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Potential effects of the African Continental Free Trade Area (AfCFTA) on African agri-food sectors and food security

This study was conducted by the European Union’s Joint Research Centre (JRC) at the request of the Department of Agriculture, Rural Development, Blue Economy and Sustainable Environment (DARBE) of the African Union Commission (AUC). The study provides insights into several key areas of African economic integration, even though not all negotiations on the implementation of the African Continental Free Trade Area (AfCFTA)are concluded yet, especially on the common external tariff regime. The AU Commission will utilise the results of the study to organize discussions with regional economic communities (RECs).

The JRC developed this study within the recently created Pan-African Network for economic Analysis of Policies (PANAP), established in 2019 under the aegis of the African Union (AU) - European Union (EU) partnership. PANAP is a network of academic, research and institutional partners developing research on agro-economics and policy issues. PANAP aims to strengthen the liaison between researchers/scientists and policymakers in Africa, and to stimulate their cooperation on selected topics linked to policy priorities that reinforce the stability of African agriculture and food sectors.

The report was presented at the 17th Comprehensive Africa Agriculture Development Program (CAADP) Partnership Program in November 25th 2021.

This interactive infographic complements the homonym scientific report with tools to explore freely data visualizations and to discover facts within the model results.

UPDATE: the data in the repository was changed after the publication of scientific article published in Global Food Security (Simola, et al. 2022 Economic integration and food security – The case of the AfCFTA, Global Food Security, 35, 100651, https://doi.org/10.1016/j.gfs.2022.100651). Some of the results have changed slightly due to application of updated population growth trajectories. Also an error in the calculation of Number of Undernourished People and Prevalence of Undernourishment indicators is fixed, which corrects the effects larger than in the publication. The other changes are small, and the original results can be found in a separate repository.


All results

This section gives an overall view of the AfCFTA impact on various indicators. The more detailed results can be found in the full dashboard, which includes individual regions, commodity and labour skill breakdown, and additional visualizations.

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Trade by region

This section gives an overall view of the AfCFTA impact on trade flows between specific regions. The more detailed results can be found in the full dashboard, which includes individual partner region and commodity breakdowns, and additional visualizations.

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The study

Introduction

Signed by 54 of the 55 African Union (AU) member states, the African Continental Free Trade Area (AfCFTA) agreement entered into force on 1 January 2021 with the promise to boost economic development and to improve Africa’s position in global trade by leveraging economies of scale and the cost decrease of imported products. This study provides a comprehensive description of the free trade agreement’s impacts by focusing on the food sectors and on food security at multiple geographic scales (continental, regional and national).

The study employed a Global Trade Analysis Project (GTAP)-based economic model, which captured the impacts of the AfCFTA on trade flows at a bilateral level and across a wide range of merchandise and service groupings. The results cover a period up to 2035 and are evaluated against a multiannual socioeconomic development baseline. With the tariff offers still under consideration by the signatory countries, the analysis includes alternative liberalisation scenarios implemented across the continent, which have the objectives of government revenue maximisation on one hand, and the promotion of food security, of intermediate products or of revealed comparative advantage products, on the other.

Another area of uncertainty addressed in this report is the capacity of the trade agreement to reduce the costs related to non-tariff measures (NTMs). In the African context, the existing ad valorem estimations, although highly uncertain, highlight a higher burden of these costs on trade than the burden of tariffs. Therefore, the analysis includes results for both a ‘tariff-only’ reduction under the AfCFTA and a ‘tariff and NTMs’ liberalisation scenario. The latter anticipates that by 2035 the NTM-related costs for all intra-African trade will decrease by 50%, whereas those for imports and exports from and to AfCFTA third countries will decrease by 25%, to reflect wider gains from the harmonisation of standards at the border.

Scenarios

The study examines alternative policy scenarios that consider both tariff cuts alone and combined with NTMs reductions. The modelled tariff cuts take into account the AfCFTA tariff cut modalities, and the study addresses the uncertainty on the final tariff offers by constructing four alternative scenarios. The NTM reductions being even more uncertain, the study resorts to use a plausible level of NTM reductions in the absence of reliable estimates of the actual ones.

