
Vegetable products n.e.s.
Animal, vegetable fats and oils, etc.
Meat, fish and seafood food preparations n.e.s.
Sugars and sugar confectionery
Cocoa and cocoa preparations
Cereal, flour, starch, milk prep and products
Vegetable, fruit, nut, etc., food preparations
Miscellaneous edible preparations
Beverages, spirits and vinegar
Food Residues, animal fodder
Tobacco and manufactured tobacco substitutes
Organic chemicals
Essential oils, perfumes, etc.
Albuminoids, modified starches, enzymes
Miscellaneous chemical products
Wool, animal hair
Cotton
SACU
9 (30)
7 (18)
6 (12)
5 (20)
3 (19)
1 (13)
0 (3)
3 (11)
3 (11)
3 (9)
1 (4)
4 (7)
7 (13)
2 (19)
4 (10)
10 (26)
9 (31)
7 (13)
5 (13)
3 (10)
24 (71)
4 (8)
1 (6)
1 (9)
3 (8)
0 (3)
3 (9)
Source: tralac calculations based on SADC tariff offers
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BOX 1: A review of South African agricultural exports facing tariffs in the
Tripartite Configuration
During the data training week we undertook a review of the agricultural products that South
African exporters face in the COMESA-EAC-SADC Tripartite Configuration. Our aim is to
identify agricultural products that are classified as sensitive products by members of the Tripartite
Configuration and therefore may well remain so in the broader regional integration process. We
have also included the broad range of clothing and textile products (HS50-63) as a category, but it
should be noted that only agricultural products (raw materials such as cotton, animal hair, etc) are
considered and therefore should not be confused as an examination of the entire clothing and
textiles products. To determine the tariffs barriers applied to these South African products, we
use the current applied rates as reported to the UN ITC MacMap database (at the HS 6 level of
the harmonised classification system) by the respective countries in the Tripartite Configuration.
Our preliminary analysis reveals the following:
•
With the exception of SACU members and Libya, South African agricultural products still
face tariffs in the Tripartite Configuration;
•
Mauritius (95%), Seychelles (85%), Zambia (83%) and Mozambique (81%) are the only
countries which are relatively open to South African exports of agriculture products. The
rest of the countries in the Tripartite Configuration still maintain protection in over
60 percent of the product lines under review, with Burundi (100%), Rwanda (96%),
Zimbabwe (95%) and Sudan (88%) maintaining protection in virtually all products.
•
In most countries under review, ‘food, beverages and tobacco’ and ’vegetable products’ still
face the highest level of protection.
•
Mauritius, Mozambique, Seychelles, Tanzania and Uganda are the only countries that offer
duty-free access to South Africa raw material products classified under Clothing and Textile
(C&T) exports.
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The tables below provide details of the level of protection that South African exporters are
facing in each of the countries in the Tripartite Configuration. We have only shown the
averages and not the variations within these categories. The data clearly signals that:
(a)
There is a considerable distance to go in implementation of the so-called SADC Free
Trade Agreement; and
(b)
This and the high tariffs further north do not augur well for anything other than an
FTA in name only!
The categories shown are:
•
Chapters HS 01 to 05 inclusive, live animals, animal products;
•
Chapters HS 06 to 14 inclusive, vegetable products;
•
Chapter 15, animal or vegetable fats & oils;
•
Chapters 16-24 inclusive, food, beverages & tobacco; and
•
Chapters 50 -63, clothing and textiles.
Table 4: Regional (average) tariff barriers to South African exports
Country/sector
Ch 01 05
Ch 06-14
Ch 15
Angola
10
9
5
Burundi
18
5
5
Comoros
11
5
5
DRC
12
12
14
Djibouti
12
12
13
Egypt
14
6
7
Eritrea
7
10
9
Ethiopia
23
24
23
Kenya
26
21
14
Madagascar
18
18
16
Malawi
13
19
18
Mauritius
12
11
Mozambique
14
14
13
Rwanda
13
9
22
Seychelles
63
83
Sudan
37
33
33
Tanzania
27
24
19
Uganda
27
23
19
Zambia
11
19
13
Zimbabwe
35
23
15
Source: UN ITC MacMap database, data training week analysis
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Ch 16-24
14
5
8
16
15
237
16
25
23
18
22
13
12
26
211
36
23
23
19
36
CH 50-63
2
5
16
5
26
5
2
6
0
5
8
0
0
6
0
28
0
0
15
5
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Impact of sensitive products on deepening integration and the way forward
As highlighted earlier, the process of designating sensitive products can perpetuate
inefficiencies and thus undermine the process of deeper integration. Furthermore, the lack of
resources and analytical capacity to undertake detailed analysis and the lack of properly
defined guidelines and benchmarks also hinder the process. This may partly explain the
delays by some countries in COMESA and SADC to identify and notify their lists of sensitive
products to the respective secretariats. There is therefore need for policy makers to find
ways to develop a systematic approach to determining sensitive products and to ensure that
all stakeholders are aware of the purpose of a sensitive products list (i.e. economic versus
political versus social concerns). As a principle, it is proposed that the RECs should not
introduce new sensitive products in addition to those currently present in their schedules,
but to work towards reducing their lists. This is simply because even a short list of sensitive
products reflects poorly on the desire to establish a CU with a CET, to which all the RECs
have aspired or aspire to attain.
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Chapter 6 – An assessment of agricultural sensitive products
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References
COMESA Customs Union. 2009. Status on Sensitive Products and Implementation of the
Transition Period. [Online]. Available:
http://programmes.comesa.int/attachments/119_comesa_customs_union_status_03%2006%2
02010.pdf.
Gouel C., Mitaritonna, C. and Ramos, M.P. 2010. The Art of Exceptions: Sensitive Products in the
Doha Negotiations. Working Paper No 2010 – 20. Paris: Centre d'Etudes Prospectives et
d'Informations Internationales (CEPII).
