Non-tariff measures (NTMs) and intra-African trade.
Date: 2021-06-11
Institutions: Joint Research Centre
Subjects: Africa, export policy, free movement of goods, non-tariff barrier, research report, technical barriers to trade, trade agreement, trade policy, trade restriction

Non-Tariff Measures (NTMs) and Intra-
African Trade 

Sanjuán López, A.I. 

Gracia de Rentería, P. 

Ferrer Pérez, H. 

Philippidis, G. 

Ferrari, E. 

2021 

EUR 30738 EN 

 
 
 
 
 
 
 
 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and knowledge service. It 
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JRC125378 

EUR 30738 EN 

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ISBN 978-92-76-38678-0 

ISSN 1831-9424 

doi:10.2760/11470 

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How to cite this report: Sanjuán López, A.I., Gracia de Rentería, P., Ferrer Pérez, H., Philippidis, G., Ferrari, E., Non-Tariff Measures (NTMs) 
and Intra-African Trade, EUR 30738 EN, Publications Office of the European Union, Luxembourg, 2021, ISBN 978-92-76-38678-0, 
doi:10.2760/11470, JRC125378. 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Contents 

Abstract ....................................................................................................................................................................................................................................................................... 2 

1 

Introduction..................................................................................................................................................................................................................................................... 3 

2  Sectors and countries scope ........................................................................................................................................................................................................... 5 

3  Description and analysis of existing databases on ad-valorem equivalents of non-tariff measures .................... 7 

3.1  The Trade restrictiveness indices database (KNO2009) ....................................................................................................................... 7 

3.2  An updated trade restrictiveness indices database (KNO2019) .................................................................................................... 9 

3.3  The Productivity, Non-Tariff Measures and Openness (PRONTO) database ...................................................................... 9 

4  Own Estimation of intra-African ad-valorem equivalents of non-tariff measures ............................................................... 11 

4.1  The non-tariff measures data ..................................................................................................................................................................................... 11 

4.2  Empirical model and data ................................................................................................................................................................................................ 12 

4.3  Estimation Results ................................................................................................................................................................................................................... 15 

5  Conclusions .................................................................................................................................................................................................................................................. 23 

References ............................................................................................................................................................................................................................................................. 24 

List of abbreviations and definitions ........................................................................................................................................................................................... 26 

Abbreviations of African country ISO-codes ......................................................................................................................................................................... 27 

Abbreviations of GTAP sector codes ............................................................................................................................................................................................. 28 

List of tables ........................................................................................................................................................................................................................................................ 29 

Annexes .................................................................................................................................................................................................................................................................... 30 

Annex 1. Additional Data ................................................................................................................................................................................................................ 30 

i 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
Authors 

Sanjuán López, A.I. 
Gracia de Rentería, P. 
Ferrer Pérez, H. 
Philippidis, G 

Ferrari, E.  

Centro de Investigación y Tecnología Agroalimentaria de Aragón (Zaragoza, Spain) 
Centro de Investigación y Tecnología Agroalimentaria de Aragón (Zaragoza, Spain) 
Centro de Investigación y Tecnología Agroalimentaria de Aragón (Zaragoza, Spain) 
Centro de Investigación y Tecnología de Aragón, Aragonese Agency for Research 
and Development (ARAID) (Zaragoza, Spain) 
JRC-Seville (Spain) 

1 

 
 
 
 
 
Abstract 

Recently, African countries signed the African Continental Free Trade Agreement (AfCFTA) to provide a single 
continental market for goods and services with free movement of people and investments and to accelerate 
intra-Africa trade. African countries recognised the significance of non-tariff measures (NTMs) in achieving the 
AfCFTA objectives and adopted an Annex to the agreement specifically dedicated to eliminating NTMs, i.e., all 
those policy measures other than ordinary customs tariffs that can have an effect on trade. Although NTMs will 
be crucial to the success of AfCFTA, a proper estimation of the expected trade cost reductions associated with 
NTM eliminations on intra-African trade is lacking. This study examines the impact of NTMs on intra-African 
trade by exhaustively reviewing databases of previous ad-valorem equivalent (AVE) estimates of NTMs applied 
by African countries in agri-food products and by providing estimates  of NTM trade impacts for sectors and 
regions of special relevance. 

The report finds a systematic trade-restricting effect arising from the application of both technical and non-
technical  measures  with  a  tendency  for  the  latter  to  be  more  trade-restrictive.  It  also  finds  remarkable 
deviations in estimated AVEs for Africa from the overall means in the sample of countries. Finally, the estimates 
highlight that the main hotspots for NTMs in intra-African trade would be in sectors like rice and sugar, while 
the main policy actions need to address non-technical measures. 

2 

 
1 

Introduction 

According to the United Nations Conference on Trade and Development (UNCTAD) Economic Development in 
Africa Report 2019 (UNCTAD, 2019a), African international trade accounted for US$760 billion during the period 
2015-17. Examining the same source, this remains someway short of the value of trade in Europe (US$4,109 
billion) or Asia (US$6,801 billion). Moreover, most countries of the African continent exhibited a high degree of 
export-commodity-dependence  between  2013  and  2017  (UNCTAD,  2019b)1.  Africa  has  also  exhibited  an 
increasing  trend  toward  interregional  trade  over  the  last  decade,  although  it  remains  low  relative  to  other 
regions. For example, the share of intra-African exports accounted for almost 17%2 in 2017, compared with 
corresponding statistics for Europe (68%) and Asia (59%). However, in 2016, Africa reported deeper integration 
in  terms  of  intra-regional  trade  among  economic  communities  showing  shares  between  18%  and  85%  for 
seven out of the eight Regional Economic Communities (RECs). The RECs that rank highest are the Southern 
African Development Community (SADC), the Common Market for Eastern and Southern Africa (COMESA), the 
Economic  Community  of  West  African  States  (ECOWAS)  and  the  East  African  Community  (EAC)  (UNCTAD, 
2019a)3. 

Recently,  African  countries  signed  the  African  Continental  Free  Trade  Agreement  (AfCFTA)  and  launched  an 
action plan for Boosting Intra-African Trade (BIAT) with a view to strengthening regional integration, boosting 
Africa’s  appeal  as  a  global  trading  partner  (BIAT  2012)  and  hence  fostering  prosperity  consistent  with 
sustainable development (UNCTAD, 2019a). The AfCFTA was formally established in March 2018 at the 10th 
Extraordinary Session of the African Union (AU) Assembly in Kigali, Rwanda. It is one of the key priorities of the 
Africa Agenda 2063 and a major step toward African continental economic integration. The AfCFTA featured 
prominently in the political declaration of the fifth AU-EU Summit held in Abidjan, Cote d’Ivoire in November 
2017. The agreement went into force on May 30th, 2019 and entered its operational phase on July 7, 2019 
following the 33rd Extraordinary Summit of the African Union. Fifty-four of the 55 AU member States have now 
signed  the  agreement.  As  of  the  5th  February  2021,  thirty-six  Member  States  had  ratified  and  deposited 
ratification instruments. 

The AfCFTA aims at providing a single continental market for goods and services with free movement of people 
and capital. Its goal is to accelerate Intra-Africa trade through better harmonisation and coordination of trade 
liberalisation.  Nevertheless,  the  path  ahead  will  not  be  easy  due  to  several  barriers  such  as  infrastructure 
investment, administrative and transaction costs, information costs, costs induced from non-tariff measures 
(NTMs) to name a few obstacles to trade. African nations recognised the significance of NTMs, and adopted an 
Annex to the AfCFTA Agreement specifically dedicated to eliminating them. 

NTMs  are  policy  measures  other than  ordinary  customs tariffs  that  can  have  an  effect  on  trade.  NTMs are 
classified according to their scope and/or design and include a wide range of instruments such as  (inter alia) 
sanitary and phytosanitary (SPS) measures, technical barriers to trade (TBT), pre‐shipment inspection and other 
formalities,  contingent  trade‐protective  measures,  intellectual  property  rights  and  rule  of  origin.  (UNCTAD, 
2015). By contrast, to transparent and measurable tariffs, there is no common agreement on the aim, collection, 
quantification and modelling of NTMs. Agri-food sectors are among those which undergo many different NTMs.  

The importance of the presence of NTMs has grown significantly over the last two decades. With the successful 
conclusion of numerous free trade agreements (FTAs), customs tariffs barriers are gradually falling such that 
NTMs now constitute the main friction to trade. Many studies attempt to understand better the effect of NTMs 
on international trade (Ferrantino (2006), Disdier et al., (2008), Kee et al., (2009), Bratt (2017), Cadot et al., 
(2018),  Niu  et  al.,  (2018)  among  others)  but  there  is  relatively  scant  literature  focused  on  Africa.  Some 
exceptions are Nimenya et al., (2012), Santeramo and Lamonaca (2019) and Liu et al., (2019). Notwithstanding, 
and  to  the  best  of  our  knowledge,  the  impact  of  NTMs  in  intra-regional  trade  in  Africa  has  not  yet  been 
addressed,  which  is  key  for  policy  making  and  trade  negotiators  when  seeking  to  open  market  access 
opportunities, thereby enhancing intra-African trade. 

The  literature  on  the  impacts  of  the  AfCFTA  is  flourishing,  most  of  which  employs  simulation  modelling.  In 
general, all these studies find welfare gains from combined tariff elimination accompanied by trade facilitation 
measures and reductions in  NTMs.  This general  result is  because tariff levels are  already low among many 

1  

2  

3  

Following  UNCTAD’s  terminology,  a  country  is  said  to  be  export-commodity-dependent  when  more  than  60%  of  its  merchandise 
exports are composed of commodities (UNCTAD, 2019b). Thus, except for Egypt, Morocco, Western Sahara, Tunisia and South Africa, 
the rest of African countries are included in this group. 
This figure is even smaller when considering the average of the sum of imports and exports, than on average in the period 2015-
2017 accounted only for 2%. 
The  RECs  are  COMESA,  SADC,  EAC,  ECOWAS,  CEN-SAD  (Community  of  Sahel-Saharan  States),  AMV  (Arab  Maghreb  Union),  IGAD 
(Intergovernmental Authority on Development) and ECCAS (Economic Community of Central African States). 

3 

 
                                           
African countries largely due to the existing RECs. In this context, it is expected that the largest impact in this 
agreement will most likely come from changes in NTMs, which is the key to a successful implementation of the 
AfCFTA  (Stender  and  Vogel,  2021)  and  other  flanking  broad  trade  facilitations  measures.  Several  studies 
estimate that NTMs are at least three times more restrictive than regular customs duties suggesting that African 
countries could  gain  US$20  billion in  Gross Domestic Product (GDP)  growth by tackling  such  barriers at the 
continental level (Vanzetti et al., 2018). As a rule, all the existing studies suggest that the reduction of  NTMs 
and implementation of trade facilitation will provide the biggest gains with significant increases in intra-African 
trade and GDP gains (World Bank, 2020). 

For  this  reason,  a  proper  estimation  of  expected  trade  cost  reductions  associated  with  provisions  on  NTMs 
becomes a critical element for assessing the potential economic impacts of AfCFTA. This study fills the gap in 
the literature and attempts to investigate the impact of NTMs on intra-African trade. This analysis is structured 
in two stages: the first stage exhaustively reviews databases on previous estimates of ad-valorem equivalents 
(AVEs) of NTMs applied by African countries in agri-food products; the second stage sets out to generate new 
estimates on NTM trade impacts for  those sectors and regions of special relevance that are not sufficiently 
covered by secondary data. 

The rest of the report is organised as follows. Section 2 introduces the country and sector scope of the analysis. 
Section 3 describes the existing databases on AVEs of NTMS, section 4 describes the estimation of intra-African 
AVEs of NTMs. Section 5 concludes. 

4 

 
2  Sectors and countries scope 

This report focuses on trade within Africa and, in particular, on countries belonging to the African RECs: SADC 
(including countries from the South African Custom Union (SACU)), EAC, ECOWAS (including countries from the 
African Economic and Monetary Union (UEMOA)), ECCAS, and COMESA.  

Regarding the  sectoral coverage, this report  uses the  Harmonized System Nomenclature at 2, 4 and  6-digit 
disaggregation  (HS-2,  HS-4  and  HS-6,  respectively)  and  the  Global  Trade  Analysis  Project  (GTAP)  database 
disaggregation (Aguiar et al., 2016). In particular, the report focuses on the agri-food products included in these 
classifications. Due to the difficulty of analysing this multitude of products,  we identified the products with a 
greater flow of intra-African trade to analyse in greater depth the application of NTMs in these key products. 
For this purpose, the United Nations’ COMTRADE database was used to select the most traded products among 
the countries belonging to the African RECs. The identified products with greater trade weights, together with 
the value of intra-African RECs’ trade for the year 2018, is presented in Table 14. The value of bilateral trade 
for these  products among the different  African RECs is shown in Table A1 and Table A2 of the Appendix. 
Additionally, the same data were analysed for the year 2015 to validate the relevance of these sectors, although 
results are not presented in this report for brevity. 

