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How Similar are Africa’s Trade Regimes? Implications for the AfCFTA

By John Stuart
16 Oct 2019
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How Similar are Africa’s Trade Regimes? Implications for the AfCFTA

The regional grouping of Africa – both its continental and island states – is a large and diversified bloc of countries. Within this region are multiple regional economic communities (RECs) and their associated preferential and non-preferential trade regimes. Eight of these RECs are seen as building blocs of the African Economic Community but there are other important groupings besides, such as the Southern African Customs Union (SACU) and the West African Economic and Monetary Union (WAEMU). Many of these RECs overlap, resulting in a complex and confusing set of arrangements, and one which is seen as an impediment to broad intra-African trade.

This state of affairs is set to change to some extent, however, with the phasing in of the African Continental Free Trade Area (AfCFTA). The AfCFTA entered into force on 30 May 2019 and is set to be a game-changer for African economic integration. While the AfCFTA will not impose on already existing RECs, such as ECOWAS, the EAC and SADC, it will seek to foster greater integration between the members of RECs and the residual countries of Africa. This will happen as nations phase down their non-preferential trade regimes vis-a-vis other African nations that are not already partnered in a REC, until each African nation trades with every other African nation at the free trade level, at least. By way of clarification, this means that non customs union member countries will only be negotiating concessions with other countries that are not REC partners. In the case of the customs unions, the entire customs union will negotiate with other customs unions and with countries that are in free trade areas.

As with most modern free trade areas (FTAs), the AFCFTA will cover investment provisions, services liberalisation, competition and intellectual property rights. Liberalisation will also not be complete, with a coverage of 90% required and an allowable exclusion of a maximum of 3% of goods. As with many similar arrangements worldwide, it can be expected that liberalisation of the agricultural sector will be highly contested and will likely fall into the exclusion category for many countries.

One significant indicator of the success of the AfCFTA is the extent to which members’ tariff regimes diverge. An FTA does not require harmonisation of external tariff regimes – each member state retains trade policy space – and so may conclude trade agreements with third parties; Significant divergence in tariff regimes in an FTA (or a potential FTA) can indicate the following:

  • Diverging priorities on which sectors to protect and which sector’s imports are seen as a revenue source. The latter point is particularly relevant to developing countries, which are typically more dependent on tariff revenue than developed countries, due to limited alternative tax bases.

  • Big differences between ‘strong’ and ‘weak’ sectors either due to differences in structural economic factors, resource endowments or legacy industrial policy.

  • Arbitrary factors including political-economic influences, large corporate influences (state capture) and rent-seeking.

These disparities, whatever their source, are important because the greater they are the greater the required effort to attain harmonisation. Also, the greater the disparities the greater their potential to destabilise the FTA. This is because, in the absence of a common external tariff (as would be the case with customs unions), trade flows can arise after establishment of the FTA that are suboptimal from an economic perspective.

The first of these suboptimal trade flows is known as trade diversion, where a member country replaces its imports from a more efficient non-member with those from a less efficient member state, since the FTA partner’s product are now effectively cheaper, no longer being subject to an import tariff. Trade diversion, where it boosts production that is less efficient than a third country alternative, is suboptimal and leads to distortions in markets - both goods and factor[1] markets. However, it will not necessarily destabilise the FTA.

On the other hand, when trade flows arise under the FTA directly as a result of differential external tariff regimes, this can destabilise an FTA and lead to disputes and bureaucratic overhead. This situation arises as a result of trade deflection or transhipment, which are two terms for the same thing. Essentially what happens is that the member country with the lowest external tariff (on some specific product) is able to import the item and sell it to its FTA partners at a price lower than their tariff-ridden price. This defeats their protection mechanism and has two effects – it compromises any of their domestic industries that are subject to protection and it also erases their tariff revenue on this product line. As previously pointed out, this can be quite an important source of revenue for developing countries and so is not an insignificant risk with an FTA.

A new tralac trade brief focuses on the issue of the extent of similarity, or difference, between the external most favoured nation (MFN) tariff regimes of the nations of Africa[2]. This paper uses a machine learning technique known as clustering to assess the groupings of the nations of Africa by similarity in their external tariff regimes. The approach taken is to assess differences at a bilateral level, i.e., with respect to each African nation and every other African nation. This approach, although more painstaking, can provide far greater insights than by simply looking at gross measures of divergence, such as for example, deviation from the mean.

Several novel visualisation techniques are introduced in order to present the clustered data. The heat map, dendrogram, and network diagram are all chart types not typically used in supervised economic analysis but which can add much value when using an unsupervised technique such as clustering. Rather than a single form of chart or visualisation, clustered data requires a collection of tools in order to represent the many relational dimensions within the data.

Based on the application of several clustering methods to the countries of Africa, it is apparent that only the members of the two customs unions – SACU and the EAC – consistently cluster together. This is as applied to the weighted MFN rates of protection across five import product groups: one comprising metals and ores and the other four all variants of manufactured goods categories. This implies that there is still appreciable diversity among the nations of Africa, when it comes to trade policy as it applies to non-preferential market access.

Critically, this finding has bearing on the viability of the incoming African Continental Free Trade Area (AfCFTA), since non-uniformity in market access regimes can open the door to trade deflection within a free trade area. Trade deflection has the potential to destabilise the AfCFTA should it not be dealt with both by means of rules of origin (ROO) requirements as well as harmonisation of trade policy regimes. Whereas ROO requirements bring with them a bureaucratic overhead and increase border friction; harmonisation of trade policy regimes has the potential to facilitate regional economic integration and also pave the way for customs union formation.

However, harmonisation also requires attention to domestic industrial policy and the rationalisation of the protection of uncompetitive domestic industries that would not survive without protection in the long run. As the nations of Africa move to increase the integration of their economies, this will call for coordinated regional industrial policy that takes comparative advantage into account but also allows smaller nations to identify and nurture potentially robust industrial sectors. Nations such as Rwanda and Lesotho provide evidence that smaller economies are capable of developing competitive, export-oriented industries, in the presence of economically dominant neighbours.

I am grateful to Trudi Hartzenberg for valuable feedback and editing.


[1] A factor market is a market in which the ‘factors’ of production – land, labour, capital and natural resources – are traded.

[2] Stuart, J. 2019. Market Access Patterns in Africa: Assessing Similarities and Differences. tralac Trade Brief. Stellenbosch: tralac

About the Author(s)

John Stuart

John Stuart

John Stuart is an economist and policy analyst with special interests in trade, economic integration, data visualisation and economic modelling. He began his career in academia at Rhodes University and later the University of Cape Town, after which he entered private consulting first with AFReC (Pty) Ltd and subsequently with PBS (Pty) Ltd. Besides economics research and teaching, he has experience in project management, general management, public sector performance management, systems analysis and entrepreneurship. He holds an M. Com degree in Economics from the University of Natal (Durban).

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