Import tariffs

As final tariff liberalisation offers from the AU member states detailing the exact tariff lines and rate cuts were not available at the time of drafting the report, several tariff liberalisation scenarios are constructed for each country separately while respecting regional trade blocs. Specifically, the scenarios are constructed utilising the information on the modalities on liberalisation of trade in goods agreed in the AfCFTA negotiations to date. The scenario construction allocates tariff lines into four lists of products: non-sensitive, non-sensitive G7, sensitive and excluded. The details of the liberalisation commitments for each of the four lists differ by country category – non-LDC, LDC and G7 – and are summarised in Table 1. For the list of excluded products, the AfCFTA adopts a double-qualification approach, limiting the number of tariff lines to 3% (or 156 lines) of all HS6 tariff lines, as well as limiting the value of intra-AU trade covered by selected lines to 10%, measured using the average import value over the period 2014–2016 or 2015–2017. The basis for the tariff liberalisation is the MFN rates applied in 2019, the year the AfCFTA came into force (Lunenborg, 2019).

#Non-sensitive products#Sensitive products#Excluded products Coverage rules#At least 90% of tariff lines#Up to 7% of tariff lines#Up to 3% of tariff lines covering less than 10% of value of imports from the AU Non-LDCs#Cut by 100% over 5 years#Cut by 100% over 10 years#No cut LDCs#Cut by 100% over 10 years#Cut by 100% over 13 years, start may be delayed to year 6#No cut G7#85% of tariff lines cut by 100% over 10 years; 5% of tariff lines cut by 100% over 15 years, start may be delayed to year 11#Cut by 100% over 13 years, start may be delayed to year 6#No cut
Table 1. AfCFTA liberalisation commitments and time frames.

Member states are tabling liberalisation offers individually unless they belong to an REC that is applying a common external tariff. The EAC, ECOWAS and the Southern African Customs Union (SACU) are customs unions, and the Central African Economic and Monetary Community (CEMAC) is in a late stage of forming one, so these four regional groupings are assumed to each table common offers (United Nations Economic Commission for Africa, 2018; Lunenborg, 2019). Considering some ambiguity about the negotiation and application details of the AfCFTA (Lunenborg, 2019), the following additional assumptions are made in the present analysis: the four customs union RECs are classified as non-LDCs (although they frequently include LDC countries), and each AU member state applies the same set of tariffs to all other AU member states, except if they belong to the same customs union.

Non-tariff measures

The AfCFTA will harmonise rules and regulations in many areas of merchandise and services trade (SPS standards, TBTs, etc.) and thereby reduce the cost of trading not only for trade within the AU but also for trade outside the AU. In the absence of information on how NTMs will be reduced by the AfCFTA, we assume that the scope of the AfCFTA facilitates a reduction of 50% in NTMs on merchandise and services trade for all intra-AU states. The AfCFTA is also assumed to reduce the cost of trading with countries outside of the AU as a result of greater standardisation and harmonisation of rules and regulations and a corresponding reduction in costs of compliance with foreign rules and regulations. NTMs for all imports and exports between AU and non-AU countries are assumed to decrease by 25%. The NTMs are reduced linearly, starting from the year 2020 and completing in 2035.

NTMs on merchandise are distinguished between technical measures (SPS and technical measures) and non-technical measures (contingent trade measures, quantitative restrictions, price controls, finance measures) (see Kee and Nicita, 2016), and are treated differently in the implementation of the simulation scenarios. All NTMs on services are assumed to be technical measures.

To address variation in these types of NTMs and their effects, we assumed that the technical measures are cost generating and represented them as iceberg costs in the model, and that the non-technical measures are rent generating and represented them as ad valorem tariff equivalents. The NTM reductions in the scenarios are distributed accordingly.

Selection of product lines for the exclusion list

The construction of realistic tariff line scenarios, as they might emerge at the end of the AfCFTA implementation period for all member states after 15 years, involves selecting tariff lines according to the four lists of products considered here, as a hierarchical process with three steps: in step 1, a maximum of 3 % of tariff lines for the list of excluded products are selected; in step 2 – only in the case of G7 countries – a maximum of 5 % of non-sensitive tariffs are selected, with an extended and optionally delayed liberalisation period; and in step 3, a maximum of 7 % of sensitive products are selected, with an extended and optionally delayed liberalisation period. All remaining tariff lines are allocated to the list of non-sensitive products.

Two alternative assumptions about rules guiding governments’ tariff line selection were considered for constructing scenarios. The first rule is maximisation of tariff revenue retained and the second rule is maximisation of the political economy-based index developed by Jean et al. (2008), which combines preferences for retaining tariff revenue with preferences to continue the protection of products with high tariff rates. However, as differences in selected tariff lists turned out to be small, the decision was taken to proceed with the tariff revenue maximisation rule only.