The category of products with more intense trade flows between African RECs are, in this order, “fish” (0303), 
“sugar” (1701), “tea” (0902), “palm oil” (1511) and “corn” (1005)  (Table 1). Most active RECs in these main 
food traded categories are ECOWAS, SACU, EAC and COMESA, and intra-RECs trade absorbs a big proportion of 
intra-African trade in some specific product categories. For instance, intra-ECOWAS trade account for more than 
half of the trade in fish (54%) and palm oil (77%); intra-SACU trade accounts for 50% of trade in sugar (1701); 
and intra-EAC trade accounts for 38% of corn trade. In tea, the third most important traded food, the main 
trade flow occurs from EAC to COMESA (77%) (Table A2). 

Table 1. Products selected and value of intra-African RECs’ trade in 2018 

HS-4 
code 

0303 

Description 

Fish, frozen, excluding fish fillets and other fish meat 
of heading 03.04 

0401  Milk and cream, not concentrated nor containing 
added sugar or other sweetening matter 
0402  Milk and cream, concentrated or containing added 

sugar or other sweetening matter 

2018 Trade 
value (thousand 
US$) 
832,364.63 

% over 
GTAP 
sectors 
29.12% 

% over 
agri-food 
trade 
14.81% 

92,229.09 

3.23% 

1.64% 

83,149.41 

2.91% 

1.48% 

0901  Coffee, whether or not roasted or decaffeinated; 

47,094.19 

1.65% 

0.84% 

coffee husks and skins; coffee substitutes containing 
coffee in any proportion 
Tea, whether or not flavoured 

0902 
1001  Wheat and meslin 
1005  Maize (corn) 
1006  Rice 
1511  Palm oil and its fractions, whether or not refined, but 

not chemically modified 

347,798.47 
16,679.85 
225,081.28 
71,419.48 
234,961.95 

12.17% 
0.58% 
7.87% 
2.50% 
8.22% 

6.19% 
0.30% 
4.00% 
1.27% 
4.18% 

1701  Cane or beet sugar and chemically pure sucrose, in 

423,809.75 

14.83% 

7.54% 

solid form 

2201  Waters, including natural or artificial mineral waters 

3,988.50 

0.14% 

0.07% 

and aerated waters, not containing added sugar or 
other sweetening matter nor flavoured; ice and snow 

2202  Waters, including mineral waters and aerated 

83,171.74 

2.91% 

1.48% 

waters, containing added sugar or other sweetening 
matter or flavoured, and other non-alcoholic 

4  

The following countries were considered to obtain the figures presented in Table 1: Lesotho, Namibia, South Africa and  Swaziland 
(from SACU); Kenya, Rwanda, Tanzania and Uganda (from EAC); Benin, Burkina Faso, Cape Verde, Côte d’Ivoire, Gambia, Ghana, Guinea, 
Guinea-Bissau, Liberia, Mali, Mauritania, Niger, Nigeria, Saint Helena, Senegal, Sierra Leone and Togo (from ECOWAS); Angola, Burundi, 
Comoros,  Democratic  Republic  of  the  Congo,  Djibouti,  Egypt,  Eritrea,  Ethiopia,  Madagascar,  Malawi,  Mayotte,  Seychelles,  Somalia, 
Sudan, Zambia and Zimbabwe (from COMESA). 

5 

 
 
                                           
beverages, not including fruit or vegetable juices of 
heading 20.09 
2203  Beer made from malt 
2204  Wine of fresh grapes, including fortified wines; grape 

must other than that of heading 20.09 

128,563.79 
90,618.19 

4.50% 
3.17% 

2.29% 
1.61% 

2205  Vermouth and other wine of fresh grapes flavoured 

3,818.87 

0.13% 

0.07% 

with plants or aromatic substances 

2206  Other fermented beverages (for example, cider, 

27,673.23 

0.97% 

0.49% 

perry, mead); mixtures of fermented beverages and 
mixtures of fermented beverages and non-alcoholic 
beverages, not elsewhere specified or included 

2207  Undenatured ethyl alcohol of an alcoholic strength 
by volume of 80 % vol. or higher; ethyl alcohol and 
other spirits, denatured, of any strength 
2208  Undenatured ethyl alcohol of an alcoholic strength 

by volume of less than 80 % vol.; spirits, liqueurs 
and other spirituous beverages 

89,832.63 

3.14% 

1.60% 

56,281.96 

1.97% 

1.00% 

6 

 
 
3  Description and analysis of existing databases on ad-valorem 

equivalents of non-tariff measures 

This  section  provides  an  exhaustive  review  on  available  information  on  the  application  of  NTMs  by  African 
countries and NTMs related trade costs previously estimated in the literature. A review of the databases serves 
as a starting point for identifying the country and sector coverage of these databases and the weak points that 
should be addressed in future econometric analysis. 

The review of the literature reveals the existence of three databases on ad-valorem equivalents of NTMs. The 
first one by Kee et al., (2009) (henceforth, KNO2009) and the second one updated by these same authors in 
2019  (henceforth,  KNO2019).  The  third  database  is  produced  under  the  PRONTO  (Productivity,  Non-Tariff 
Measures and Openness) project funded by the European Commission and described in Ghodsi et al., (2016). 

These databases provide worldwide information on NTMs imposition and its AVE cost, and all of them use the 
Harmonized System Nomenclature. The KNO2019 database also uses the GTAP database sectoral classification 
(Aguiar et al., 2016). To simplify the analysis of these  databases and facilitate comparison with the results 
obtained  in  this  report, this  section  only  analyses  the  information  in  these  databases  for  the  countries and 
products  that  are  the  subject  of  this  report  and  that  have  been  detailed  in  the  previous  section.  Then,  an 
exhaustive analysis of each of these databases is carried out. 

3.1 

The Trade restrictiveness indices database (KNO2009) 

The seminal work of Kee et al., (2009) provides a rich database that offers unilateral AVEs of NTMs imposed 
worldwide at the HS-6 level of disaggregation. They use cross sectional data collected between 2000 and 2004. 
Tariff data comes from the World Trade Organization (WTO) Integrated database and UNCTAD Trade Analysis 
and Information System (TRAINS); trade data comes from the United Nations COMTRADE database; and NTM 
data  comes  mainly  from  UNCTAD  TRAINS,  and  it  is  updated  with  WTO’s  Trade  Policy  Reviews  and  the  EU 
Standard’s Database. NTMs enter the model as a dummy (i.e. when the importer applies at least one NTM on 
that sector) and jointly covers technical, price control, quantity restrictions and monopolistic measures. Kee et 
al., (2009) specify an import equation to examine the impact of NTMs on import values (in US$), taking into 
account as control variables the countries characteristics proposed by Leamer (1990)5 and two gravity standard 
variables6.  They  estimate  this  model  for  each  product  line  using  non-linear  least  squares  and  instrumental 
variables for avoiding possible endogeneity problems. Then, they obtain the AVEs of NTMs based on results of 
this  estimation  and  on  their  own-estimated  import  demand  elasticities  (Kee  et  al.,  2008),  also  publically 
available. 

This methodological process leads to a database of  unilateral AVEs of NTMs for 93 countries at HS-6 level. 
However, only 17 of these countries are members of the African RECs, thus covering only 41% of the countries 
of interest. Moreover, the data are not exhaustive (for  all HS-6 product lines) or consistent across countries 
(different product lines are covered for each country).  

The comparison of this database with the other two is also difficult because, as shown in Table 2, countries 
included in the different databases are not always the same. In the same way, there are relevant differences 
in the products analysed by each of these databases for the countries considered, as can be corroborated in 
Table A3 in the Appendix. 

5  

6  

These country characteristics are the relative factor endowments (agricultural land over GDP, capital over GDP and labour over GDP), 
as well as GDP to capture economic size. 
Variables considered are a dummy for islands and a measure of the average distance to world markets (i.e., the import-weighted 
distance to each trading partner). 

7 

 
 
 
 
                                           
Table 2. African importers included in the publically available databases on AVE of NTMs 

Reporter country 
Angola 
Burundi 
Benin 
Burkina Faso 
Côte d’Ivoire 
Dem. Rep. of the 
Congo 
Comoros 
Cape Verde 
Djibouti 
Egypt 
Eritrea 
Ethiopia 
Ghana 
Guinea 
Gambia 
Guinea-Bissau 
Kenya 
Liberia 
Lesotho 
Madagascar 
Mali 
Mauritania 
Malawi 
Mayotte 
Namibia 
Niger 
Nigeria 
Rwanda 
Sudan 
Senegal 
Saint Helena 
Sierra Leone 
Somalia 
Eswatini 
Seychelles 
Togo 
Tanzania 
Uganda 
South Africa 
Zambia 
Zimbabwe 

ISO3 code 
AGO 
BDI 
BEN 
BFA 
CIV 

COD 

COM 
CPV 
DJI 
EGY 
ERI 
ETH 
GHA 
GIN 
GMB 
GNB 
KEN 
LBR 
LSO 
MDG 
MLI 
MRT 
MWI 
MYT 
NAM 
NER 
NGA 
RWA 
SDN 
SEN 
SHN 
SLE 
SOM 
SWZ 
SYC 
TGO 
TZA 
UGA 
ZAF 
ZMB 
ZWE 

KNO2009 

KNO2019 

PRONTO 

 

 
 

 

 
 

 

 

 

 

 

 
 
 

 

 
 

 

 
 

 

 
 

 

 
 
 
 

 
 
 

 

8 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
3.2 

An updated trade restrictiveness indices database (KNO2019) 

The 2009 database on NTM AVEs was updated in 2019 based on the estimation method developed in Kee and 
Nicita (2016). The methodological strategy is similar to that proposed by Kee et al., (2009) and relies on the 
same sources of information (mainly, COMTRADE and UNCTAD TRAINS), although in this case the cross sectional 
data were collected around 2011. However, there are important differences. To start with, in this case, a gravity 
model (i.e.,  bilateral trade) instead of an import equation  is specified.  Model specification differences affect 
both the endogenous variable (in this case, the imported quantity is treated as discrete instead of continuous) 
and the control variables, since more gravity variables7 are included. Besides, to obtain specific NTM impacts 
for each country, the NTM variable is interacted with import and export market shares of each country in world 
trade of each product, instead of the relative factor endowments used in KNO2009. This model is estimated 
for each HS-6 product line using a Zero-Inflated Negative Binomial (ZINB) model to tackle the large presence 
of zeros in the endogenous variable, although other estimation alternatives are also tested8. Finally, as in Kee 
et al., (2009), instrumental variables are used to address possible endogeneity issues of tariffs and NTMs. 

Based on results of this estimation and on its own-estimated import demand elasticities, a database is provided 
with AVEs of NTMs, which differs significantly from those of Kee et al., (2009), and not only on the country 
coverage.  A  first  difference  is  that  KNO2019  database  provides  AVEs  for  two  broad  categories  of  NTMs: 
technical  and  non-technical.  Technical  measures  include  those  measures  gathered  in  Chapters  A  and  B  of 
UNCTAD  (2015)  classification,  whereas  non-technical  ones  include  measures  of  Chapters  D-G  of  said 
classification. Chapter A are the SPS measures and Chapter B include the TBT. The second difference is that 
KNO2019 comprises two sub-databases that mainly differ on the product classification. The first one uses the 
aforementioned HS-6 product disaggregation (covering the product lines showed in Table A2 of the Appendix 
for the sectors and countries of interest in this report), and covers 40 importing countries plus the European 
Union. However, only seven of these countries belong to our African RECs of interest (see Table 2). The second 
sub-database follows the GTAP 8 classification, although AVEs for 3 of the 25 agri-food sectors could not be 
estimated (wheat, sugar cane/beet, and raw milk). Another relevant difference with the first sub-database is 
that the former offers bilateral AVEs, thus including 40 importing countries plus the European Union and 151 
exporting countries plus the European Union. However, only 7 of the importing countries (see Table 2) and 30 
of the exporting countries belong to the African RECs considered. 

3.3 

The Productivity, Non-Tariff Measures and Openness (PRONTO) database 

This database provides, among a wide variety of variables related to the NTMs application, importer-specific 
AVEs of NTMs imposed by a large array of countries. One of the main differences with the  methodologies of 
previous databases is that this one relies on panel data for the period 2002-2011 and on the WTO database 
for NTM notifications and Special Trade Concerns. Trade and tariff data rely on the same global databases as 
the aforementioned papers (COMTRADE and UNCTAD TRAINS). 