Mudungwe, N. (2010). Is there a Common Criteria or Approach for Selecting Sensitive Products in
Regional Trading Blocs: The Case for COMESA, EAC and SADC? Paper presented at the
Tripartite Regional Integration Forum, Arusha, Tanzania, 17-18 June 2010
Waiyaki, N. (Undated). Sensitive & Special Products in Trade Negotiations: The Case of Kenya.
Kenya Institute for Public Policy Research and Analysis, available on
http://www.uneca.org/trid/documents/cotonu/Special%20Products%20Slides%20Cotonou.ppt
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Chapter 7 – Non-tariff barriers affecting trade in the COMESA-EAC-SADC Tripartite Free Trade Agreement
Chapter 7
Non-tariff barriers affecting trade
in the COMESA-EAC-SADC Tripartite Free Trade Agreement
Willemien Viljoen
Key point summary
Successive rounds of multilateral trade negotiations have led to a decrease in the use of
tariffs as barriers to trade. However, the reduction in tariffs has been substituted by the
utilisation of non-tariff barriers (NTBs). NTBs are defined as any barrier to trade other than
import and export duties. This includes export taxes, import bans, government monopolies,
cumbersome documentation requirements and a lack of physical infrastructure. The
utilisation of NTBs is a growing concern in Africa and a major obstacle to regional
integration, since these barriers increase business costs and restrict market access.
The most prevalent NTBs hindering regional trade in the Tripartite Territory (COMESA, the
EAC and SADC)1 include customs procedures and administrative requirements, technical
standards, government participation in trade and the lack of physical infrastructure. This is of
particular importance to agricultural trade within the region. Cumbersome documentation
requirements, stringent standards and inefficient road and rail networks cause time delays
and increase the cost of intra-regional trade. This has a direct and indirect impact on the
quality and price of agricultural products available in the regional market.
The member states of COMESA, the EAC and SADC have realised that NTBs are a major
impediment to the expansion of intra-regional trade and identified the elimination of NTBs
as one of the key objectives of the Draft Tripartite Agreement. In order to reach this
objective a Web-based NTB Monitoring Mechanism has been put into place. Although the
mechanism has been successful in resolving various NTB complaints among trading partners,
there is still a range of NTBs prevalent in the region. In order to enhance regional
development and promote intra-regional trade the Tripartite member states need to
intensify efforts to address NTBs on a regional basis.
1
Common Market for Eastern and Southern Africa, the East African Community and the Southern African
Development Community.
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Chapter 7 – Non-tariff barriers affecting trade in the COMESA-EAC-SADC Tripartite Free Trade Agreement
1.
Introduction
Successive rounds of multilateral trade negotiations have seen a significant reduction in the
use of tariffs as trade barriers among trading partners. However, some ‘tariff peaks’ are still
present in the agricultural sector. As the utilisation of tariff barriers has decreased, the
importance and prevalence of non-tariff barriers have increased.
The term ‘non-tariff barriers’ has various interpretations, but it is mostly used to indicate
policy interventions, except tariffs, which affect and distort the trade of goods, services and
factors of production. Trade distortions exist when the price at the border differs from the
domestic price due to measures like domestic regulations or administrative procedures
imposed for various reasons, including ensuring food safety, product safety and addressing
environmental issues.
The Organisation for Economic and Cooperation Development (OECD) makes a distinction
between non-tariff measures (NTMs) and NTBs. NTMs are seen as policy measures which
limit trade, with no implied judgement on the legitimacy of the measures. NTBs are seen as
instruments which violate World Trade Organisation (WTO) law. A comprehensive
distinction between NTBs and NTMs is also provided by the Non-Tariff Barriers Reporting,
Monitoring and Eliminating Mechanism for the EAC, COMESA and SADC. NTBs are
restrictions which are the result of prohibitions, conditions or specific market requirements
which make the import or export of products difficult and/or costly. NTBs can also be used
to describe the unjustified or improper application of NTMs including sanitary and
phytosanitary measures.
NTMs can exist for good reason, for instance sanitary and phytosanitary measures are
necessary for consumer protection and the protection of public health and safety, while antidumping measures are used to address competitiveness issues and to correct anticompetitive behaviour. However, these measures can become a barrier to trade when
implemented in such a way as to unnecessarily increase the cost of international trade,
inhibit trade, or are implemented in a manner which discriminates against import relative to
domestically produced goods or applied in an illegitimate or WTO inconsistent manner.
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NTBs are thus seen as very important trade policy issues due to these measures’ ability to
restrict trade in an unpredictable and persistent manner and to influence trade patterns and
the free movement of goods and services within countries and across borders. Countries
have made significant progress in the reduction of traditional NTBs, such as quotas and
import licensing requirements. However, there has been an increase in the use of ‘new’ or
modern NTBs like Sanitary and Phytosanitary Measures (SPS) requirements and technical
standards and regulations.
The multilateral rules of the WTO include explicit agreements to manage NTBs with a
specific focus on customs and transit, technical regulations and health and safety issues. Over
the past 15 years WTO member countries have implemented the SPS and Technical Barriers
to Trade (TBT) Agreements. The WTO dispute settlement process has also resulted in a
number of NTBs resolved on a multilateral level through the enforceable legal process and
the consequent development of case law. At the same time, regional agreements have also
now shifted their focus to addressing NTBs prevailing in regional trade. This is also the case
in Regional Economic Communities (RECs) in southern and eastern Africa, with the trade
agreements and protocols of the EAC, COMESA and SADC providing a legal framework for
the elimination of NTBs.
The focus of this chapter is on the various NTBs present in the EAC, COMESA and SADC
in order to establish which NTBs are prevalent in the Tripartite Territory. Firstly, the
division of NTBs into different categories is examined. Secondly, the focus is on those NTBs
which have been identified as common to the EAC, COMESA and SADC regions. Thirdly, an
in-depth analysis of the specific NTBs applicable to the agricultural trade in the three RECs is
provided. Lastly, the legal framework and practical measures to address and eliminate NTBs
in the region are evaluated.