In addition to the above guiding rule, four options regarding a primary objective for an import liberalisation strategy are considered, which imply complete liberalisation of a defined product group before applying the tariff revenue maximisation approach to the remaining tariff lines. These options are:

  1. no additional strategy (Tarrev),
  2. improving food access by liberalising agri-food products (Agrifood),
  3. promoting industrialisation by liberalising intermediate input products (Interm),
  4. increasing healthy competition by liberalising industries with revealed comparative advantage within the AU (RCA).

Governments’ selection process in each hierarchical step as outlined above is formulated as a combinatorial optimisation problem, maximising the tariff revenue subject to the coverage limits summarised in Table 1. For example, the list of excluded products has the limits of a maximum of 3 % of tariff lines and of a maximum 10 % share of intra-AU import value covered. Additional protection strategies are implemented by liberalising sets of tariff lines beforehand and excluding these from the tariff revenue-maximising selection procedure. For (b), all agri-food products are liberalised, for (c), all lines relating to intermediate inputs as categorised by the UN broad economic categories are liberalised, and for (d), all lines of products with a revealed comparative advantage (see Balassa, 1965) greater than 1 are liberalised.

For the purpose of the tariff line selection, the four RECs are each included as a single region instead of their member countries being included individually. It is assumed that REC members all get equal weight in the decision about the common tariff liberalisation offer, which is achieved by using democratic weighting during the calculation of average tariff revenue shares and import shares for the maximisation criteria. Note that since the double-qualification criteria are applied to each REC as a whole, individual countries might stay below the 90 % liberalisation target. Moreover, it is assumed that the formation of the four customs unions (EAC, ECOWAS, SACU and the CEMAC) is completed before the start of the AfCFTA so that the import tariffs within each of these four RECs are zero.

All tariff lines with zero imports are excluded during the maximisation process, but such tariff lines could be added arbitrarily afterwards to fill the limit of 156 lines for the exclusion list. This, however, would have no effect in the present study.

Regarding the timing of tariff line liberalisation, again tariff revenue maximisation is assumed so that liberalisation is delayed as much as possible according to the modalities.

In CGE models, supply and demand of commodities and endowments meet in markets, which are perfectly competitive and clear through price adjustments. MAGNET is a recursive-dynamic model, in which each period inherits an updated database incorporating the previous period’s solutions. Capital stock develops as a result of investment demand, which is determined by regional expected rates of return. The model closure thus corresponds to the GTAP expected rate of return closure, in which the regional savings rates are exogenous, and the trade balances adjust to accommodate the equality between savings and investments. Labour supply is determined exogenously by population projections.

To characterise the peculiarities of agricultural markets, the model accounts for the heterogeneity of land usage by agricultural activity; a regional endogenous land supply function; the sluggish mobility of capital and labour transfer between agricultural and non-agricultural sectors with associated wage and rent differentials; and the inclusion of explicit substitution possibilities between various feed inputs in the livestock sectors.

Trade is modelled in a way that domestically produced goods can be sold either on the domestic market or to other regions of the world. Similarly, domestic intermediate, private household and government demand for goods can be satisfied by domestic production or by imports from other regions of the world (i.e. the ‘Armington assumption’). The Armington assumption implies that an increase in the domestic price relative to import prices leads to an increase in demand for imports relative to domestic goods. Similarly, if imports from one source country become more expensive, these will be substituted with imports from another, cheaper, source country. Other regions are accounted for with their own import and export taxes. Sourcing of imports happens at the border, after which– based on the resulting composite import price– the optimal mix of import and domestic goods is derived.

Caveats to the approach

Economic models provide a conceptual framework that enables the representation of the economy in a structured but schematic and simplified manner. They cannot reproduce the reality in its full complexity and thus have shortcomings and limitations, which should be appreciated, and which affect the results of the studies based on such models. Some more detailed caveats merit a mention.

MAGNET enables policy experiments to be conducted, in which a reference scenario or baseline is first simulated over a future period and then, after changing one or more underlying assumptions (e.g. about policy settings or about exogenous macroeconomic developments, weather trends, etc.), a new scenario incorporating these changes is run over the same period. A comparison of the new scenario with the reference scenario at a given point in the simulation period, usually in terms of percentage differences, establishes the direction and relative magnitude of the impacts on all the endogenous variables of the change that is depicted in the hypothetical scenario at that point in time. In other words, the model is intended to compare, for the same moment in time (i.e. holding time constant), the outcomes prevailing in two or more different hypothetical ‘states of the world’ that might prevail at that point in time.