Although the methodological approach, described in Ghodsi et al., (2016), is similar to the previous papers, there 
are also significant differences regarding specification and estimation. First, variables included for controlling 
bilateral trade are classical gravity variables and factor endowments relative to GDP (for labour, capital stock 
and agricultural land area), which are retrieved from the Penn World Tables (PWT 8.0) (see Feenstra et al., 2013, 
2015). They also include as an explanatory variable the index proposed by Baltagi et al., (2003) reflecting how 
different the trading partners are with respect to real GDP per capita. Moreover, product, importer, country-pair, 
and time fixed effects are included. Then, a Poisson Maximum Likelihood estimator is used to estimate this 
model for each  HS-6  product line, in which exogenous variables  such as policy  variables  are lagged by one 
period to avoid endogeneity bias. 

Based on results of this estimation and on its own-estimated import demand elasticities, the result is a database 
providing  unilateral  AVEs  of  NTMs,  which  again  differs  from  previous  databases  not  only  on  the  countries 
considered (see Table 2), but also on the detail of information. One of the main contributions of the PRONTO 
database  is  that  AVE  estimates  are  provided  distinguishing  9  NTM  categories:  antidumping,  countervailing 
duties, quantitative restrictions, safeguard measures, SPS measures, specific trade concerns raised against SPS, 
special safeguards, TBT, and specific trade concerns raised against TBT. 

7  

8  

The log of GDP of importer and exporter, bilateral distance between importer and exporter, landlocked indicators for importer and 
exporter, and common border indicator. 
They also run Negative Binomial (NB), Zero-Inflated Poisson (ZIP), OLS in log, and Poisson-type regressions 

9 

 
 
                                           
These AVEs are averaged for the period 2002-2011 and correspond to 100 countries at the HS-6 level (see 
Table  A3  of  Appendix).  Similarly  to  what  happens  in  the  other  databases  described,  only  eight  out  of  the 
included countries belong to the African RECs of interest (see Table 2). 

Summing  up,  information  on  AVEs  for  NTMs exist  for a  limited  number of  African  countries,  while  they  are 
widely spread in terms of sectors. Mostly, the values are unilateral (i.e., unique trade cost when accessing the 
importer)  although  some  bilateral  outcomes  are  also  available  within  the  constrained  sample  of  African 
countries.  Finally,  the  estimates  by  KNO  (2009  and  2019)  only  account  for  trade  costs,  where  possible 
“spillways” or trade enhancing effects are constrained to zero. Ghodsi et al., (2016) provide both positive and 
negative AVEs to account for both trade costs and trade enhancing effects. 

10 

 
 
4  Own Estimation of intra-African ad-valorem equivalents of non-tariff 

measures 

4.1 

The non-tariff measures data 

The NTM data used in the estimation is based on an updated version9 that reconciles information from UNCTAD-
TRAINS and the International Trade Centre (ITC). The UNCTAD-TRAINS-ITC database informs about the number 
of measures applied by each reporter  s in each HS-6 product line  h and up to 4-digit NTMs categories (see 
UNCTAD, 2015). NTMs are classified into 16 chapters (15 chapters affecting imports and one exports). The first 
three  chapters  cover  technical  measures:  SPS  (Chapter  A),  TBT  (Chapter  B)  and  pre-shipment  inspections 
(Chapter C), and the remaining twelve are non-technical, as price and quantity control measures, for instance. 
The estimation considers the aggregation into technical and non-technical measures. These measures can be 
imposed unilaterally to any partner (most of the observations) or bilaterally to specific countries or regions, and 
we carefully accounted for both when calculating the number of measures faced by country r when exporting 
to country s. The data for each country has been collected at different points in time, between 2010 and 2018, 
although the maximum number of observations correspond to 2015 and 2016. Starting and ending dates of 
application are provided, what we exploited to calculate the number of measures in place every year in the 
period 2012-2018, for every triad exporter-importer-sector. 

This latest release covers 92 countries (the EU as single region), from which, eighteen are African countries 
belonging to the RECs of interest: UEMOA-ECOWAS (Benin, Burkina Faso, Côte d’Ivoire, Mali, Niger, Senegal, and 
Togo); ECOWAS (Cape Verde, Gambia, Ghana, Guinea, Liberia, Nigeria); COMESA (Ethiopia, Mauritius, Zimbabwe); 
ECCAS (Cameroon); and SADC (Botswana).  Additionally, the  database  covers four Northern African  countries 
members of the Arab Maghreb Union (AMU): Algeria, Morocco, Mauritania and Tunisia.  

Table A4 in the appendix shows the number of NTMs (mean), by Sub-Saharan African country, NTM category, 
and each HS-4 product identified as most intensely traded (see Table 1). Additionally, to have an idea of the 
NTMs imposed in agri-food products in general, the number of NTMs affecting on average each agri-food GTAP 
sector, by country and NTM category, is presented in Table A5 in the Appendix. 

Summarizing, the selected countries impose on average 13.89 measures on the selected products, a figure that 
is significantly lower than the 41.32 NTMs imposed by all countries in the database. In comparison to other 
sectors,  11.84  NTMs  are  imposed  by  the  selected  African  countries.  This  revels  that  the  selected  countries 
impose less NTMs to the selected products in comparison with the rest of the world, but they impose more 
NTMs to the selected products than to other agri-food products. 

By countries, Gambia, Mauritius, Guinea, Benin and Cabo Verde are those imposing more NTMs to the selected 
products (on average, for all HS-4, they impose 28.37, 23.89, 17.59, 14.06, and 13.21 NTMs, respectively). In 
contrast, Côte d’Ivoire, Burkina Faso, Niger, Senegal, and Zimbabwe are the countries that impose less NTMs to 
the selected products (on average, for all HS-4, they impose 2.04, 4.02, 4.19, 4.30, and 4.59 NTMs, respectively).  

Differences by products are less pronounced. On average, by all the selected countries, most products have 
around 10-13 NTMs. Fish products (0303) stand out for the largest number of NTMs imposed (17.09 for all 
selected counties as a whole), whereas wheat (1001) and maize (1005) are the products with the least NTMs 
(on average, 6.10 and 6.11 for all selected countries as a whole, respectively).  

Regarding the NTM categories, most are technical measures (on average, for all selected countries and products, 
85% are technical and 15% are non-technical). Among the technical NTMs, there is a larger presence of SPS 
measures (Chapter A), more than doubling the number of technical barriers to trade (Chapter B). 

9  

Downloaded in November 2020 from: https://trains.unctad.org/Forms/Analysis.aspx 

11 

 
 
 
                                           
4.2 

Empirical model and data 

The impact of NTMs on trade is traditionally studied in the context of a gravity equation, where bilateral trade 
(in value) is explained by a set of unilateral and bilateral trade partners’ specific variables, such as GDP, distance 
and other geographical, historical and cultural variables. In a direct approach, an NTM indicator enters as an 
additional explanatory variable. In absence of explicit NTMs variables, the indirect approach infers “hidden trade 
costs” either from the residuals of the gravity equation or by comparing international with domestic trade flows 
(e.g., Head and Mayer, 2004; Chen and Novy, 2011). In any case, the impact of NTMs on bilateral trade needs 
to  be  translated  into  a  price-equivalent  by  using  elasticities  of  substitution,  normally  borrowed  from  the 
literature, but also estimated within the model. 

From  an  estimation  standpoint,  the  Poisson  pseudo-maximum  likelihood  (PPML)  has  consolidated  as  the 
preferred estimator since the proposal by Silva and Tenreyro (2006). The PPML not only allows the estimation 
of the gravity model in its theoretical multiplicative form preserving the inclusion of zero-trade values, but also 
avoids inconsistent coefficient estimates in the presence of heteroscedasticity. The PPML provides consistent 
estimates even if data is not pure count (Wooldridge, 2012), as it is the case of bilateral trade (i.e., bilateral 
trade is continuous rather than integer but takes values equal or greater than zero). 

NTM data are recorded at the HS 6-digit level. Accordingly, within the list of mostly traded sectors in Table 1, 
we selected 13 HS 6-digit sectors to conduct the estimation of the AVEs of NTMs. These correspond to corn, 
rice, sugar and palm oil. See Table 3 for a full description. 

Table 3. Selected HS-6 digit products for the estimation of NTMs AVEs 

Product 

Corn 

Rice 

Palm oil  
(and its 
fractions, 
whether or not 
refined, but not 
chemically 
modified) 
Sugar 
(Cane or beet 
sugar and 
chemically pure 
sucrose, in 
solid form) 

HS 6-digit 
100510 
100590 
100610 
100620 

100630 

100640 
151110 

Description 
Seed 
Other 
Rice in the husk (paddy or rough) 
Husked (brown) rice 
Semi-milled or wholly milled rice, whether or not 
polished or glazed 
Broken rice 
Crude Oil 

151190 

Other 

170112 
170113 
170114 
170191 

170199 

Beet sugar 
Cane sugar 
Other cane sugar 
Containing added flavouring or colouring matter 

Other 

GTAP 
GRO 
GRO 
PDR 
PCR 

PCR 

PCR 
VOL 

VOL 

SGR 
SGR 
SGR 
SGR 

SGR 

Thus, a gravity equation is estimated individually for each single HS 6-digit sector. Besides, and as in most of 
the  applications  with  NTMs,  we  opted  for  a  cross-section  estimation  that  we  chose  according  to  the  NTM 
collection year. We chose 2018, unless the direction of the tariff was counter-intuitive, leading to implausible 
AVEs, in which case, the alternative year 2015 was selected. 

The final gravity specification using the Poisson regression model is: 

𝐸(𝑚𝑟𝑠|𝒙) = 𝑒𝑥𝑝(𝒙′𝜷)

=  𝒆𝒙𝒑 (

𝛽0 + 𝛽𝑚𝑡𝑚𝑡𝑟𝑠 + 𝛽𝑁𝑇𝑀,𝑟𝑠𝑁𝑇𝑀𝑟𝑠 +   𝛽1𝑙𝑔𝑑𝑝𝑟 +   𝛽2𝑙𝑔𝑑𝑝𝑠 +   𝛽3𝑟𝑡𝑎𝑟𝑠
+  𝛽4𝑠𝑞𝑖𝑛𝑐𝑜𝑚𝑒𝑟𝑠 +
𝛽5𝑙𝑑𝑖𝑠𝑡𝑟𝑠 + 𝛽6𝑐𝑜𝑛𝑡𝑖𝑔𝑟𝑠 + 𝛽7𝑙𝑎𝑛𝑑𝑙𝑜𝑐𝑘𝑒𝑑𝑟 +   𝛽8𝑙𝑎𝑛𝑑𝑙𝑜𝑐𝑘𝑒𝑑𝑠
+ 𝛽9𝑐𝑜𝑙𝑜𝑛𝑦𝑟𝑠 + 𝛽10𝑙𝑎𝑛𝑔𝑟𝑠

(1) 

) 

12 

 
 
 
 
where 𝐸(𝑚𝑟𝑠|𝒙) is the expected value or mean of the dependent variable i.e., bilateral trade between exporter 
r and importer s (mrs) conditional on explanatory variables x, and 𝜷 is the matrix of coefficients to estimate. 
Trade data comes from UN COMTRADE, downloaded through the Wold Integrated Trade Solution (WITS). It refers 
to value of imports (in thousands US $), and has been collected for each HS 6-digit line. Non-reported values 
by a reporter for a specific partner and year are replaced by 0 (see for instance UNCTAD-WTO, 2015, p.121; 
Helpman et al., 2008). Then, countries that have never exported in the period 2012-2018 are excluded. 

As economic explanatory variables, GDPs in the exporter and importer country (in logs) (lgdp), GDP per capita 
(sqincome) and regional trade agreements (rta) are considered. The theory predicts a coefficient equal to one 
for GDP in aggregated trade. In more disaggregated sectors, still some authors suggest keeping GDPs (UNCTAD-
WTO, 2015) as the economies grow, trade expands by trading more products (extensive margin) but also more 
volume for each product (Hummels and Klenow, 2005). GDP per capita is usually included in connection with 
the testing of the “Linder” hypothesis or as proxy for factor endowments. In particular, in our specification, the 
per capita GDP (sqincome) enters as the log of the square difference of per capita income in countries r and s: 
ln[(GDPpcr –  GDPpcs)2].  GDP  and  GDP  per  capita  come  from  the  World  Bank,  World  Development  Indicators. 
Regional trade agreements (RTA) values one when the exporter and importer are members of the same RTA, 
and the data comes from Mario Larch’s Regional Trade Agreements Database from Egger and Larch (2008) 
and updated December 2015. 

The  traditional  gravity  bilateral  controls are  distance  between  countries (ldist)  which  enters  in  logs;  dummy 
variables  for  contiguity  (cont);  being  landlocked,  for  both,  exporter  and  importer  (landlocked);  sharing  some 
colonial  relationship  (colony);  or the  same  official  language  (lang),  and  are  taken  from the  GeoDist  dataset 
(Mayer and Zignago, 2011). 