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Chapter 7 – Non-tariff barriers affecting trade in the COMESA-EAC-SADC Tripartite Free Trade Agreement
2.
Categories of NTBs
In order to identify NTBs, Deardorff and Stern (1998) suggests the taxonomy of NTBs,
consisting of five broad categories:
a.
Quantitative restrictions and similar limitations including import quotas, export limitations
and import and export prohibitions.
b.
Non-tariff fees and related charges which include anti-dumping and countervailing duties,
border tax adjustment and deposit requirements on imports.
c.
Government participation in restrictive practices in trade like government procurement and
domestic content requirement policies, immigration policies and state-trading and
state-sponsored monopolies.
d.
Customs procedures and administrative practices which include differing customs valuation
methods, customs classification other than the Harmonised System codes and customs
inspections.
e.
Technical barriers to trade related to health like sanitary and environmental regulations,
quality standards and packaging and labelling requirements.
However, the categorisation of NTBs in Annex 14 of the revised Draft Agreement on
establishing the Tripartite FTA between COMESA, SADC and the EAC of December 2010
follows the general categorisation of NTBs by the WTO:
a.
Government participation in trade and restrictive practices which are tolerated by
government, including export subsidies, state trading, government monopoly and
flawed procurement policies.
b.
Customs and administrative entry procedures, which include anti-dumping duties imposed
on imports, import licensing, international taxes and arbitrary customs classification.
c.
Technical barriers to trade, like restrictive regulations and standards which are not based
on international standards and inadequate or unreasonable testing and certification
arrangements.
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d.
Sanitary and phytosanitary measures, including conformity assessment related to SPS or
TBT and special formalities which are not related to SPS or TBT.
e.
Specific limitations, which include quantitative restrictions, exchange controls, minimum
import price limits, quotas and export restraint arrangements.
f.
Charges on imports like administrative fees, variable levies, border taxes and special
supplementary duties.
g.
Other procedural problems for example, discrimination, costly and lengthy procedures,
documentation requirements and a lack of information on procedures.
Apart from these specifically listed categories there can also be other sources of NTBs.
These include corruption and inadequate infrastructure. The need to pay bribes imposes an
additional cost on importers and exporters of which the value is impossible to estimate
prior to the bribe being paid. Poor road, rail and port infrastructure can lead to delays in the
transportation of products which translates into higher freight charges for importers and
exporters and thus an increase in the price at which the product is available to the final
consumer.
3.
NTBs in COMESA, the EAC and SADC
The utilisation of NTBs is a growing concern within Africa, with NTBs being substituted for
the reduction in tariffs. NTBs are a major obstacle to regional trade and have a serious
impact on business costs and market access. Agricultural products are affected the most
with various agricultural commodities facing restraints like import and export bans, quota
restrictions, SPS and TBT restrictions, licensing requirements and seasonal restrictions.
NTBs are therefore a major constraint on the progression of regional integration within
southern and eastern Africa.
Although various trade liberalisation schemes have been launched in African RECs to reduce
the use of NTBs, the application of various NTBs persists in a range of countries in the
region. These include the use of quantitative restrictions, roadblocks and checkpoints which
pose serious barriers to intra-regional trade.
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Inadequate infrastructure is one of the main obstacles to intra-Africa trade, investment and
private-sector development. There is also a high cost of doing business in Africa due to
these infrastructural shortcomings and other barriers, including duplicate border procedures
and cumbersome documentation requirements.
3.1
NTBs common to COMESA, the EAC and SADC
In the November 2009 Draft Agreement Establishing the Tripartite FTA Annex 1 identifies
23 NTBs common to the Tripartite Territory. Even though this section was omitted from
the December 2010 Draft Agreement, it still indicates those NTBs which have been
identified in all three RECs of the Tripartite Territory. Using the Deardorff and Stern five
category NTB classification these common NTBs are the following:
Table 1: NTBs common in the Tripartite Territory
Quantitative
restrictions
and similar
limitations
• Temporary
bans
• Import bans
and quotas
• Import
prohibitions
• Import and
export quotas
• Import and
export
licensing and
permits
Non-tariff
fees and
related
charges
• Nonstandard
and costly
transit
charges
• Restrictive
non-import
and -export
duties
Government
participation in
restrictive
practices in
trade
• Immigration
procedures
Customs procedures
and administrative
practices
• National food
security
restrictions
• Including non-standard
import declarations,
limited customs working
hours and the nonacceptance of
certificates of origin
• Cumbersome
visa requirements
• Incorrect tariff
classifications
• Single channel
marketing
• Non-acceptance of
certificates and trade
documents
• Business
registration and
licensing
Technical
barriers to
trade
• Quality
inspection
procedures
• Varying trade
regulations
Other
• Inadequate
infrastructure
• Agricultural
NTBs
• Roadblocks
• Pre-shipment
inspections
• Duplication of verifying
quality, quantity and
duty value of cargo
• Changes in road and
border tolls
Source: Draft Agreement Establishing the Tripartite FTA (11 November 2009)
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In the Draft Report on Establishing the Tripartite FTA November 2009 a summary of those
NTBs which have been reported in official documents of the three RECs is provided. From
the table below it is evident that there is little variation in the major NTBs which is
applicable in COMESA, the EAC and SADC.