Although MAGNET could be used to project individual values of all variables, it must be stressed that it is not a forecasting model and users should be aware that the values projected may be unreliable as to what will happen in that year. Although this type of model is calibrated to fit a given year closely, its solutions become less reliable the further into the future it is used to simulate outcomes. Given the substantial number of assumptions, estimated or calibrated parameters, and stylised specification features that these models assemble, each of which is ‘correct’ only up to an (unknown) probability, it is impossible to establish confidence intervals or margins of error around individual projected numbers.

A further limitation relates to the coverage and the disaggregation of the countries and of the agricultural products in the database used to calibrate the model. The CGE model MAGNET has a comprehensive coverage of the world and of the economy, and thus of the agri-food sector and beyond. However, some of the most important cash crops and processed agricultural products that fall in another food category cannot be included in this analysis owing to data limitations. These products, which include coffee, cocoa, tea, and other cash crops, are typical flagship export products for various African countries. This limitation leads to underestimating the trade gains for the African agri-food sector in a broad sense. In addition, not all the African countries are included as individual countries in the database and some of them only appear aggregated in composite regions.

MAGNET also suffers from a typical caveat that applies to all CGE models employing the constant elasticity of substitution assumption: the so-called small share problem. The constant elasticity of substitution treatment underestimates trade creation opportunities when the import flow in the benchmark data is ‘small’. When the GTAP benchmark data import share is ‘small’, significant tariff reduction might induce a fall in the price but, even when coupled with a large trade elasticity, changes to bilateral imports will still be negligible (Philippidis et al., 2014). This is the case in many trade flows among African countries within the GTAP database. This caveat also refers to trade in new products that might appear on the market in the subsequent years. When the initial trade in these products is zero, it will remain zero in the model. This could be particularly relevant in the context of a rapid industrialisation process in African countries and their potential to enter new industries and produce goods they are not producing now.

Another notable caveat relates to the adjustment of the database to reflect the most up-to-date tariff rates between countries. As mentioned earlier, we apply the Altertax method by Malcolm (1998) for this purpose. In addition, the same adjustment is required for NTMs when they are modelled as AVEs rather than as iceberg costs. Typically, the required Altertax adjustment is much larger for NTMs than for tariffs. Therefore, the database deviates more from its original form in the case of the NTMs. The consequent changes in trade and income flows are a major weakness of the AVE approach. In addition, the Altertax method assigns all the rent generated by the NTMs as government income, which is hardly realistic and can lead to misleading changes in tariff revenues.

Database and aggregations

This study employs a fully consistent and academically recognised global database based on contributions from members of the GTAP network and constructed by the GTAP team at Purdue University, United States (Aguiar et al., 2019). Version 10 of the GTAP database contains a complete record of all economic activity (i.e. production, trade, primary factor usage, final and input demands, taxes, trade tariffs, and transport margins) for 65 activities and 141 regions for the year 2014. Our analysis employs an aggregation of the database that catches the most salient features of agri-food industries in various African countries. The aggregated database includes 40 tradable commodities and 36 regions, of which 29 are in Africa (see Figure 1 for details of regional aggregation). We make the results available in the REC level also. However, this poses a challenge since some of the countries that belong to an aggregated GTAP region belong to several RECs. In those few cases, we needed to deviate from the actual REC composition by assigning the aggregated region to its REC that was economically the largest. However, the deviation is small, and we believe that our REC aggregation gives an adequate view of the REC-level results. See the section on regional aggregations for more details.

Commodity aggregation in MAGNET simulations


Commodity;MAGNET codification;A;B;C;D;E;F;G;H Paddy rice;pdr;X;;X;;;;; Wheat;wht;X;;X;;;;; Cereal grains not elsewhere classified (n.e.c.);gro;X;;X;;;;; Horticulture;hort;X;;X;;;;; Oilseeds;osd;X;;X;;;;; Sugar (cane and beet);c_b;X;;X;;;;; Plant-based fibres;pfb;X;;X;;;;; Crops n.e.c.;ocrops;X;;X;;;;; Cattle;ctl;X;;X;;;;; Other animal products;oap;X;;X;;;;; Raw milk;rmk;X;;X;;;;; Wool, silkworm cocoons;wol;X;;;;;;; Fishing;fish;;;X;X;;;; Forestry;for;;;;X;;;; Coal;coa;;;;;X;;; Oil;oil;;;;;X;;; Gas;gas;;;;;X;;; Other mining extraction;oxt;;;;;X;;; Cattle meat;cmt;;X;X;;;;; Meat products n.e.c.;omt;;X;X;;;;; Vegetable oils and fats;vol;;X;X;;;;; Dairy products;mil;;X;X;;;;; Processed rice;pcr;;X;X;;;;; Sugar;sugar;;X;X;;;;; Other food products;ofd;;X;X;;;;; Beverages and tobacco products;b_t;;X;X;;;;; Animal feed;feed;;X;X;;;;; Light manufacturing;LightManuf;;;;;;X;; Lumber;lum;;;;;;X;; Paper and paper products;ppp;;;;;;X;; Petroleum and coal products;petro;;;;;;X;; Manufacture of chemicals and chemical products;chm;;;;;;X;; Fertilisers;fert;;;;;;X;; Manufacturing;Manuf;;;;;;X;; Electricity;ely;;;;;;;X; Gas distribution;gas_dist;;;;;;;X; Wholesale and retail trade;trd;;;;;;;;X Transport;Trnsp;;;;;;;;X Food services;foodserv;;;;;;;;X Services;serv;;;;;;;;X