All the above explanatory variables are country specific, while the dependent variable (m), and the explanatory 
variables tariffs (mt) and NTMs also depend on the sector. Two variables are essential for the purpose of this 
estimation: tariffs and NTMs. The tariff enters as ln(1+AdV/100), where AdV is the effectively applied tariff, 
that is, the lowest tariff granted by a reporter to a partner for the considered product, which will be the most-
favoured nation tariff unless a preferential tariff exists. Missing tariffs for specific years (if any) are replaced 
by the closest previous available tariff for the same route-HS6 sector (WITS provides this utility). Tariffs data 
come from the UNCTAD TRAINS database and are collected for the HS-6 digit sectors. 

The NTM enters the model as a continuous variable that accounts for the number of measures applied by the 
importer s to the exporter r in the specific sector. This is an alternative way to deal with NTMs, where usually 
simply a dummy variable is included that account for their presence. In our application, however, there is not 
enough country variability on the NTM presence to estimate reliably the NTM impact.  

Besides, although usually the NTMs are imposed unilaterally, they might have a different impact on different 
exporters.  To  account  for  this  possible  heterogeneous  trade  impact,  we  follow  the  “indirect  characteristics” 
approach  by  Gourdon  et  al.,  (2018),  and  Kee  and Nicita  (2016).  This  approach  requires  interacting  the  NTM 
variable with sectoral trade shares. Over alternative methods, like the one used by Ghodsi et al., (2016), where 
every  possible  NTM-importer  (route)  interaction  is  estimated,  the  current  proposal  reduces  significantly  the 
computational burden. 

Consequently, the variable NTM in equation (1) is augmented by interacting the NTM variable with the world 
market share of exports of the HS-6 product from exporter r (shf), and the world market share of imports of 
the HS-6 product of importers s (sht): 

𝛽𝑁𝑇𝑀,𝑟𝑠 =   𝛽𝑁𝑇𝑀 +   𝛽1,𝑁𝑇𝑀   ∙ 𝑠ℎ𝑓𝑟 +   𝛽2,𝑁𝑇𝑀 ∙ 𝑠ℎ𝑡𝑠 

(2) 

By  replacing  the  market  shares  by  average  values  for  the  exporter/importer  of  interest,  we  obtain  specific 
country semi-elasticities or  NTM trade impacts.  In particular, we employ average trade  shares in the period 
2012-2018. 

Kee and Nicita (2016) offer some interpretation of both positive and negative impacts of these interactions. 
The larger the market share of the exporter in a particular product (shf), the more likely is that it will find easier 
to comply with the importer’s regulations, favouring bilateral trade. However, the larger the exporter’s share the 
easier is to divert trade to third countries with less cumbersome regulations, leading to a depressing impact on 
bilateral trade with that particular importer. Similarly, when the  market share of the importer (sht) is large, 
exporters will need to adapt to the importer’s regulations, as there will be less alternative destination markets, 
favouring  bilateral  trade.  On  the  other  hand,  larger  importers  tend  to  regulate  trade  more,  and  accordingly 
exporters might find more difficult to comply with regulations of the market leaders, leading to a depressing 
bilateral trade impact. 

13 

 
 
 
However, our dataset shows a very low variability of market shares, as trade concentrates in a few exporters 
and/or importers. Particularly, African countries enjoy very small trade shares, what would lead to practically no 
variation in terms of the influence of NTMs across countries, but also in comparison with other regions with 
also  small  trade  shares.  Consequently,  the  expression  in  (2)  has  further  been  modified  to  include  specific 
impacts of NTMs on African countries, as follows: 

𝛽𝑁𝑇𝑀,𝑟𝑠 =   𝛽1,𝑁𝑇𝑀   ∙ 𝑠ℎ𝑓𝑟 +   𝛽2,𝑁𝑇𝑀 ∙ 𝑠ℎ𝑡𝑠 + 𝛽3,𝑁𝑇𝑀 ∙ 𝑡𝑎𝑓𝑟𝑖𝑐𝑎𝑠 +   𝛽4,𝑁𝑇𝑀 ∙ 𝑓𝑡𝑎𝑓𝑟𝑖𝑐𝑎𝑟𝑠  

(3) 

where  tafrica  is a  dummy  variable  that  values 1  when  the  destination  country  is an  African  country,  and  0 
otherwise; and ftafrica values 1 when the exporter and exporter are African countries, and 0 otherwise. 

From the estimated coefficients on NTM and tariffs, we obtain the AVE or the tariff equivalent that induces the 
same percent change in trade as one additional NTM, and is calculated as10: 

𝑁𝑇𝑀 𝐴𝑉𝐸  =   (𝑒𝑥𝑝[𝛽𝑁𝑇𝑀,𝑟𝑠 𝛽𝑚𝑡

⁄

] − 1)   ∙ 100  

(4) 

The coefficient 𝛽𝑁𝑇𝑀,𝑟𝑠 measures the semi-elasticity or percent change in bilateral trade when changing the 
number  of  NTMs  by  1;  while  𝛽𝑚𝑡  measures  the  percent  change  in  bilateral  trade  following  1%  change  in 
ln(1+AdV/100). 

The coefficient 𝛽𝑁𝑇𝑀,𝑟𝑠 depends on the values of market shares (shfr and shts), and the dummies ftafrica and 
tafrica. We provide three AVEs: 

1. 

2. 

3. 

the overall average in the sample, by replacing the values of market shares and the African dummies 
by their mean values11 in the sample. 

for  African importers, where  tafrica=1, and market  shares and the  ftafrica dummy are replaced  by 
their average values in the sample of African importers. 

for intra-African trade, where ftafrica=1, and market shares and tafrica are replaced by their average 
in the sample of intra-African countries. 

There is a substantial correlation between the number of technical and non-technical measures. Accordingly, 
we conduct the estimation separately. Likewise, although we built the NTM variable aiming at maximising the 
bilateral information recorded in the data, the outcome is that the NTMs imposed by a reporter do not vary 
substantially across exporters. This means that traditional importer-fixed effects recommended in the literature, 
would enter into collinearity problems with the NTM variable. The same argument applies to tariffs or other 
country  specific  variables,  for  the  matter.  Consequently,  the  regression  does  not  include  any  country  fixed 
effects (similarly to Kee et al. (2016). 

Finally, the number of exporters and importers vary between sectors, between 111 and 138, and 99 and 106, 
respectively, as those countries that have never engaged in trade in the period 2012-2018 are excluded. 

10  

11  

This formula is obtained by calculating the change in trade following a discrete change from 0 to 1 in the NTM variable, and from 0 
to AdV* in the power of the tariff ln(1+AdV*), and equating both. 
The mean of a dummy is the proportion of observations in the sample with that characteristic (i.e., the importer is African (when 
tafrica=1), or exporter and importer are African countries (ftafrica=1). 

14 

 
 
 
 
 
 
 
                                           
 
4.3 

Estimation Results 

Estimation results are shown in Tables 4 to 7, for corn, rice, palm oil and sugar, respectively, for both, technical 
and non-technical measures.  

Bilateral distance has a negative and significant impact on trade in all sectors but corn. Contiguity impacts trade 
positively and significantly but only in a minority of sectors and specifications. Sharing language or a historical 
colonial relationship does not always have a significant impact. In fact, in some sectors, unexpected negative 
impacts are found. Something similar occurs with being landlocked, where both, positive and negative impacts 
are found. Nevertheless, it is more plausible to find the a-priori expected influences in aggregated trade than 
in highly singular sectors. 

Influences that are more systematic are found for GDPs and membership to the same RTA, where positive and 
significant influences clearly predominate. The trade elasticity to GDP ranges between 0.2 and 1, with a majority 
of values above 0.5. Likewise, countries members of an RTA trade more than double than countries that do not 
take part in RTAs, what occurs in 9 out of the 13 sectors considered. In any case, to better ascertain the RTA 
effect, a panel model would be needed12. 

The sqincome variable is always negative and significant in most of the models, what favours the hypothesis 
that countries with different  factor intensities (proxied  by per capita income) get  more engaged in trade, in 
accordance with the Heckscher-Ohlin hypothesis. 

The tariff elasticity is negative, as expected, although not always significant (e.g., palm oil). This will cause a 
problem when calculating the NTM AVE. The magnitudes vary considerably across HS-6 sectors, what even if 
the NTMs effect is similar would lead to significant differences in the NTM AVEs.  

With respect to the NTM variables, in almost every model, the NTM market share interactions are positive and 
significant. This supports the interpretation that the more dominant the position of the exporter or the importer 
in the trade of a specific product, the more bilateral trade attracts despite its regulatory burden as there are 
less possibilities for trade partners to divert trade.  

Given the number of interactions, it is difficult to visualise the overall impact of NTMs. To ease the interpretation, 
the average trade impacts in the whole sample, among African importers, and among intra-African partners, 
are presented in the first columns of Table 8 (see equation 3). 

Both  technical  and  non-technical  measures  have  a  negative  and  significant  impact,  although  not  always 
statistically significant, in particular, in the case of non-technical measures. Thus, in four out of thirteen sectors 
non-technical measures have a non-significant value on average in the sample (see column 1 in Table 8). 

The  specification  used  in  the  estimation  allows  seeing  differences  depending  if  the  importer  is  an  African 
country or even if both partners are African (second and third columns in Table 8). The number of sectors for 
which NTMs do not seem to affect significantly trade is larger among African countries. This could be due to 
the relatively small number of NTMs actually in place (see penultimate column in Table 8). However, whenever 
a significant impact is found, this is of larger magnitude than on average in the sample. Technical and non-
technical NTMs are particularly trade-restrictive in intra-African trade in some rice (100620, 100640) and sugar 
products (170112, 170191).  

It is also interesting to remark that non-technical measures are more trade-restrictive than technical measures, 
and this occurs in every single sector as well as on average, amongst African importers and in intra-African 
trade. 

12 [exp (βRTA)-1]*100 is larger than 100% for coefficients greater than 1. 

15 

 
 
 
                                           
Table 4. Estimation results for the corn sector 

HS-6 100510 

HS-6 100590 

mt 

Tech × shf 

Tech × sht 

Tech × tafrica 

Tech : ftafrica = 0 

Tech : ftafrica = 1 

Nontech × shf 

Nontech × sht 

Nontech × tafrica 

Nontech : ftafrica = 0 

Nontech : ftafrica = 1 

lgdp_r 

lgdp_s 

rta 

sqincome 

ldist 

contig 

landlocked_r 

landlocked_s 

colony 

lang 

Constant 

Observations 
R2 
r2_p 
chi2 

Technical 
-4.632** 
(2.121) 
0.271*** 
(0.080) 
0.247*** 
(0.057) 
-3.194*** 
(0.913) 
-0.044** 
(0.017) 
2.730*** 
(0.932) 

0.532*** 
(0.072) 
0.414*** 
(0.069) 
1.735*** 
(0.529) 
-0.150*** 
(0.056) 
0.009 
(0.517) 
0.978* 
(0.574) 
0.017 
(0.753) 
0.011 
(0.490) 
0.145 
(0.582) 
-0.494 
(0.531) 
2.288 
(4.068) 
11,673 
0.215 
0.490 
478.6 

Non-Technical 
-8.969 
(5.474) 

1.610*** 
(0.471) 
2.407*** 
(0.751) 
-5.424*** 
(0.823) 
-0.245*** 
(0.095) 
4.396*** 
(1.079) 
0.588*** 
(0.086) 
0.438*** 
(0.072) 
1.733*** 
(0.557) 
-0.136** 
(0.060) 
-0.016 
(0.509) 
1.140** 
(0.580) 
0.009 
(0.752) 
-0.013 
(0.386) 
-0.031 
(0.591) 
-0.417 
(0.587) 
0.938 
(4.131) 
11,673 
0.101 
0.465 
403.9 

Technical 
-1.553** 
(0.691) 
0.064*** 
(0.010) 
0.198*** 
(0.048) 
-0.912* 
(0.527) 
-0.024** 
(0.010) 
0.030 
(0.907) 

0.659*** 
(0.222) 
0.594*** 
(0.138) 
0.902 
(0.579) 
-0.026 
(0.039) 
-0.176 
(0.472) 
-0.223 
(0.725) 
-0.304 
(0.679) 
-1.976** 
(0.787) 
-1.894*** 
(0.653) 
-0.668 
(0.443) 
0.419 
(5.788) 
13,631 
0.399 
0.526 
1666 

Non-Technical 
-1.290** 
(0.611) 

0.314*** 
(0.075) 
1.311*** 
(0.317) 
-2.733*** 
(0.456) 
-0.116** 
(0.055) 
0.639 
(0.850) 
0.755*** 
(0.214) 
0.583*** 
(0.121) 
0.877 
(0.603) 
0.011 
(0.045) 
-0.240 
(0.424) 
-0.085 
(0.706) 
-0.257 
(0.684) 
-2.203*** 
(0.792) 
-2.133*** 
(0.658) 
-0.650 
(0.400) 
-1.152 
(5.219) 
13,631 
0.388 
0.505 
2145 

Notes: Robust standard errors clustered by country pairs in parentheses. *** p <0.01, ** p<0.05, *p<0.1. R2: Square correlation between 
actual and fitted values, used in count models as a fit measure (Cameron and Trivedi, 2010: 358). r2_p is the pseudo-R-squared. Chi2 is 
the LR for joint significance. 