Table 2: Major NTBs applied in all three RECs
NTB
Specific measures utilised
Customs administration and
document procedures
Conflicting application of rules of origin and verification of goods;
application of discriminatory and variable taxes and fees and limited
working hours of customs officials
SPS and TBT measures
Quality inspections even of goods certified by accredited laboratories
or marks issued by bureaux of standards; different measurements of
weight, labelling and quality standards; different procedures for
certification marks and variable requirement for tolerance limits and
packaging
Immigration requirements
Visa and work permit requirements
Varying and problematic
transit and transport
procedures
High and variable transit charges; bottlenecks in ports and border
posts; non-harmonised axle-load limits; long delays during vehicle
inspections
Quotas or bans
Imports and exports
Source: Draft Report on Establishing the Tripartite FTA 11 November 2009
According to the Draft Report pre-shipment inspection and single channel marketing are
more prevalent in COMESA and SADC, while roadblocks, corruption and weighbridges are
more problematic within the EAC. In COMESA one of the major NTBs is the application of
foreign exchange controls, while in SADC it is national food security restrictions.
3.2
Agricultural specific NTBs
One of the major obstacles to agricultural trade in the Tripartite Territory is the great
degree of overlap in member countries among COMESA, the EAC and SADC. Out of the 25
countries (Madagascar excluded) which will form the Tripartite Territory all EAC member
countries, except Tanzania which is also a member of SADC, are also members of COMESA,
while there are seven member countries (Madagascar excluded) which overlap in
membership between COMESA and SADC. The lack of harmonisation of customs and
documentation requirements, technical standards and SPS requirements among member
countries of an REC as well as among RECs with overlapping membership creates
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uncertainty and confusion for traders regarding the import procedures and documentation
to use when exporting to a country with overlapping membership. This can lead to time
delays and increased costs if the incorrect procedures and documents are used.
Overlapping membership and a lack of harmonisation can also have a severe impact on the
use of rules of origin among member countries and RECs. If there are different rules of
origin applicable to intra-regional trade within each REC and countries are members to
more than one of these RECs it can result in countries utilising those rules of origin which
are the most beneficial and the simplest to use. It can also imply that a country uses two
different sets of rules of origin when trading with different countries which are members of
different RECs. A free trade area aims to reduce intra-regional barriers among member
states, however with different rules of origin applicable within each of the three RECs, and
when some countries apply different rules of origin to different countries, uncertainty,
confusion and barriers to trade will increase rather than decrease.
The other NTBs which have a significant impact on agricultural trade in the three RECs
include customs and administrative procedures, inefficient and inadequate physical
infrastructure and technical standards. There is a great degree of similarity in the NTBs
applicable to all three RECs, which can indicate that a regional approach to eliminating
barriers might be more successful than national strategies.
3.2.1 The EAC
Various NTBs have been identified within the EAC of which customs and administration
procedures, government participation, lack of infrastructure and additional import fees and
charges are the most prolific. Other NTBs also applicable within the region include
roadblocks, non-harmonised SPS requirements and arbitrary technical standards and
inspections. NTBs in the region which have been identified for immediate action to be taken
are the non-recognition of EAC rules and certificates of origin, import bans on milk, day-old
chicks, beef and poultry, corruption, road blocks and weighbridges along major highways,
inspections required for imports to Tanzania, and Kenya’s lack of recognition of SPS
certificates and levies charged on plant import permits for Ugandan tea (Kirk, 2010).
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3.2.1.1
Customs and administration procedures
The highest direct and indirect costs to the private sector trading within the EAC is the loss
of man-days when goods are in transit due to the time required for clearance of goods at
the internal borders and along the transport corridors. It takes approximately two weeks for
goods’ clearance at Mombasa and all goods entering Burundi must be cleared at one customs
point at Bujumbura port.
Prolonged formalities, lengthy procedures, duplication of clearance procedures, limited
capacity at the border posts and the flexible implementation of national policies all add to
the monetary costs and transit time for goods traded in the EAC. This includes the varying
systems of import declaration, limited and varying working hours at the customs posts and a
flexible application of axle-load restriction for trucks in Kenya and Uganda. Of the five EAC
member states Uganda, Tanzania, Rwanda and Burundi use the ASYCUDA customs system
while Kenya uses SIMBA, with the linkage between the systems problematic. This causes
delays in the processing of import and export documentation with papers often reaching the
border days after the arrival of goods.
There is no consensus in terms of standardised certificates of origin with bureaucratic delays
in issuing the certificates affecting the flow of goods in the region. The EAC rules of origin
only apply when imports are ‘wholly produced’ in the EAC member country; if any changes
in the product have taken place member countries tend to use the COMESA rules of origin.
Administrative complexities are problematic for transparency and efficiency in the clearance
of goods, and the inefficiency and lack of information to customs officials and agents add
even further delays and congestion, and increase the cost of loading and clearing cargo.
These delays increase the risk of quality deterioration of the import products.
3.2.1.2
Government participation
The national government, parastatals and monopolies are responsible for the operation of
ports, including inland container freight stations and weighbridges. The various weighbridges
in the member countries of the EAC have differing readings, varying with as much as
between 500-700 kilograms, with weighbridges in Kenya also not being calibrated. The use of
different weights creates the opportunity for bribery of the weighbridge officials. These
compulsory weighbridges create bottlenecks for economic activity in the region.
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3.2.1.3
Inadequate infrastructure
Railways are traditionally the cheapest form of transport for bulk goods with high volumes
and important for the transportation of bulk commodities like food grains, animal products
and other agricultural commodities which form an integral part of intra-EAC trade.
However, goods transportation in the EAC via railways has substantially decreased over the
years due to the decline in the quality and reliability of rail services in the region.
The lack of physical infrastructure is constrained by government monopolies operating the
infrastructure in member countries, limited capacity of the existing infrastructure and the
inability of governments to maintain and rehabilitate these facilities.
3.2.1.4
Import charges and fees
Kenya, Tanzania and Rwanda charge entrance fees (road tolls) on each truck entering their
territory. Kenya, for instance, charges approximately US$60 for a truck travelling from
Mombasa to Nairobi and US$90 for trucks going farther than Nairobi. Each country also
applies varying local levies on road transportation. Tanzania levies different non-tariff fees on
agricultural import products, including inspection fees, warehousing fees, agricultural cess,
livestock marketing cess and form support fees.