A = primary agricultural commodities, B = processed foods, C = food commodities, D = renewable resources, E = non-renewable resources, F = manufacturing, G = utilities, H = services.

Regional aggregation of the African countries/territories


;GTAP;MAGNET;COMESA;EAC;ECCAS;ECOWAS;SADC;UMA Egypt;egy;egy;X;;;;; Morocco;mar;mar;;;;;;X Tunisia;tun;tun;X;;;;;X Algeria;xnf;xnf;;;;;;X Libya;xnf;xnf;(X);;;;;X Western Sahara;xnf;xnf;;;;;;X Benin;ben;xwf;;;;X;; Burkina Faso;bfa;bfa;;;;X;; Cameroon;cmr;cmr;;;X;;; Côte d’Ivoire;civ;civ;;;;X;; Ghana;gha;gha;;;;X;; Guinea;gin;xwf;;;;X;; Senegal;sen;sen;;;;X;; Togo;tgo;xwf;;;;X;; Cabo Verde;xwf;xwf;;;;X;; The Gambia;xwf;xwf;;;;X;; Guinea-Bissau;xwf;xwf;;;;X;; Liberia;xwf;xwf;;;;X;; Mali;xwf;xwf;;;;X;; Mauritania;xwf;xwf;;;;O;;(X) Nigeria;nga;nga;;;;X;; Niger;xwf;xwf;;;;X;; Saint Helena;xwf;xwf;;;;X;; Sierra Leone;xwf;xwf;;;;X;; Central African Republic;xcf;xcf;;;X;;; Chad;xcf;xcf;;;X;;; Congo;xcf;xcf;;;X;;; Equatorial Guinea;xcf;xcf;;;X;;; Gabon;xcf;xcf;;;X;;; São Tomé and Príncipe;xcf;xcf;;;X;;; Angola;xac;xac;;;X;;X; Democratic Republic of the Congo;xac;xac;(X);;X;;X; Ethiopia;eth;eth;X;;;;; Kenya;ken;ken;X;X;;;; Madagascar;mdg;mdg;X;;;;X; Malawi;mwi;mwi;X;;;;X; Mauritius;mus;mus;X;;;;X; Mozambique;moz;moz;;;;;X; Rwanda;rwa;rwa;X;X;X;;; Tanzania;tza;tza;;X;;;X; Uganda;uga;uga;X;X;;;; Zambia;zmb;zmb;X;;;;X; Zimbabwe;zwe;zwe;X;;;;X; Burundi;xec;xec;X;(X);(X);;; Comoros;xec;xec;X;;;;(X); Djibouti;xec;xec;X;;;;; Eritrea;xec;xec;X;;;;; Mayotte;xec;xec;X;;;;; Seychelles;xec;xec;X;;;;(X); Somalia;xec;xec;X;;;;; Sudan;xec;xec;X;;;;; South Sudan;—;—;;(X);;;; Botswana;bwa;bwa;;;;;X; Namibia;nam;nam;;;;;X; South Africa;zaf;zaf;;;;;X; Eswatini;xsc;xsc;(X);;;;X; Lesotho;xsc;xsc;;;;;X;

NB: The GTAP and MAGNET columns show the GTAP and MAGNET regional aggregations of African countries, respectively. The remaining columns show how the countries are aggregated to the various RECs: COMESA, EAC, ECCAS, ECOWAS, SADC, and UMA. The regional label X means that a country belongs to that REC in reality and in model results. The label (X) means that the country belongs to that REC in reality, but not in the model results. The label O means that the country belongs to the REC in the model results but not in reality. The discrepancies between the real REC memberships and the modelled ones are due to the regional aggregation of the GTAP model. The rest of the world is aggregated in the following seven regions: the EU-27, the United Kingdom, the rest of Europe, North and South America, Asia, the Middle East, and the rest of the world. South Sudan is not included in the most recent version of the GTAP database and was thus not part of the analysis.