16 

 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
Table 5. Estimation results for the rice sector. 

HS-6 100610 

HS-6 100620 

HS-6 100630 

HS-6 100640 

Technical 

Non- 
Technical 

Technica
l 

mt 

Tech × shf 

Tech × sht 

Tech × tafrica 

Tech : ftafrica = 0 

Tech : ftafrica = 1 

Nontech × shf 

Nontech × sht 

Nontech × tafrica 

Nontech : ftafrica = 0 

Nontech : ftafrica = 1 

lgdp_r 

lgdp_s 

rta 

sqincome 

ldist 

contig 

landlocked_r 

landlocked_s 

colony 

lang 

Constant 

Observations 
R2 
r2_p 
chi2 

-1.492 
(1.012) 
0.038*** 
(0.013) 
0.077*** 
(0.023) 
-0.105 
(0.077) 
0.000 
(0.007) 
0.008 
(0.116) 

0.680** 
(0.286) 
-0.022 
(0.191) 
2.173*** 
(0.610) 
-0.145*** 
(0.039) 
-0.820*** 
(0.265) 
0.716 
(0.637) 
0.108 
(0.854) 
-2.434*** 
(0.617) 
0.285 
(0.702) 
-1.066 
(0.725) 
3.337 
(4.163) 
10,917 
0.881 
0.678 
2192 

-2.897** 
(1.284) 

0.128 
(0.088) 
0.663*** 
(0.182) 
-0.555 
(0.476) 
0.032 
(0.037) 
0.400 
(0.448) 
0.752*** 
(0.266) 
0.190 
(0.194) 
1.791*** 
(0.623) 
-0.119*** 
(0.041) 
-0.826*** 
(0.258) 
1.220** 
(0.523) 
0.140 
(0.856) 
-2.348*** 
(0.672) 
0.356 
(0.656) 
-1.278* 
(0.686) 
-0.481 
(3.628) 
10,917 
0.852 
0.674 
1409 

-6.404*** 
(1.887) 
0.045*** 
(0.004) 
0.050*** 
(0.010) 
0.116*** 
(0.016) 
-0.000 
(0.002) 
-1.744*** 
(0.427) 

0.211** 
(0.095) 
0.313** 
(0.145) 
0.580 
(0.440) 
-0.033 
(0.056) 
0.609*** 
(0.192) 
3.174*** 
(0.560) 
-0.209 
(0.645) 
-1.140* 
(0.623) 
-0.628 
(0.511) 
0.464 
(0.443) 
-0.823 
(3.040) 
12,127 
0.674 
0.552 
2367 

Non- 
Technica
l 
-9.138*** 
(3.037) 

0.496*** 
(0.044) 
0.461*** 
(0.096) 
0.547*** 
(0.057) 
0.006 
(0.021) 
-3.003*** 
(0.819) 
0.176* 
(0.093) 
0.528*** 
(0.122) 
0.581 
(0.379) 
-0.043 
(0.042) 
0.617*** 
(0.208) 
3.303*** 
(0.592) 
-0.232 
(0.660) 
-0.673 
(0.598) 
-0.743 
(0.559) 
0.522 
(0.480) 
-3.276 
(3.098) 
12,127 
0.692 
0.624 
1876 

Technica
l 

-1.601* 
(0.957) 
0.093*** 
(0.013) 
0.070 
(0.043) 
-0.021 
(0.026) 
-0.017*** 
(0.004) 
-0.596* 
(0.319) 

0.583*** 
(0.078) 
0.528*** 
(0.100) 
0.171 
(0.297) 
-0.098*** 
(0.038) 
-0.988*** 
(0.198) 
0.133 
(0.547) 
-1.671** 
(0.658) 
-1.864*** 
(0.292) 
-0.911* 
(0.475) 
-0.591* 
(0.333) 
4.095** 
(1.922) 
13,768 
0.0987 
0.443 
658.1 

Non- 
Technica
l 
-1.931 
(1.348) 

0.806*** 
(0.065) 
0.621 
(0.573) 
0.267*** 
(0.088) 
-0.110*** 
(0.024) 
-0.881 
(0.547) 
0.602*** 
(0.080) 
0.561*** 
(0.095) 
0.121 
(0.290) 
-0.097*** 
(0.036) 
-1.065*** 
(0.206) 
0.124 
(0.566) 
-1.644** 
(0.656) 
-1.928*** 
(0.302) 
-0.986** 
(0.473) 
-0.552 
(0.345) 
3.807** 
(1.796) 
13,768 
0.100 
0.446 
685.2 

Technica
l 

-3.555*** 
(1.199) 
0.077*** 
(0.018) 
0.426*** 
(0.065) 
0.081*** 
(0.016) 
-0.016** 
(0.007) 
-1.236*** 
(0.288) 

0.524*** 
(0.154) 
-0.307** 
(0.132) 
1.159** 
(0.574) 
-0.275*** 
(0.059) 
-0.177 
(0.186) 
0.657 
(0.785) 
-2.276*** 
(0.657) 
-0.965 
(0.606) 
0.987* 
(0.596) 
-1.048** 
(0.501) 
14.935*** 
(1.686) 
11,723 
0.0667 
0.421 
283.0 

Non- 
Technica
l 
-2.317* 
(1.351) 

0.867*** 
(0.206) 
3.172*** 
(0.438) 
0.172 
(0.112) 
-0.175** 
(0.071) 
-1.578*** 
(0.471) 
0.498*** 
(0.139) 
0.103 
(0.130) 
0.566 
(0.479) 
-0.264*** 
(0.068) 
-0.294 
(0.232) 
0.889 
(0.799) 
-2.457*** 
(0.671) 
-0.444 
(0.707) 
0.732 
(0.585) 
-1.017* 
(0.531) 
11.679*** 
(1.529) 
11,723 
0.234 
0.443 
408.0 

Notes: Robust standard errors clustered by country pairs in parentheses. *** p<0.01, ** p<0.05, * p<0.1. R2: Square correlation between 
actual and fitted values, used in count models as a fit measure (Cameron and Trivedi, 2010: 358). r2 p is the pseudo-R-squared. Chi2 is 
the LR for joint significance.

17 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Table 6. Estimation results for the palm oil sector. 

mt 

Tech × shf 

Tech × sht 

Tech × tafrica 

Tech : ftafrica = 0 

Tech : ftafrica = 1 

Non-Technical 
-5.977 
(6.831) 

HS-6 151110 
Technical 
-0.817 
(1.076) 
0.118*** 
(0.022) 
0.200*** 
(0.036) 
0.066 
(0.058) 
-0.030*** 
(0.009) 
-0.171** 
(0.075) 

Non-Technical 
2.120 
(1.777) 

HS-6 151190 
Technical 
-1.011 
(1.568) 
0.206*** 
(0.033) 
0.146*** 
(0.035) 
-0.064 
(0.059) 
-0.097*** 
(0.021) 
-0.092 
(0.058) 

rta 

ldist 

lgdp_r 

lgdp_s 

sqincome 

Nontech × sht 

Nontech × shf 

Nontech × tafrica 

Nontech : ftafrica = 1 

Nontech : ftafrica = 0 

2.069*** 
(0.526) 
3.103*** 
(0.442) 
0.149 
(0.141) 
-0.613** 
(0.303) 
-0.172 
(0.194) 
0.207*** 
(0.072) 
0.489*** 
(0.067) 
1.264*** 
(0.389) 
0.085 
(0.080) 
-0.374 
(0.308) 
0.604 
(0.782) 
-11.650*** 
(0.738) 
-3.955*** 
(0.490) 
0.892 
(0.705) 
-0.238 
(0.377) 
-1.263 
(3.326) 
11,946 
0.882 
0.595 
1248 
Notes: Robust standard errors clustered by country pairs in parentheses. *** p<0.01, ** p<0.05, * p<0.1. R2: Square correlation between 
actual and fitted values, used in count models as a fit measure (Cameron and Trivedi, 2010: 358). r2 p is the pseudo-R-squared. Chi2 is 
the LR for joint significance. 

2.009*** 
(0.228) 
1.296*** 
(0.356) 
0.260* 
(0.142) 
-0.818*** 
(0.127) 
-0.144 
(0.176) 
0.455*** 
(0.047) 
0.815*** 
(0.129) 
0.529 
(0.441) 
-0.130** 
(0.061) 
-0.292 
(0.182) 
0.425 
(0.490) 
-4.091*** 
(0.947) 
-1.893*** 
(0.542) 
-0.775 
(0.713) 
-0.045 
(0.374) 
-4.195* 
(2.458) 
13,556 
0.487 
0.537 
1366 

0.253** 
(0.106) 
0.667*** 
(0.101) 
3.979*** 
(1.133) 
-0.047 
(0.065) 
-0.786*** 
(0.212) 
-0.590 
(0.726) 
-11.576*** 
(0.927) 
-2.991*** 
(0.567) 
-4.072*** 
(1.299) 
-0.149 
(0.393) 
-0.587 
(2.315) 
11,946 
0.740 
0.685 
752.5 

0.393*** 
(0.055) 
0.752*** 
(0.138) 
1.082*** 
(0.383) 
-0.126*** 
(0.048) 
-0.320* 
(0.173) 
0.144 
(0.453) 
-3.737*** 
(0.952) 
-1.364*** 
(0.492) 
-0.113 
(0.439) 
-0.136 
(0.276) 
-1.529 
(2.758) 
13,556 
0.600 
0.666 
1102 

Observations 
R2 
r2_p 
chi2 

landlocked_s 

landlocked_r 

Constant 

colony 

contig 

lang 

18 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
Table 7. Estimation results for the sugar sector. 

HS-6 170112 
Technical  Non-

HS-6 170113 
Technical  Non- 

HS-6 170114 
Technical  Non- 

HS-6 170191 
Technical  Non- 

HS-6 170199 
Technical  Non- 

mt 

-7.316* 
(4.416) 
Tech × shf  0.874*** 
(0.201) 
Tech × sht  0.853*** 
(0.145) 
-0.719** 

× 

Tech 
tafrica 

Technical 
-6.427* 
(3.533) 

Technical 
-13.709**  -33.752**  -1.563 
(1.614) 
(5.665) 
(14.757) 
0.177*** 
0.850** 
(0.050) 
(0.376) 
0.305** 
-0.376 
(0.128) 
(0.235) 
-0.378** 
0.000 

Technical 
-4.221* 
(2.271) 

Technical 
-12.362*** -14.443***  -2.730*** 
(0.534) 
(3.393) 
(3.441) 
0.212*** 
0.320 
(0.035) 
(0.231) 
0.349*** 
0.620*** 
(0.075) 
(0.155) 
-0.050 
0.101 

Technical 
-2.886*** 
(0.596) 

(0.337) 
-0.183***   
: 
Tech 
ftafrica = 0 

(0.049) 
-1.248** 
Tech 
: 
ftafrica = 1 

(0.518) 

(0.000) 
-0.313* 

(0.185) 
-0.144 

(0.211) 

(0.170) 
-0.062** 

(0.026) 
0.027 

(0.083) 

(0.098) 
-0.165***   

(0.035) 
-2.388***   

(0.657) 

(0.032) 
-0.021*** 

(0.004) 
-0.389* 

(0.200) 

3.255*** 

(1.055) 
-1.361 

(1.748) 
0.000 

(0.000) 
-0.494 

(0.422) 
-0.377 

(1.638) 
0.701*** 
(0.080) 
0.926* 
(0.531) 
-2.969 
(2.084) 
0.165 
(0.161) 
1.353** 
(0.629) 
-4.892 
(4.479) 
-2.514*** 

0.423*** 

(0.064) 
1.962*** 

(0.532) 
-0.923 

(0.902) 
-0.022 

(0.033) 
0.000 

(0.000) 
0.413*** 
0.430*** 
(0.093) 
(0.063) 
0.992*** 
0.814*** 
(0.209) 
(0.172) 
1.320*** 
2.510*** 
(0.451) 
(0.461) 
-0.187** 
-0.064 
(0.094) 
(0.055) 
-0.028 
-0.199 
(0.406) 
(0.448) 
-0.150 
-0.518 
(0.918) 
(0.716) 
-1.199***  -0.311 