3.2.1.5
Other applicable NTBs
There are multiple police roadblocks and mobile control centres in each member state
throughout the region, resulting in unnecessary delays in the road transportation of goods,
for instance, it takes two days to transport goods the 950 kilometres journey from Mombasa
to Malaba due to convoys and roadblocks on all the major highways.
There is a lack of harmonised EAC-wide SPS requirements and standards. The arbitrary
application of technical quality standards and inspections with compulsory SPS requirements
prevail in the REC. EAC standards bureaux have different procedures for issuing certification
marks, inspections and product testing which make it difficult to facilitate intra-regional
trade.
Tanzania has cumbersome SPS certificate and permit procedures with the laboratory in
Dar es Salaam supported by the Tropical Pesticide Research Institute in Arusha delaying the
approval of goods. Even with national and EAC quality marks and certificates issued by the
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required specified authorities, product verification and inspections at the borders are
problematic. Tanzanian food exporters pay 200 000 Tanzanian shillings per shipment for a
Tanzanian Bureau of Standards certificate, but these certifications are not recognised by
other EAC members. Ugandan products transiting through Kenya to countries outside the
region is required to pay an inspection fee of US$400 at the border despite having
certificates issued by the Ugandan Bureau of Standards. All EAC countries have statutory
marketing standards for fruit and vegetables, but these have not yet been harmonised.
3.2.1.6
Product specific NTBs
NTBs applicable to the intra-EAC trade of grains, beef and dairy include the following:
•
Grain: Various NTBs on the grain trade include proof of origin, phytosanitary
certificates, police roadblocks and periodic bans on imports by Kenya and Tanzania.
Tanzania often bans the export of grains and other food products due to a poor
harvest, while Kenya temporarily restricts grain imports to protect domestic farmers.
•
Beef: NTBs include administrative requirements (licences, municipal and council
permits) taxes, roadblocks, weighbridges and corruption. Uganda has placed a ban on
beef imports from Kenya due to unspecified animal diseases.
•
Dairy: A complex regulatory regime prevents imports of dairy products from Tanzania
and Uganda to Kenya. Kenyan regulations require milk exporters from Tanzania and
Uganda to have certificates which prove that these products are processed under
constant supervision by the veterinary authority in the region. However, the veterinary
standards of Kenya are not made public and there are four official institutions
responsible for Kenya’s dairy imports.
3.2.2 COMESA
The most important NTBs in COMESA are technical barriers to trade, including SPS
requirements, technical standards and inspections, and customs documentations and
procedures. There is a lack of transparency and consistency in customs procedures, high
freight and transport charges and a wide-range of health and safety requirements in the
region.
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3.2.2.1
Technical barriers to trade
Cumbersome and costly quality inspections and testing procedures, unstandardised SPS
requirements and technical food safety standards are some of the NTBs which hinder intraCOMESA trade. An example is Ugandan exports to Egypt. Egypt requires certificates of
origin for Ugandan products to be endorsed by the Egyptian Embassy in Kampala before any
products can be exported to Egypt.
Some member states require pre-shipment inspections before allowing import into their
domestic market which increases the costs of imports. It is estimated that pre-shipments
required for imports from the rest of COMESA increase the average cost by US$200 per
consignment. Pre-shipment inspections are also required for all goods imported into Malawi
from any member state.
3.2.2.2
Customs procedures and administrative requirements
There is a lack of harmonisation in the documentation and documentation processes
required by the COMESA member states, which hinders cross-border trade. Cumbersome
customs clearance procedures also lead to long waiting periods for imports at the various
internal borders.
3.2.2.3
Product specific NTBs
The horticultural trade in COMESA is subject to various health, safety and environmental
NTBs, including export bans, the misuse of SPS measures, technical standards and material
conformance requirements.
•
Trade policy NTBs: these include the misuse of rules of origin, export taxes, import
licences, import quotas, production subsidies, state trading and import monopolies.
•
Administrative NTBs: these include customs clearance delays, lack of transparency and
consistency in customs procedures, overly bureaucratic and arbitrary processing and
documentation requirements for consignments.
•
Poor infrastructure: these include poor physical infrastructure, including roads and
railways and a lack of capacity in existing infrastructure for large volumes of transit
traffic and the availability of storage and warehouse facilities.
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3.2.3 SADC
A wide range of NTBs is applicable to trade in the SADC region, especially barriers on the
import and export of various agricultural products. These include quantitative NTBs and
similar restrictions, customs procedures and administrative requirements, technical barriers
and a lack of physical infrastructure. Gillson (2010) identified the following barriers which
affect the trade in different agricultural products in the region:
NTB
Products affected
Import bans, quotas and
levies
Import permits and levies
Wheat, poultry, flour, meat, maize, sugar, eggs, pork and fruit &
vegetables
Eggs, fruit & vegetables, livestock and maize
Single marketing channels
Wheat, meat, dairy, maize and sugar
Rules of origin
Palm oil and wheat flour
Export taxes
Dried beans, live animals, sugar, maize, meat and coffee
Standards
Milk, meat, maize, bran, cotton cake, poultry, sugar, coffee and
ostriches
Source: Gillson (2010)
3.2.3.1
Quantitative NTBs and similar restrictions
Trade permits, export taxes, import licences and bans are still prolific in the SADC region
which limits the opportunity for regional sourcing and trade of agricultural products.