Figure 1. Regional aggregations of African countries used in the simulations. The following regions are not visible on the map: Mayotte (included in Rest of Eastern Africa).

Baseline

The model baseline is based on the Shared Socioeconomic Pathways (SSPs), which are long-term projections of the world economy produced by various integrated assessment models. The baseline is based on SSP2, which is the middle-of-the-road scenario (Fricko et al., 2017). The baseline is driven by the following exogenous factors: population growth and GDP growth by region, endowment demand by region and endowment category (skilled and unskilled labour, capital, and natural resources), and productivity of land by region and agricultural sector. Population growth and GDP growth are based on the SSP2 scenarios, whereas the endowment growth is derived from them. The overall labour supply is defined by the population growth, and the split between skilled and unskilled labour comes from educational projections from the Wittgenstein Centre’s data on global educational attainment (Goujon et al., 2016). The capital stock is assumed to have the same growth rate as the GDP, whereas the natural resource use has a quarter of that growth rate. The baseline starts from the 2014 GTAP database version 10 (Aguiar et al., 2019), and we apply 5-year simulation steps starting from 2020 until the end of the AfCFTA transition period in 2035. The 2014 database is updated to 2020 with GDP and population projections coming from the IMF’s World Economic Outlook (IMF, 2020) projections.

We assume that there are no changes in tariff rates or NTMs among both AfCFTA member countries and non-AfCFTA countries. The initial values of the tariffs are adjusted using the Altertax method (Malcolm, 1998) to 2014 values obtained from the MAcMap database. The NTMs are modelled as a mix of ad valorem tax equivalents and iceberg costs. The Altertax adjustment target equals the sum of actual tariffs and the AVE share of NTMs in the NTM scenarios.

Main findings

The AfCFTA is likely to further strengthen the positive economic development across the continent. The gross domestic product (GDP) effects, although moderate, are predicted to be positive across all the countries and regions in Africa. However, the outcomes for trade, notably intra-African, and income are more remarkable. At a continental level, trade is predicted to expand in terms of total volume but also across commodity groups and destination regions. This intra-African trade growth is predicted to result in a reduction in trade concentration away from a small set of unprocessed or semi-processed commodities. Although it is expected that the key commodities specific to each country will continue to be important, the general diversification of exports enables an increased protection of national economies against volatility in the global bulk commodity markets.

From a food security perspective, the trade agreement is expected to lead to a rise in food availability and food consumption. However, agricultural production is predicted to become more concentrated in a few regions. For primary agricultural commodities, the output is predicted to increase most in Southern Africa but to decrease in central areas and many sparsely populated countries. A similar outcome can be seen for processed foods. There are a few exceptions to this pattern on a more detailed commodity level. Cereal output is predicted to decrease almost everywhere, the exceptions being increased wheat output in Eastern Africa and increased paddy rice output in Southern Africa. Oilseed output is predicted to increase, especially in Eastern Africa. Dairy production is predicted to grow notably in Nigeria and Côte d’Ivoire. The expansion of beef cattle production is predicted to be concentrated in Southern Africa, and the output of other meats is expected to increase, mostly in Southern and Eastern Africa. The output of fish is predicted to increase throughout Africa. The supply of services and food services is also predicted to increase throughout Africa, reflecting the change in income levels. The outcomes for food prices are mixed, with predicted decreases in prices in some regions and increases in others. The predicted increase in prices for some commodities and countries marks the need to address the issue of food affordability for low-income groups.

Both production and price changes in agri-food sectors reflect a reallocation of production means following the AfCFTA implementation as countries and regions steer trade towards products and services in which they have a comparative advantage. As a result, agricultural production at a continental level is predicted to grow less than GDP, indicating that, on average, non-agricultural sectors will become more important in the national economies and thus the share of value added in total output increases.

Overall, with tariff and NTMs liberalization intra-African trade is predicted to be 22% above the baseline level by 2035, with all regions and countries experiencing an increase in both exports from and imports to other AfCFTA members. On the exports side, the Economic Community of West African States (ECOWAS) countries are expected to have the highest growth rate, with a more uniform expansion across the other RECs. For imports, the largest growth in relation to the baseline is expected in Ghana (73%), Côte d’Ivoire (58%), Nigeria and South-Central Africa (both 50%), whereas, at the other end of the spectrum, many Southern African Development Community (SADC) countries and some Arab Maghreb Union (AMU) countries are predicted to have the lowest expansion.