0.429*** 
(0.084) 
0.012 
(0.178) 
1.975*** 
(0.518) 
-0.174*** 
(0.062) 
-1.294*** 
(0.257) 
2.632*** 
(0.652) 
-5.378*** 

0.949 

(0.720) 
4.271*** 

(1.380) 
0.381 

(0.252) 
-0.580*** 

(0.108) 
-6.013*** 

(0.988) 
0.524*** 
(0.076) 
0.030 
(0.132) 
2.437*** 
(0.671) 
-0.135** 
(0.058) 
-1.490*** 
(0.282) 
2.742*** 
(0.601) 
-5.335*** 

0.470*** 
(0.059) 
0.280*** 
(0.090) 
1.043*** 
(0.204) 
-0.102*** 
(0.031) 
-1.110*** 
(0.134) 
0.348 
(0.314) 
-0.949** 

1.154*** 

(0.207) 
2.558*** 

(0.843) 
0.168** 

(0.083) 
-0.102*** 

(0.028) 
-0.904* 

(0.496) 
0.447*** 
(0.055) 
0.385*** 
(0.085) 
0.900*** 
(0.204) 
-0.080** 
(0.033) 
-1.078*** 
(0.129) 
0.687** 
(0.341) 
-1.055*** 

Nontech  × 
shf 

Nontech  × 
sht 

Nontech  × 
tafrica 

Nontech 
: 
ftafrica = 0 

Nontech 
: 
ftafrica = 1 

3.920*** 

(1.261) 
3.508*** 

(1.152) 
0.000 

(0.000) 
-0.791*** 

(0.297) 
-1.996* 

(1.077) 
0.623*** 
0.619***  0.542*** 
(0.103) 
(0.192) 
(0.177) 
1.173*** 
0.590***  0.322** 
(0.378) 
(0.190) 
(0.143) 
-2.071** 
2.914***  2.639*** 
(1.040) 
(0.596) 
(0.636) 
-0.149** 
0.203 
-0.084 
(0.094) 
(0.218) 
(0.074) 
-1.290***  -1.093***  1.847* 
(1.084) 
(0.180) 
(0.173) 
1.691 
-0.272 
-0.919 
(1.912) 
(0.654) 
(0.738) 
-2.049*** 
1.228* 
1.125 

lgdp_r 

lgdp_s 

rta 

sqincome 

ldist 

contig 

landlocked_
r 

landlocked_
s 

colony 

lang 

Constant 

(0.731) 
1.840***  0.758 

(0.735) 

(0.651) 
(0.760) 
-2.403**  -2.959*** 

(0.355) 
-0.627 

(0.263) 
-0.207 

(1.054) 
1.941** 

(0.944) 
2.803*** 

(0.373) 
-0.601* 

(0.371) 
-0.593* 

(0.576) 

(0.455) 
-0.015  0.110 
(0.724) 
0.088 
(0.577) 
-0.797  2.615 
(2.949) 

(0.700) 
-0.406 
(0.651) 

(1.916) 

(0.584) 
(0.915) 
(1.151) 
0.620 
-0.028 
1.287 
(0.549) 
(0.908) 
(1.644) 
-1.392*** 
-2.495*** 
-2.240*** 
(0.379) 
(0.756) 
(0.847) 
-35.120**  -30.080***  -6.602 
(5.520) 
(4.807) 
(17.031) 

(0.811) 
0.542 
(0.573) 
-1.483** 
(0.739) 
-1.442 
(4.269) 

(0.652) 
(0.785) 
-1.281 
-0.332 
(0.820) 
(0.794) 
-3.864*** 
-3.315*** 
(0.622) 
(0.729) 
11.614***  9.616*** 
(2.461) 
(2.553) 

(0.355) 
1.208*** 
(0.408) 
-0.441 
(0.307) 
7.967*** 
(1.628) 

(0.351) 
1.175*** 
(0.402) 
-0.461 
(0.295) 
6.131*** 
(1.397) 

19 

 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
7,753 

7,708 

7,792 

7,902 

11,776 

Observation
s 
R2 
0.0620 
0.478 
r2_p 
chi2 
406.0 
Notes: Robust standard errors clustered by country pairs in parentheses. *** p<0.01, ** p<0.05, * p<0.1. R2: Square correlation between 
actual and fitted values, used in count models as a fit measure (Cameron and Trivedi, 2010: 358). r2 p is the pseudo-R-squared. Chi2 is 
the LR for joint significance. 

0.0686  0.0479 
0.524 
224.7 

0.0928 
0.577 
210.5 

0.251 
0.532 
759.5 

0.128 
0.465 
214.3 

0.136 
0.509 
237.3 

0.963 
0.786 
351.0 

0.968 
0.786 
337.2 

0.198 
0.536 
634.5 

0.445 
322.1 

11,652 

11,109 

11,109 

13,345 

13,345 

Based  on  the  coefficients  previously  calculated  in  the  first  three  columns  of  Table  8,  and  based  on  the 
estimated coefficients in Tables 4 to 7, we calculated the NTM AVEs applying equation (4), making use of the 
tariff own estimated coefficient. For comparison purposes, we present the equivalent summarised results based 
on publicly available databases in Table 9. 

Even if NTMs have a significant impact on trade, the relatively large magnitude of the tariff elasticities leads 
to relatively small AVEs. In agreement with Kee and Nicita (2016), the tariff elasticities obtained with bilateral 
trade  and  as  such  a  high  degree  of  sector  granularity,  tend  to  be  different  from  those  derived  from  other 
estimation approaches.  Nevertheless, with the exception  of palm oil where  there is no significant impact of 
tariff,  the  remaining  sectors  show  significant  tariff  coefficients  (at  least  in  one  of  the  NTM  category 
specification). Besides, these tariff coefficients move in the range of GTAP substitution elasticities. For instance, 
the GTAP elasticity for corn is σ = 2.60 (i.e., considering the equivalence βmt =1- σ = -1.60), which is between 
our estimated values for both subsectors, -1.29 and -4.63. For the remaining sectors, our tariff estimates are 
remarkably close to the equivalent GTAP substitution elasticities. Thus, the GTAP elasticity for processed rice is 
5.20 and our average tariff elasticity for its constituent sectors is  -4.16; and the GTAP elasticity for sugar is 
5.40, and our average estimate for 4 subsectors is -5.20 (excluding 170113). In this sense, we can conclude 
that the NTM AVEs provided will have certain consistency with the use of GTAP elasticities in the simulation 
model. 

The AVE estimates vary across sectors and NTM categories, and interestingly, between African importers and 
other importers in the sample. 

Looking  at  overall  figures,  with  the  exception  of  one  corn  sector  (100590)  with  a  maximum  AVE  for  non-
technical measures of 63%, the rest of the sectors move between 1 and 18%, with a majority under 5%. Corn 
is affected by higher AVEs, around 11-14% (excluding the aforementioned case), while rice AVEs are in the 
range 1-8%, palm oil between 3 and 11%, and sugar, between 1.8 and 5% (with the exception of 18% AVE of 
non-technical measures in sugar beet (170112)). 

AVEs for non-technical measures tend to be larger than for technical measures, and this regularity happens 
irrespectively of the geographical dimension considered. When looking at global figures, this gap is around 6 
percentage points in processed broken rice (100640) or crude palm oil (151110), reaching a maximum gap of 
13 percentage points in beet sugar (170112).  

Trade costs induced by NTMs are significantly higher when accessing African countries than on average in the 
sample,  and  can  increase  even  more  for  African  exporters  accessing  African  markets.  Specially,  high  NTM 
induced trade costs in African routes are found within rice and sugar sectors, reaching two-digit values. For 
instance, technical measures induce a trade cost equivalent to 29-47% in rice, and 17-25% in sugar.  

As observed by looking at Table 9, the variability of AVE results in the literature is large, even for the same 
sector, and it is difficult to extract a pattern. There is not a regular indication about non-technical measures 
being more trade-restrictive than technical, although theoretically, non-technical have traditionally been viewed 
as more aligned with protective goals. Interestingly though, the AVEs found by the studies reported in Table 9, 
tend to be lower for African than other countries, with only a few exceptions. 

In comparison to the values reported in Table 9, our estimates are in general more conservative, both, globally 
and for the African countries. Likewise, our results cluster around similar values within the same broad product 
categories, show a lower dispersion of values, and consistently show higher trade impacts and costs of non-
technical measures.  

20 

 
 
Table 8. Trade impacts and AVE of NTMs (%), by HS-6 code and NTM category. 

Trade  impact  for  one  additional 
measure 1,2  

AVE of NTMs (%) 

Number 
NTMs (mean) 

of 

Market share 
(%) 3 

Trade 
weight (%) 4 

African 
importers 
2.1 

0.3 

6.4 

13.7 

13.7 

15.8 

151110 

100640 

100630 

100610 

100510 

100620 

100590 

Overall mean 

HS-6 

NTM category 

Intra-
African 
-0.46 n.s. 
-1.03 n.s. 
-0.88 n.s. 
-2.09 

Overall mean  African importers 

Intra-African  African importers Intra-African 

Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 
Technical 
Non-Technical 

11.3 
11.6 
14.5 
62.8 
1.4 
2.1 
1.1 
0.04 
3.7 
4.9 
1.8 
8.1 
2.7 
8.8 
11.2 
-- 
4.9 
18.3 
2.4 
1.6 
4.5 
4.7 
1.9 
4.9 
1.8 
2.9 
1 Trade impacts are marginal measures, ceteris paribus the remaining variables in the model. Calculated as in (3), replacing market shares and dummies by averages in the subsample of analysis. These are equivalent 
to coefficients and need to be interpreted as proportional change in trade (% if multiplied by 100). 
2 n.s.: statistically non-significant; in bold, sectors where intra-African NTMs are more trade stringent. 
3 Market share of imports from African countries over imports of African importers. 
4 Weight of the HS-6 sector imports over the 13 HS-6 sectors imports in Africa. 

-0.49 
-0.98 
-0.21 
-0.63 
-0.02 n.s. 
-0.06 n.s. 
-0.07 
-0.03 n.s. 
-0.06 
-0.09 
-0.06 
-0.18 
-0.02 
-0.51 
-0.11 
-0.69 
-0.35 
-1.08 
-0.32 
-0.52 n.s. 
-0.07 
-0.19 n.s. 
-0.23 
-0.69 
-0.05 
-0.08 

African 
importers 
-2.44 
-4.33 
-0.92 
-0.61 
-0.10 n.s.  -0.10 n.s. 
-0.44 n.s.  -0.15 n.s. 
-0.33 
-1.63 
-0.21 n.s.  -2.45 
-0.21 
-0.61 
-0.07 n.s.  -0.61 n.s. 
-0.26 
-1.14 
-0.30 n.s.  -1.31 
-0.004 n.s. -0.10 
-0.02 n.s. 
-0.32 
-0.16 
-0.16 
-0.12 n.s. 
-0.34 
-1.96 
-1.06 
-1.96 
-2.63 
-0.41 
-0.14 n.s. 
-0.77 n.s.  -0.37 n.s. 
-0.11 n.s.  -0.35 
-0.93 n.s.  -0.91 
-2.27 
-0.54 
-5.57 
-1.33 
-0.44 
-0.18 
-0.15 n.s.  -0.72 n.s. 

5.30 
1.00 
4.62 
0.71 
8.48 
1.38 
7.76 
1.19 
7.57 
1.24 
7.21 
1.26 
6.58 
1.00 
7.19 
1.14 
5.41 
0.94 
3.56 
0.31 
3.52 
0.24 
5.15 
0.90 
5.19 
0.86 

10.5 
12.1 
76.4 
-- 
6.7 
5.5 
29.0 
3.08 
47.0 
37.3 
37.9 
76.4 
13.7 
0.3 
16.6 
-- 
30.7 
35.7 
1.1 
1.1 
25.1 
24.2 
20.2 
47.1 
17.4 
28.5 

69.5 
62.0 
80.6 
-- 
7.1 
16.4 
5.3 
2.4 
14.4 
3.8 
7.6 
13.6 
0.4 
5.5 
16.9 
-- 
15.5 
50.6 
3.0 
2.3 
7.5 
24.7 
4.5 
9.6 
6.7 
5.4 

151190 

170114 

170112 

170191 

170113 

170199 

43.0 

15.3 

16.4 

31.4 

15.4 

3.0 

0.3 

1.2 

0.6 

0.2 

9.5 

3.4 

1.2 

9.1 

2.7 

6.6 

1.4 

8.1 

2.6 

1.0 

21 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table 9. AVE of NTMs obtained by previous publically available databases (%). 