According to Gillson (2010) Shoprite spends US$20 000 per week on import permits to
trade meat, milk and plant-based goods to stores in Zambia alone. Shoprite applies for
approximately 100 to 300 single entry import permits per week to enable it to export goods
from South Africa to stores in other SADC member countries. As a result of these and
other documentation requirements there can be as many as 1600 documents accompanying
each truck Shoprite sends over a SADC border. A lack of coordination between permit, tax
and licence requirements among governments also causes further delays, especially when a
new product needs to authorised for import into another SADC member country. Gillson
(2010) reports that it took three years for another South African retailer to obtain
authorisation to export beef and pork products from South Africa to Zambia.
Zambia restricts the importation of fruits and vegetables from other SADC member
countries through the use of permits and licences. Export taxes are used by Zambia on
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exports of coffee to South Africa and by Zimbabwe to restrict the exportation of strategic
food products and raw materials. Zimbabwe currently has an import ban on poultry
products and import restriction on flour and maize meal, while Zambia does not issue
import permits for sterilised milk imported from Zimbabwe.
According to the Botswana Cattle Export Levy (Amendment) Act 2005 and Regulations
(2006) and the Livestock and Meat Industries Act (2006) the Minister can impose an export
tax on any livestock and livestock products produced in Botswana. The Controls of Goods,
Prices and Other Charges Act allows for import restriction on horticulture, poultry, fresh
milk, pork and grains. According to the Act importers must apply for import permits which
specify the exact quantities that will be imported. Import permits for horticultural products,
unprocessed chicken meat and table eggs and pork are not issued when domestic
production is sufficient to meet the local demand. Dairy processors are allocated monthly
import quotas for fresh milk, while retailers can only purchase milk and milk products from
domestic producers; thus, in effect, fresh milk imports are prohibited.
3.2.3.2
Customs procedures and administrative requirements
Inefficiencies in customs procedures, including delays at road checks, borders, cumbersome
administrative requirements for rules of origin certificates, and variations in border operating
times have been identified as significant barriers to trade.
According to Gillson (2010) the costs traders incur due to the administrative requirements
for certificates of origin account for almost half of the value of the duty preference. Shoprite
spends approximately US$5.8 million per year on administrative costs, including filing
certificates and obtaining import permits to secure US$13.6 million in duty savings under the
SADC preferences. Woolworths does not use SADC preferences when exporting food
products to stores in non-Southern African Customs Union (SACU) SADC markets,
choosing to pay full tariffs due to the administration of the SADC Rules of Origin (RoO)
regime being too costly.
Non-standardised SADC documents and the lack of qualified personnel at border posts
result in the delay of goods being imported into the region. The average declaration of
imports, assessment of goods, physical verification and release from customs at
Dar es Salaam takes approximately seven days to be completed. In Mozambique there are
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16 procedures in the importation process which take an average of 38 days at an average
cost of US$1,616 per container, while six documents on average are required for all
exports, taking 39 days for goods to be authorised and cleared at customs at an average cost
of US$1,516 per container. Cumbersome and bureaucratic delays in terms of processing
documents are delaying the importation of goods at the Malawian border. Inefficient
bureaucracy, red tape and time-consuming customs clearance of goods also cause time
delays at the border between Namibia and Angola.
3.2.3.3
Technical barriers to trade
The standards regime in SADC can be classified as being over-reliant on mandatory
inspections and certifications, national standards and testing requirements and overlapping
responsible authorities. Efforts have been made to harmonise standards in a regional SADC
regime, but there is still a lack of application by all member countries. Currently only
Namibia and Swaziland have adopted all 78 SADC standards for the region.
Various member countries require cumbersome pre-shipment inspections and stringent SPS
certification requirements for the importation of different agricultural products. There is
also a lack of clarity and information regarding the authorities responsible for issuing and
processing SADC certificates of origin in member states.
Mauritius requires special permit certificates for health and environmental reasons for
various goods, including food products like vegetable inputs and potatoes, while all
agricultural imports require phytosanitary certificates. Namibia has stringent SPS
requirements for imports of live animals and meat products.
Tanzania requires pre-shipment inspections for all imports by the exporting country, but also
after the products cross the Tanzanian border. Mozambique requires pre-shipment
inspections for products on the restricted goods list, including vegetable foodstuffs and
animal and animal product.
All products exported to Angola must be accompanied by pre-shipment documentation
from the country of origin and must undergo additional inspections on arrival in Angola.
Currently Angola requires an inspector to be present at the time of offloading of milk and
meat-based products, fruit and vegetable products and juices to draw samples per load, line
item and batch. This can amount to approximately 300 samples per container, which have to
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be processed and tested while the rest of the cargo remains in quarantine until testing is
complete.
3.2.3.4
Lack of physical infrastructure
Various countries in SADC are landlocked (Botswana, Lesotho, Malawi, Swaziland, Zambia
and Zimbabwe) in consequence of which road and rail transport networks become essential
for the transportation of products throughout the region. However, high transaction costs
due to inadequate and unreliable transport infrastructure in most member states results in
inefficient rail and road transport.
Various ports in the member countries are unable to handle containers exceeding 6 metres,
which dramatically limits exporters in using the most cost-effective way to transport large
volumes of goods throughout the region. Road networks in Malawi are not maintained,
increasing transport costs and time delays for products transported through the country.
3.2.3.5
Other NTBs
The participation of national governments, parastatals and monopolies in the trading systems
is also prevalent in many SADC member states. This includes the operation of borders and
ports by government parastatals, local content requirements for the protection of domestic
industries and trading of specific goods by government monopolies.
In Zambia, the exportation of maize can only take place through single channel marketing,
while the importation of maize in Zimbabwe takes place via state trading government
monopolies. The Botswana government has a local content requirement for the production
of grains (maize and sorghum) prior to import permits being issued. The government trade
restricting measure is a 40:60 procurement rule. Grain processors need to produce at least
40 percent of their maize and sorghum domestically before an import permit will be
awarded for the remaining 60 percent.