At REC level, the ECOWAS is predicted to see the most significant increase in general exports (intracontinental and extracontinental), and the highest increase in export diversification away from agri-food trade. This will enable the largest increase in income per capita across the RECs (2.7% above the baseline in 2035). At the same time, the GDP gains are expected to be in line with the AU average (0.4%) and the corresponding positive welfare effect is predicted to be slightly reduced by an increase in food prices (1.0% above baseline values); nevertheless, there are differences between member countries, with Ghana and Nigeria having negligible GDP gains, whereas the other countries obtain a much greater increase (0.7–1.6% by 2035).

In the Common Market for Eastern and Southern Africa (COMESA), the predicted income expansion by 1.7% is in line with the AU average, with GDP increasing by 0.3%. The region is also predicted to have the second highest agri-food exports expansion (5.9% above baseline values), facilitated by an increase in trade flows within the continent, with intra-African exports increasing the most in Rwanda (33.5 %), Uganda (29.5 %) and Ethiopia (27.0 %).

Partially overlapping COMESA, the East African Community (EAC) and the SADC have different expected outcomes resulting from the AfCFTA implementation. The EAC is predicted to benefit from above the average GDP gains, resulting from an increase in total exports. At the same time, the EAC is predicted to have the highest increase in food imports (5.7% above the baseline) and food prices (1.4%), whereas the food consumption growth (0.6%) is predicted to be below the AU average.

The SADC is expected to have the second-highest increase in GDP facilitated by the rise in agri-food exports (6.2% above baseline values in 2035), with Rwanda and Botswana expanding the most. However, the food security gains in the region are moderate; the food consumption expansion is below the AU average (0.65% above the baseline) despite the second-highest decrease in food prices at a regional level (0.25% below the baseline), although not uniform across member states.

The Economic Community of Central African States (ECCAS) is expected to have the largest GDP, but a moderate food consumption expansion compared to the other RECs. The region has the lowest expansion of total intra-African exports (11.4% above the baseline in 2035), whereas the agri-food trade with the rest of the continent is predicted to expand above the AU average for both exports (20.3 %) and imports (28.0 %).

AMU is the REC in which the AfCFTA is predicted to have some of the lowest impacts on the economic and food security metrics. GDP, income, and trade are also estimated to be below the AU averages. The changes in trade diversification indices are lower than in the other RECs, although the countries in this region are already more integrated within the global trade networks. Nevertheless, by 2035, food exports expand at the highest rate of the RECs (5.6% above the baseline), while food consumption increases by 1%.

With very few exceptions, the tariff liberalisation results in a decrease in government tariff revenues relative to baseline levels, despite an increase in total trade volumes at the continental scale and across countries. Nevertheless, the AfCFTA stimulates general economic activity and thus indirectly generates additional fiscal revenue from other tax classes. When the effects of NTM liberalization are also included, this increase in non-tariff revenue is enough to compensate for the tariff liberalisation and to result in an overall positive impact on public finances in most countries.

Across the four liberalisation scenarios considered in this report, the one promoting trade in agri-food products results in the lowest food prices. This scenario also results in the largest increases in per capita food consumption. However, a trade-off comes from a slight reduction in the income gains. From a GDP perspective, the differences in impact across the scenarios, although small, are country and region specific and are dependent on the structure of each economy– at the REC level, the agri-food promotion liberalisation scenario leads to the highest GDP gains in the ECOWAS and the SADC, whereas a scenario promoting industrialisation through trade in intermediate products leads to better outcomes for COMESA, EAC and AMU.

An important aspect of food security is the affordability of food for the most vulnerable groups. We find that this indicator changes to positive on average, but in some regions basic food prices increase faster than the wages of low-income groups. This result indicates that due to the AfCFTA some regions could experience increase in food security risks in vulnerable groups. We also find that the negative effects can be mitigated if the regions uniformly choose to promote industrialization. Thus, our results suggest that promoting industrialization rather than low food prices is a better strategy for mitigating food security risks as it leads to a more positive income development in vulnerable groups. However, this result is not uniform across regions, and therefore non-uniform strategies, which take into consideration regional conditions, should be further investigated.

Policy recommendations

The study shows that, although food consumption increases on average throughout Africa, changes in food prices could make low-income earners more vulnerable to food security risks in some regions. There are regions where the prices of basic food items increase more than the wages on low-income groups. Therefore, governments in these regions are advised to take measures to protect the vulnerable groups. In particular, establishing food safety nets, and providing adequate income transfers, should be high on these governments’ agendas.