KNO2009  

(Technical & Non-Technical) 
African 
countries 

All countries 

KNO2019 

Technical 

Non-Technical 

African countries  All countries  African countries  All countries 

132.91 
35.42 
24.06 
21.47 
165.07 
30.28 
92,31 
0.00 
118.26 
25.21 
- 
- 
40.50 
3.27 

142.29 
43.66 
54.77 
10.03 
211.53 
9.07 
106.75 
2.99 
131.67 
73.87 
- 
- 
22.34 
11.17 

- 
- 
79.69 
- 
12.37 
- 
- 
4.49 
- 
13.61 
- 
- 
0.00 
4.44 

- 
- 
60.28 
- 
78.02 
- 
- 
4.74 
- 
16.34 
- 
- 
1.46 
3.89 

- 
- 
4.63 
- 
23.41 
- 
- 
- 
- 
7.85 
- 
- 
78.04 
2.23 

- 
- 
3.29 
- 
46.78 
- 
- 
- 
- 
6.55 
- 
- 
36.80 
1.32 

HS-6 

100510 
100590 
100610 
100620 
100630 
100640 
151110 
151190 
170111 
170112 
170113 
170114 
170191 
170199 

Results for the PRONTO database are not shown since there are very few AVEs calculated for the African countries, and most of the AVEs obtained are  negative and non-significant. Figures shown are the mean 
value of the AVEs obtained for the African selected countries and for all countries available in the dataset indicated in the head of the table. The number of countries is not always the same for the different sectors 
and databases. 

22 

 
 
5  Conclusions 

On the 1st of January 2021, African countries began officially trading under the new continent-wide free trade area. 
Fifty-four  out  of  55  members of  the  African  Union  have  already  signed  the  African  Continental  Free  Trade  Area 
(AfCFTA) and 36 states have ratified it. The treaty aims to progressively eliminate tariffs and non-tariff measures 
(NTMs) and to facilitate trade in goods via enhanced co-operation in the areas of technical barriers to trade and 
sanitary and phytosanitary measures. It presents the African continent with the opportunity for creating the world’s 
largest free trade area and meeting the African Union’s Agenda 2063 and the Sustainable Development Goals.  

Despite significant interest in measuring the impact of reducing tariffs and NTMs within the AfCFTA, the literature 
on intra-African NTMs and its quantification is still extremely scarce. The collection of detailed and comparable NTMs 
data across sectors and countries has gained momentum in recent years, although the quantification of their impact 
on trade and the estimation of their  ad-valorem equivalents (AVEs) remains challenging with no clear consensus 
emerging in the existing literature. The revision of available datasets has revealed three main databases that provide 
AVEs of NTMs, although the coverage of African countries remains limited. 

This report reviewed the available datasets on this topic; whilst to complement the lack of AVE estimates of NTMs, 
it provides a first attempt to quantify intra-African NTMs for a series of key agri-food products. 

More specifically, selected according to their weight in intra-African trade, estimates for 13 HS-6 commodities within 
the categories of corn, rice, palm oil and sugar are provided. Compared to other countries, African countries impose 
on average a lower number of NTMs on these selected products, while the regulatory burden on these sectors is 
higher than in other agri-food sectors. 

Although, as in previous analysis, the heterogeneity is large, some patterns have emerged that are worth highlighting. 
Firstly, and contrary to some of the available secondary data on AVEs (e.g., PRONTO), we find a systematic trade-
restricting  effect  of  both  technical  and  non-technical  measures.  Secondly,  we  find  a  tendency  for  non-technical 
measures to be more trade-restrictive and costly for bilateral trade than technical measures. This result is coherent 
with  the  general  wisdom that  non-technical  measures  pursue  protective  goals while  technical  measures  address 
societal  and  health  concerns.  Thirdly,  our  estimated  tariff  elasticities,  although  subject  to  a  certain  degree  of 
variability across sectors and specifications, are in  most cases in the neighbourhood of those  used  by  GTAP. We 
consider this important, as it means that the AVE estimates are  coherent with the simulation model parameters. 
Fourthly, we do find significant deviations of estimated AVEs for Africa from the overall means in the sample of 
countries. Finally, our estimates highlight that the main hotspots for NTMs in intra-African trade would be in sectors 
like rice and sugar, while the main policy actions need to address non-technical measures. 

With  previous  evidence  highlighting  how  NTM  elimination  and  harmonisation,  together  with  trade  facilitation 
measures, will be the key to boost intra-Africa trade and to boost the continental economic growth, this initial attempt 
to estimate intra-Africa NTMs represents a valuable contribution to the AfCFTA literature. 

Empirically, future research could focus on two particular aspects related to the heterogeneity of NTMs. In the current 
report, for example, we only focus on how NTMs applied by the importer impact on its trade with individual exporting 
partners. Thus, a first line would go a step further to examine the issue of NTM harmonisation between trade partners. 
More specifically, the research would examine  how the ‘degree of similarity’ of NTMs applied by pairs of trading 
partners  on  their  imports,  affects  agri-food  trade  ties.  Secondly,  at  a  higher  level  of  granularity,  analyse  the 
heterogeneous  impact  of  individual  types  of  technical  and  non-technical  measures,  to  provide  a  more  detailed 
description of trade restricting/enhancing impacts, thereby better informing policy decision making. 

23 

 
References 

Aguiar, A., Narayanan, B. and McDougall, R. (2016). An Overview of the GTAP 9 -Data Base. Journal of Global Economic 
Analysis 1 (1): 181-208. doi: dx.doi.org/10.21642/JGEA.010103AF. 

Baltagi, B. H., Egger, P. and Pfaffermayr, M. (2003). A generalized design for bilateral trade flow models. Economics 
Letters 80 (3): 391-397. doi: https://doi.org/10.1016/S01650.001765(03)001150.000. 

Bratt, M. (2017). Estimating the bilateral impact of nontariff measures on trade. Review of International Economics 
25: 1105-1129. doi: https://doi.org/10.1111/roie.12297 

Cadot,  O.,  Gourdon,  J.  and  van  Tongeren,  F.  (2018).  Estimating  ad-valorem  equivalents  of  Non-Tariff  Measures: 
Combining Price-Based and Quantity-Based approaches, OECD Trade Policy Papers, No.125, OECD Publishing, Paris. 
doi: http://dx.doi.org/10.1787/f3cd5bdc-en 

Cameron, A. and Trivedi, P. (2010). Microeconometrics Using Stata. Revised Edition. College Station, USA: Stata Press.  

Chen,  N.A.  and  Novy,  D.  (2011).  Gravity,  Trade  Integration  and  Heterogeneity  across  Industries.  Journal  of 
International Economics 85(2): 206-221. 

Disdier, A.C., Fekadu, B., Murillo, C. and Wong, S.A. (2008). Trade effects of SPS and TBT measures on tropical products. 
ICTSD Issue Paper 12. 

Egger, P. and Larch, M. (2008). Interdependent preferential trade agreement memberships: An empirical analysis. 
Journal of International Economics 76: 384–399. DOI: 10.1016/j.jinteco.2008.08.003 

Feenstra,  R.C, 
http://www.rug.nl/research/ggdc/data/pwt/pwt-8.0. 

Inklaar,  R.  and  Timmer,  M.P. 

(2013).  PWT  8.0  –  a  user  guide.  Available  at: 

Feenstra, R.C., Inklaar, R. and Timmer, M.P. (2015). The Next Generation of the Penn World Table, American Economic 
Review 105 (10): 3150-3182. 

Ferrantino, M. (2006). Quantifying the Trade and Economic Effects of Non-Tariff Measures, OECD Trade Policy Papers, 
No. 28, OECD Publishing, Paris, https://doi.org/10.1787/837654407568. 

Ghodsi,  M.,  Grübler,  J.  and  Stehrer,  R.  (2016).  Estimating  importer-specific  ad  valorem  equivalents  of  Non-Tariff 
Measures, wiiw Working Paper, No. 129, The Vienna Institute for International Economic Studies (wiiw), Vienna. 

Head, K. and Mayer, T. (2004). The empirics of agglomeration and trade. In: Vernon Henderson, J. and Thisse, J.F. 
(eds.), Handbook of Regional and Urban Economics (pp. 2609-2669), Elsevier, North-Holland. 

Helpman,  E.,  Melitz,  M.  and  Rubinstein,  Y.  (2008).  Trading  Partners  and  Trading  Volumes.  Quarterly  Journal  of 
Economics 123(2): 441-487. 

Hummels, D. and Klenow, P. (2005). The variety and quality of a nation’s trade.  American Economic Review 95(3): 
704-723. 

Kee,  H.L.,  Nicia,  A.  and  Olarreaga,  M.  (2008).  Import  demand  elasticities  and  trade  distortions.  The  Review  of 
Economics and Statistics 90 (4): 666-682.  

Kee, H.L., Nicia, A. and Olarreaga, M. (2009). Estimating trade restrictiveness indices. The Economic Journal 119 (534): 
172-192. doi: https://doi.org/10.1111/j.1468-0297.2008.02209.x. 

Kee, H.L. and Nicita, A. (2016). Trade Frauds, Trade Elasticities and Non-Tariff Measures. World Bank Mimeo. Available 
at: http://pubdocs.worldbank.org/en/315201480958601753/3-KEE-paper.pdf. 

Leamer, E. (1990). Latin America as a target of trade barriers erected by the major developed countries in 1983. 
Journal of Development Economics 32 (2), 337-68. doi: https://doi.org/10.1016/0304-3878(90)90042-A. 

Liu, C., Lin, D., Liu, J. and Li, Y. (2019). Quantifying the effects of non-tariff measures on African agri-food exporters. 
Agrekon 58 (4): 451-471. https://doi.org/10.1080/03031853.2019.1581624 

Mayer, T. and Zignago, S. (2011). Notes on CEPII’s Distances Measures: the GeoDist Database. CEPII Working Paper 
No. 2011-25, Centre d’Études Prospectives et d’Information Internationales, Paris. 

Nimenya, N., Ndimira, P.F. and de Frahan, B.H. (2012). Tariff equivalents of nontariff measures: the case of European 
horticultural and fish imports from African countries. Agricultural economics 43 (6): 635-653. 

Niu, Z., Liu, C., Gunessee, S. and Milner, C. (2018). Non-tariff and overall protection: evidence across countries and 
over time. Review of World Economics 154: 675-703. https://doi.org/10.1007/s10290-018-0317-5 

24 

 
Santeramo,  F.  G.  and  Lamonaca, E.  (2019).  On  the  impact  of  non-tariff  measures on  trade  performances  of  the 
African agri-food sector. Agrekon 58 (4): 389-406. doi: 10.1080/03031853.2019.1568889 

Santos Silva, J. M. C. and Tenreyro, S. (2006). The log of gravity. Review of Economics and Statistics, 88: 641–58. 

Stender, F. and Vogel, T., (2021) Murky Trade Waters: Regional Tariff Commitments and Non-tariff Measures in Africa. 
Available at SSRN: https://ssrn.com/abstract=3792088 or http://dx.doi.org/10.2139/ssrn.3792088 

UNCTAD (2015). International classification of non-tariff measures. New York: United Nations Conference on Trade 
and  Development.  Available  at:  https://unctad.org/en/Pages/DITC/Trade-Analysis/Non-Tariff-Measures/NTMs-
Classification.aspx. 

UNCTAD  (2017).  TRAINS  NTMs:  The  Global  Database  on  Non-Tariff  Measures.  Accesed  November  2020. 
https://trains.unctad.org/Forms/Analysis.aspx. 

UNCTAD (2019a). Economic Development in Africa Report 2019: Made in Africa - Rules of origin for Enhanced Intra-
African Trade. New York: United Nations Conference on Trade and Development. ISBN: 978-92-1-112943-4. 

UNCTAD  (2019b).  State  of  Commodity  Dependence  2019.  New  York:  United  Nations  Conference  on  Trade  and 
Development. ISBN: 978-92-1-112945-8. 

Vanzetti, D., Peters, R. and Knebel C. (2018) Non-tariff measures: lifting CFTA and ACP trade to the next level, UNCTAD 
Research Paper 14, https://unctad.org/system/files/official-document/ser-rp-2017d14_en.pdf 

Wooldridge, J. M. (2012). Econometric Analysis of Cross Section and Panel Data. Cambridge, Massachusetts, London, 
England: The MIT Press. 

World Bank (2020). The African Continental Free Trade Area: Economic and Distributional Effects. Washington D.C.: 
The 
doi:10.1596/978-1-4648-1559-1. 
https://openknowledge.worldbank.org/bitstream/handle/10986/34139/9781464815591.pdf 

World 

Bank. 