Import fees and charges applicable in the SADC region include toll fee variation among
countries, an import levy of 15 percent on wheat by Botswana, high road levies and a
20 percent surcharge on the importation of chicken products and eggs by Malawi. Zambia
also charges a levy of 5 percent, additional to value-added tax, on all agricultural imports
from all other SADC members.
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The use of police roadblocks causes time delays for products being transported by road
which can have a significant impact on the quality of agricultural products available in the
region. In Tanzania, policy checkpoints stop all commercial vehicles at various point on all
the major highways causing time delays and encouraging bribery and corruption.
4.
Addressing NTBs in the Tripartite FTA and member states
The legal instruments of COMESA, the EAC and SADC, as well as the Draft Tripartite
Agreement, provide for the elimination of NTBs and prohibit the implementation of any new
NTBs. The December 2010 Draft Tripartite Agreement and Annex 14 to the Draft
Agreement provide for a common and coordinated mechanism to eliminate NTBs within the
Tripartite Territory. The three RECs also have different mechanisms to monitor and report
progress in the elimination of NTBs. However, the Draft Tripartite Agreement calls for a
coordinated effort to identify and eliminate NTBs in the region.
4.1
Legal Framework
4.1.1 SADC
Article 6 of the SADC Trade Protocol requires all member countries to adopt and
implement policies to eliminate current NTBs in terms of intra-SADC trade. Member
countries must also refrain from imposing any new NTBs on intra-SADC trade.
4.1.2 COMESA
The COMESA Treaty Article 45 calls for the removal of NTBs on trade among member
states. Article 49 requires member states to remove all those intra-COMESA NTBs which
were in place when the treaty entered into force and to refrain from imposing any new
restrictions and prohibitions on member countries. However, intra-COMESA quantitative
restrictions or prohibitions are allowed for a limited time period to protect an infant
industry.
4.1.3 EAC
In Article 75(5) of the EAC Treaty (as amended in December 2006 and August 2007) and
Article 13 of the East African Customs Union Protocol, member countries are instructed to
remove all intra-EAC NTBs and required not to impose any further NTBs on the imports of
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the member states. Article 13(2) of the Customs Union Protocol calls for a mechanism to
identify, monitor and remove NTBs within the Customs Union. However, there is no
specific provision in the EAC Common Market Protocol dealing with the elimination of
NTBs within the EAC.
4.1.4 Tripartite FTA
Article 4(1) of the December 2010 Version of the Draft Tripartite Agreement states that the
elimination of tariff and non-tariff barriers to trade in goods is one of the specific objectives
of the Tripartite member states.
Article 10 of the Draft Agreement requires all Tripartite member states to remove all NTBs
on intra-Tripartite trade and not to impose any new trade barriers. According to Article
10(2) the Tripartite recognises those reporting, monitoring and eliminating mechanisms
which have been established by the three RECs in their individual capacity, but undertake to
harmonise these into a single mechanism in terms of Annex 14 to the Draft Agreement.
4.2
Regional mechanisms for addressing NTBs
4.2.1 SADC
A complaint regarding NTBs is channelled through the National SADC Committees and
copied to the SADC Secretariat. It is then the responsibility of the Secretariat to follow up
on the removal of the NTB with the relevant member countries. If a resolution is not found
the case is forwarded to the Task Team on NTBs. If this fails an expert can be appointed to
arbitrate the dispute, and as a last resort parties can refer the dispute to the SADC Dispute
Settlement Mechanism.
4.2.2 COMESA
The National Monitoring Committees (NMCs) are responsible for the resolution of
complaints regarding NTBs and are serviced by the National Enquiry Points (NEP).
Complaints pertaining to NTBs are sent from the complainant via the NEPs in each member
country to the Secretariat which submits it in the contravening member state. Firstly, the
complaint is addressed on a bilateral level; if it cannot be resolved between the member
states the case is referred to a meeting of the policy organs of the member states.
Thereafter it is referred for arbitration and ultimately referred to the Court of Justice.
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4.2.3 EAC
The NMCs in each member country consist of public and private sector stakeholders and
are usually chaired by the ministry responsible for EAC affairs in the country. The NMC is
responsible for addressing all national and regional NTBs on a bilateral level and what cannot
be resolved at this level is referred to the Council of Ministers.
4.3
Common mechanism for the elimination of NTBs in the Tripartite FTA
Annex 14 of the Draft Tripartite Agreement requires each Tripartite member country to
establish NMCs and NEPs. One of the main functions of the NMCs is the resolution of NTB
complaints while the NEPs are responsible for implementing the Tripartite framework for
the elimination of NTBs. Annex 14 also requires a Tripartite NTB Monitoring Unit to be
established to coordinate the elimination of NTBs among members. The tools available for
reporting and monitoring NTBs in the Tripartite are a form of NTB reporting and a public
web-based NTBs monitoring mechanism.
The web-based system allows the public to register on the website (www.tradebarriers.org)
and to report an NTB and track any NTB complaint which has been made. According to the
mechanism a total of 317 complaints have been registered of which 197 complaints have
been resolved and 120 complaints are still in the process of being resolved. The mechanism
is accessible to economic operators, academic researchers and other interested parties and
allows for enhanced transparency and easy follow-up from the complaint being registered to
being resolved. After a registered user registers a complaint, the System Administrator
reviews the complaint. If the complaint is accepted as a valid NTB the National Focal Point is
assigned to process and resolve the complaint.
The other method for reporting an NTB is to submit a complaint form reporting NTBs and
other barriers. This form is sent to the National Focal Point of the reporting country,
depending on the sector the complainant belongs to. Most Tripartite member states have
both a public and private sector National Focal Point. If a trader in South Africa, for instance,
wants to raise a complaint, the form will have to be forwarded either to the Department of
Trade and Industry (public sector) or Business Unity South Africa (private sector). However,
countries like Angola, Eritrea and the DRC only have national focal points, mostly the
Ministry of Trade, which receives complaints from both the public and private sector.