The study has also shown that the decisions on tariff offers can already mitigate the worst food security outcomes as the uniform adoption of promoting industrialization yields both the highest overall improvement of food security in vulnerable groups, it also avoids the worst outcomes in regions that will encounter negative consequences. Promoting low food prices in particular is not as beneficial as regards food security, since it does not increase the incomes of the African households as much as aiming for a more rapid industrialization and improvement of general value added of production do. However, as the results vary by region, the recommendation should be considered within the local context.

On the other hand, the study shows that most African economies, after the adoption of the AfCFTA, might become less dependent on agriculture while increasing the value added shares in their output and that trade in processed foods increases more than trade in primary agricultural products. These results indicate that governments, to further boost the regional integration and positive effects on agri-food value chain, should support a more vibrant food industry sector. The study further reveals that reduction in non-tariff measures has a larger positive impact than reduction in tariffs. This result means that governments should focus efforts in ensuring reduction (or total removal) of measures that hinder or limit the flow of goods across borders. Governments should invest in efforts that facilitate cross-border trade, and eliminate unnecessary red tape that hinders the integration of the African economies. Reduction of non-tariff measures along with trade facilitation are the key areas where the AU member states should focus to make the AfCFTA a success. However, the non-tariff measures in reality are more varied than our study can account for. As regards food security, the harmonization of food related measures such as sanitary- and phytosanitary measures require a particular scrutiny and consideration of food security aspects to yield an optimal outcome also for the vulnerable groups.

The study reveals that although trade liberalization results in lower tariff revenue in 2035 compared to no liberalization, the general economic activity stimulated by the AfCFTA will compensate the shortfall through increased revenues from other tax classes. Therefore, governments should not worry about initial tariff revenue reductions, but aim at facilitating the companies in adapting to a more open international trade and strengthening the trading partnerships within Africa and beyond.

Further research

Despite the scope of the present study, additional future research would be necessary to cope with some of the limitations of the current modelling approach.

Firstly, once the final AfCFTA tariff access offer is decided, the model should assess with more precision the results of the definitive market access offer instead of relying on hypothetical scenarios.

Secondly, given the structure of the database underlying the MAGNET model, the impacts of the agreement cannot be evaluated in detail either for all countries (some of them are not available within the latest GTAP version as individual countries) or at sectoral level (the commodity disaggregation is set by the structure of the GTAP database). To overcome these limitations, a linkage between the MAGNET model, a single-country CGE model and a farm-level model developed and maintained in house by the JRC is under development. These linkages should enable the results produced by the global CGE MAGNET model to be used in country-level models to produce a refined analysis, including more detailed sectoral results and impacts on a wider range of agents (e.g. urban and rural households in various regions of the countries, and farmers).

Future research should also benefit from an improved modelling approach in allocating the NTM reductions between import and export costs. Despite all these uncertainties around the analysis of the NTM reduction, the results highlight that there is a potentially large gain for the AU from an ambitious commitment to lower NTMs.

An additional stream of research should focus on the impact of a higher level of integration among African countries, including free mobility of people and/or capital.

Finally, further model enhancements should be developed to include a more refined analysis of social (nutrition, employment, migration) and environmental (emissions, resource use, footprints) indicators affected by the trade agreement and the possible interactions and impacts on food security between Africa integration and climate change issues.

Abbreviations and definitions

AfCFTA - African Continental Free Trade Area

AMU - Arab Maghreb Union

AU - African Union

AVE - ad valorem - equivalent

CEMAC - Central African Economic and Monetary Community

CGE - computable general equilibrium

COMESA - Common Market for Eastern and Southern Africa

EAC - East African Community

ECCAS - Economic Community of Central African States

ECI - export concentration index

ECOWAS - Economic Community of West African States

EDI - export diversification index

EU - European Union

G7 - Group of seven particularly vulnerable AU Member States

GDP - gross domestic product

GTAP - Global Trade Analysis Project

HS - Harmonized System (of tariff nomenclature)

HS4 - Harmonized System (of tariff nomenclature; four-digit level)

HS6 - Harmonized System (of tariff nomenclature; six-digit level)

IMF - International Monetary Fund

JRC - Joint Research Centre

LDC - least-developed country

MAcMap - Market Access Map

MAGNET - Modular Applied GeNeral Equilibrium Tool

MFN - most-favoured nation

NTM - non-tariff measure

REC - Regional Economic Community

SACU - Southern African Customs Union

SADC - Southern African Development Community

SPS - sanitary and phytosanitary

SSP - Shared Socioeconomic Pathway

TBT - technical barrier to trade

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