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The aim of both these mechanisms is to quickly resolve NTBs between and among member
states through bilateral consultations. However, if the bilateral consultations are unsuccessful
a facilitator, normally the Tripartite Secretariat, is appointed to assist in resolving the
dispute. If the facilitator is unsuccessful in producing an amicable resolution the dispute must
be resolved in accordance with Annex 13 of the Draft Tripartite Agreement, the Dispute
Settlement Mechanism.
5.
Conclusion
Multilateral negotiations and regional integration efforts have made significant progress in
lowering tariff barriers to global and regional trade. However, the lesson from successful
regional integration experiences in the world is that addressing tariff barriers is not sufficient
to enhance regional trade and development. In order to further regional trade, countries
must also address NTBs, such as restrictive technical regulations and standards, inadequate
and inefficient physical infrastructure and cumbersome customs procedures and
administrative requirements.
The trade barriers currently prevailing in COMESA, the EAC and SADC are widespread in
the effect they have on the cost of regional trade and the access to regional markets. These
NTBs impose unnecessary costs for producers, limiting trade and raising the prices for
consumers. Import bans and time delays also create uncertainty regarding market access and
deter possible investment. Fragmented borders limit the possibility of creating regional
production chains which countries can utilise to exploit their competitive advantage in
product-specific and intra-industry trade.
A wide variety of NTBs is therefore persistent in the southern and eastern African region.
Possibly one of the biggest possible challenges faced by the Tripartite FTA is rules of origin.
This will require either the harmonisation of RoO among the regional groupings or the
development of a new set of RoO, either based on one of the existing arrangements or RoO
which are completely redesigned. Other types of existing NTBs must also be resolved, while
limiting the development of any new barriers among members. Bureaucratic requirements
need to be reduced while border management producers are streamlined.
This will require increased transparency in the NTBs existing among member states and an
efficient utilisation of the Tripartite Reporting, Monitoring and Elimination Mechanism. This
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has the potential of lower border-crossing time and reduced transport costs along the main
corridors in southern and eastern Africa. Limiting those NTBs prevalent in the region will
contribute to deeper regional integration possibilities and enhance development in the
Tripartite Territory.
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Tripartite Coordination Mechanism. 2009. Draft report on establishing the Tripartite Free Trade
Area Version of November 2009.
Tumuhimbise, C., and Ihiga, S. 2007. A survey of non-tariff barriers that affect Ugandan imports
and exports within EAC and COMESA countries. [Online]. Available:
http://www.tradebarriers.org.
World Bank. 2008. Non-tariff measures on goods trade in the East African Community: Synthesis
Report. [Online]. Available:
http://siteresources.worldbank.org/INTAFRREGTOPTRADE/Resources/EAC_NTMs_Report
_Oct_10_2008.pdf.
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Chapter 7 – Non-tariff barriers affecting trade in the COMESA-EAC-SADC Tripartite Free Trade Agreement
Legal Texts
COMESA Treaty
Draft Agreement Establishing the COMESA, EAC and SADC Tripartite Free Trade Area and
Annex 1 November 2009
Draft Agreement Establishing the COMESA, EAC and SADC Tripartite Free Trade Area and
Annex 14 Revised December 2010
East African Community Treaty
Protocol on the Establishment of the East African Customs Union
SADC Trade Protocol
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Authors’ Profiles 2011
Authors’ Profiles 2011
Sandrey, Ron is an associate at the Trade Law Centre for Southern Africa (tralac) and is
Professor Extraordinaire at the Department of Agricultural Economics at the University of
Stellenbosch. He came to Africa in 2005 following a career in New Zealand as an economist
with the New Zealand Ministry of Foreign Affairs and Trade and the Ministry of Agriculture
and Fisheries in that country. Since coming to South Africa he has worked extensively on
trade and trade related issues in Southern Africa. He holds a PhD in Economics from
Oregon State University. Contact: [email protected].
Jensen, Hans G. is a researcher at the University of Copenhagen, Institute of Food and
Resource Economics, International Economics and Policy Division. His field of expertise has
focused on the Common Agricultural Policy, World Trade Organisation, European Union
enlargement and general equilibrium modelling for the past ten years. He was awarded the
Global Trade Analysis Project Research Fellow distinction in recognition of his significant
contribution to the development of the GTAP database and is a member of the GTAP
advisory board. He has published international articles focusing on the EU enlargement, the
EU sugar regime, and the decoupling of domestic support in the EU among others. He has
also had his work published in various book chapters. Contact: [email protected].
Vink, Nick joined the University of Stellenbosch as professor in agricultural economics in
1996 after 11 years at the Development Bank of Southern Africa. He is an agricultural
economist specialising in agricultural and rural development policy, land reform and
empowerment, agricultural marketing, tax and international trade issues.
He has been
involved in a range of official government Commissions and Committees of investigation,
inter alia in the deregulation of agricultural marketing and the provision of rural financial
services. He has also consulted widely to national departments (Water Affairs and Forestry,
Agriculture, Land Affairs, Labour, Trade and Industry and the Treasury) as well as provincial
departments. He has also consulted internationally, among others for the World Bank, IFPRI,
FAO, USAID, DFID, etc. He serves on the Board of the Centre for Rural Legal Studies. He
has published widely, both nationally and internationally, with more than 110 academic
publications to his credit.
Fundira, Taku is a researcher whose focus is on economic and trade policy issues. His
interests include work related to agricultural trade, quantitative trade data analysis as well as
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Authors’ Profiles 2011
trade and industrial policy analysis. He is also involved in capacity building on trade policy
tools in Southern Africa.
Viljoen, Willemien holds a BComm Honours degree in Economics and a Bachelor of Laws
degree (LLB) from the University of Stellenbosch. She has been a researcher at tralac since
February 2009 with research interests in trade data and modeling, international arbitration
and mediation, regional integration and international trade policy.
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