25 

 
 
List of abbreviations and definitions 

AfCFTA 

African Continental Free Trade Agreement 

AMV 

AU 

AVE 

BIAT 

Arab Maghreb Union 

African Union 

Ad-valorem Equivalent 

Boosting Intra-African Trade 

CEN-SAD   

Community of Sahel-Saharan States 

COMESA   

Common Market of Eastern and Southern Africa 

COMTRADE 

International Trade Statistics Database of United Nations 

EAC 

ECCAS 

East African Community 

Economic Community of Central African States 

ECOWAS   

Economic Community of Western African States 

FTAs 

GDP 

GTAP 

HS 

IGAD 

ITC 

NTMs 

PPML 

Free Trade Agreements 

Gross Domestic Product 

Global Trade Analysis Project 

Harmonized System classification of products 

Intergovernmental Authority on Development 

International Trade Centre 

Non-tariff measures 

Poisson pseudo-maximum likelihood 

PRONTO 

Productivity, Non-Tariff Measures and Openness 

RECs 

RTAs 

SACU 

SADC 

SPS 

TBT 

TRAINS 

UEMOA 

Regional Economic Communities 

Regional Trade Agreements 

South African Custom Union 

Southern African Development Community 

Sanitary and Phytosanitary measures 

Technical Barriers to Trade 

Trade Analysis and Information System 

African Economic and Monetary Union 

UNCTAD 

United Nations Conference on Trade and Development 

WITS 

WTO 

World Integrated Trade Solution 

World Trade Organization 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
SDN 

Sudan 

ZMB 

Zambia 

ZWE 

Zimbabwe 

Abbreviations of African country ISO-codes 

LSO 

NAM 

ZAF 

SWZ 

KEN 

RWA 

TZA 

UGA 

BEN 

BFA 

CPV 

CIV 

GMB 

GHA 

GIN 

GNB 

LBR 

MLI 

MRT 

NER 

NGA 

SHN 

SEN 

SLE 

TGO 

AGO 

BDI 

COM 

COD 

DJI 

EGY 

ERI 

ETH 

Lesotho  

Namibia  

South Africa  

Swaziland  

Kenya  

Rwanda  

Tanzania  

Uganda  

Benin  

Burkina Faso  

Cape Verde  

Côte d’Ivoire  

Gambia  

Ghana  

Guinea  

Guinea-Bissau  

Liberia  

Mali  

Mauritania  

Niger  

Nigeria  

Saint Helena  

Senegal  

Sierra Leone  

Togo  

Angola  

Burundi  

Comoros  

Democratic Republic of the Congo  

Djibouti  

Egypt  

Eritrea  

Ethiopia  

MDG 

Madagascar  

MWI 

MYT 

SYC 

Malawi  

Mayotte  

Seychelles  

SOM 

Somalia  

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Abbreviations of GTAP sector codes 

B_T  Beverages and tobacco products 

CMT  Cattle meat 

CTL  Cattle 

C_B  Cane and beet sugar 

FSH  Fishing 

GRO  Other grains 

MIL  Dairy products 

OAP  Other animal products 

OCR  Other crops 

OFD  Other food 

OMT  Other meat 

OSD  Oil seeds 

PCR  Processed rice 

PDR  Rice 

PFB  Fibre crops 

SGR  Sugar and molasses 

VOL  Vegetable oils 

V_F  Vegetables and fruits 

WHT  Wheat 

WOL  Wool 

28 

 
 
List of tables 

Table 1. Products selected and value of intra-African RECs’ trade in 2018 ............................................. 5 

Table 2. African importers included in the publically available databases on AVE of NTMs ............................ 8 

Table 3. Selected HS-6 digit products for the estimation of NTMs AVEs................................................ 12 

Table 4. Estimation results for the corn sector ............................................................................ 16 

Table 5. Estimation results for the rice sector. ............................................................................ 17 

Table 6. Estimation results for the palm oil sector. ....................................................................... 18 

Table 7. Estimation results for the sugar sector. .......................................................................... 19 

Table 8. Trade impacts and AVE of NTMs (%), by HS-6 code and NTM category. ...................................... 21 

Table 9. AVE of NTMs obtained by previous publically available databases (%). ....................................... 22 

Table A1.Value of trade between the African RECs for the selected products in 2018 (in thousand US$). .......... 30 

Table A2. Value of trade between the African RECs for the selected products in 2018 (% over African trade, per 
product). ...................................................................................................................... 31 

Table A3. Products coverage of the NTM AVE databases, for mostly traded sectors and selected African countries.
 ................................................................................................................................ 33 

Table A4. Number of NTMs (mean) by HS-4 code, country and NTM category ......................................... 35 

Table A5. Number of NTMs (mean) by GTAP sector code, country and NTM category ................................. 37 

29 

 
 
Annexes 

Annex 1. Additional Data 

Table A1.Value of trade between the African RECs for the selected products in 2018 (in thousand US$). 

HS-4 code 

EAC-EAC 

SACU-SACU 

ECOWAS-
ECOWAS 

COMESA-
COMESA 

EAC-SACU 

EAC-
ECOWAS 

EAC-
COMESA 

SACU-EAC 

0303 

19.62 

49,808.97 

453,368.41 

 7,697.46  

0401 

76,827.53 

9,953.37 

1,194.81 

 216.75  

0.00 

0.17 

0.00 

0.00 

 26.82  

40.97 

 65.23  

2,635.23 

0402 

24,849.99 

8,473.12 

17,754.86 

 29.42  

159.32 

0.00 

 12,793.40  

2,867.90 

0901 

11,416.96 

7,436.14 

3,623.01 

 1,571.63  

11,028.27 

216.66 

 4,834.41  

0902 

15,707.02 

5,690.80 

997.25 

 2,483.55  

5,221.67 

16,557.35 

 267,587.13  

47.76 

89.95 

1001 

751.68 

4,435.86 

313.69 

 68.79  

0.00 

4,487.15 

 25.74  

114.19 

1005 

86,436.16 

26,395.89 

1,963.78 

 12,994.75  

1006 

30,198.90 

7,897.84 

14,744.74 

 115.15  

1511 

6,039.38 

762.58 

180,816.09 

 2,686.13  

1701 

1,110.88 

252,031.91 

16,091.88 

 13,082.46  

2201 

14.59 

3,141.02 

335.02 

 29.13  

0.32 

0.09 

0.02 

0.01  

1.17 

2202 

2,797.94 

28,015.80 

22,107.46 

 18,628.46  

10.11 

2203 

4,402.93 

86,451.69 

3,178.97 

 23.80  

2204 

2205 

2206 

39.74 

46,070.03 

3,408.49 

0.00 

3,742.34 

56.33 

 2.79  

 4.84  

9.45 

17,398.23 

263.66 

 7,697.46  

2207 

2,802.56 

134.91 

6,544.60 

 216.75  

2208 

9,362.77 

33,746.96 

1,224.11 

 29.42  

1.71 

0.12 

0.00 

0.06 

1.32 

0.44 

0.00 

0.33 

0.00 

0.07 

0.50 

0.52 

0.00 

0.50 

0.00 

0.00 

0.00 

0.00 

 9,342.16  

4,548.53 

 2,039.06  

24.46 

 1,499.97  

889.76 

 534.33  

25,293.20 

 0.97  

5.15 

 320.97  

2,187.25 

 491.19  

7,756.02 

 27.46  

21,628.56 

 0.04  

0.38 

 11.08  

1,276.75 

 1,061.84  

10,906.07 

 268.28  

4,012.97 

HS-4 code 

SACU-
ECOWAS 

SACU-
COMESA 

ECOWAS-EAC 

ECOWAS-
SACU 

ECOWAS-
COMESA 

COMESA-
EAC 

COMESA-
SACU 

COMESA-
ECOWAS 

0303 

2,133.95 

 104,394.12  

0401 

89.50 

1,233.54  

0402 

1,342.79 

13,018.61  

0901 

0902 

1001 

429.77 

1,194.90  

154.24 

5,291.89  

0.00 

6,472.42  

1005 

9,366.25 

23,875.57  

1006 

1511 

1701 

2201 

42.23 

16,127.91  

2.80 

40,956.71  

525.18 

26,256.90  

12.11 

431.44  

2202 

1,035.96 

6,011.77  

2203 

2.02 

23,682.40  

2204 

2,787.82 

16,579.34  

2205 

0.15 

 18.79  

2206 

4,049.53 

4,636.87  

2207 

12,377.95 

55,945.19  

2208 

645.50 

 6,665.10  

0.00 

0.00 

0.14 

0.00 

0.03 

0.00 

0.00 

0.00 

0.00 

0.00 

0.00 

0.96 

0.17 

0.00 

0.00 

0.00 

0.00 

0.00 

Source: own calculations based on UN COMTRADE. 

1,692.68 

85,644.33  

0.46 

 0.28  

0.00 

0.00 

253.37 

1,282.58  

35.49  

247.49  

127,289.83  

7.03  

2.17  

5.19  

286.26  

1.70 

210.87  

1,479.99  

3,586.56  

15.56  

469.61 

 10.76  

974.85  

25,794.65  

767.73  

0.11 

0.24 

0.28 

 35.69  

0.00 

10.23  

0.00 

 0.25  

47,480.02  

2,464.07  

177.86  

 368.98  

219.77  

4.75  

44.76 

 312.58  

831.72  

63.07  

3.72  

0.00 

0.09 

 85,644.33  

72,173.52  

14,812.11  

1,584.65  

1.66 

139.89 

109.09 

2.40 

0.52 

1.89 

0.30 

0.00 

0.08  

13.57  

2.09  

 5.53  

787.84  

373.54  

747.74  

 6.96  

28.67  

96.10  

2,332.07  

 20.70  

 0.31  

 0.25  

0.00 

0.00 

0.00 

0.84  

25.47  

247.49  

0.00 

54.69  

0.00 

49.40  

0.00 

0.00 

3.22  

137.90 

13.44 

118.58  

42.38  

38.71  

30 

 
 
 
 
 
 
 
 
 
 
 
Table A2. Value of trade between the African RECs for the selected products in 2018 (% over African trade, per product). 

HS-4 

EAC-
EAC 

SACU-
SACU 

ECOWAS-
ECOWAS 

COMESA-
COMESA 

EAC-
SACU 

EAC-
ECOWAS 

EAC-
COMESA 

SACU-
EAC 

SACU-
ECOWAS 

SACU-
COMESA 

ECOWAS-
EAC 

ECOWAS-
SACU 

ECOWAS-
COMESA 

COMESA-
EAC 

COMESA-
SACU 

COMESA-
ECOWAS 

0303 

0% 

6% 

54% 

1701 

0% 

50% 

0902 

5% 

1511 

3% 

2% 

0% 

1005  38% 

12% 

2203 

3% 

67% 

0401  83% 

11% 

2204 

0% 

51% 

2207 

3% 

0% 

2202 

3% 

34% 

0402  30% 

10% 

1006  42% 

11% 

2208  17% 

60% 

0901  24% 

16% 

2206 

0% 

49% 

1001 

4% 

27% 

2201 

0% 

79% 

2205 

0% 

97% 

3% 

0% 

77% 

1% 

2% 

1% 

4% 

7% 

27% 

21% 

21% 

2% 

8% 

1% 

2% 

8% 

1% 

1% 

3% 

1% 

1% 

6% 

0% 

0% 

0% 

0% 

22% 

0% 

0% 

0% 

3% 

22% 

0% 

1% 

0% 

0% 

0% 

2% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

23% 

0% 

0% 

0% 

0% 

0% 

0% 

5% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

27% 

0% 

0% 

0% 

0% 

77% 

1% 

4% 

0% 

0% 

0% 

1% 

0% 

15% 

3% 

0% 

10% 

0% 

0% 

0% 

0% 

0% 

5% 

0% 

0% 

2% 

6% 

3% 

24% 

12% 

3% 

3% 

0% 

7% 

0% 

4% 

1% 

0% 

0% 

0% 

0% 

0% 

0% 

4% 

0% 

0% 

3% 

14% 

1% 

2% 

0% 

1% 

1% 

11% 

0% 

0% 

0% 

13% 

5% 

2% 

17% 

11% 

18% 

1% 

18% 

62% 

7% 

16% 

22% 

12% 

3% 

13% 

39% 

11% 

0% 

Source: own calculations based on Table A1. Products ordered from most to least traded. The sum by row is 100%. 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

10% 

17% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

2% 

1% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

14% 

0% 

0% 

21% 

0% 

0% 

0% 

0% 

1% 

0% 

0% 

0% 

3% 

0% 

0% 

0% 

0% 

0% 

3% 

7% 

0% 

1% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

8% 

1% 

0% 

0% 

1% 

15% 

0% 

0% 

0% 

0% 

2% 

0% 

0% 

0% 

1% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

0% 

31 

 
Table A3. Products coverage of the NTM AVE databases, for mostly traded sectors and selected African countries. 

KNO2009 

KNO2019 

PRONTO 

HS-6 code 
030310 
030311 
030312 
030313 
030314 
030319 
030321 
030322 
030323 
030324 
030325 
030326 
030329 
030331 
030332 
030333 
030334 
030339 
030341 
030342 
030343 
030344 
030345 
030346 
030349 
030350 
030351 
030352 
030353 
030354 
030355 
030356 
030357 
030360 
030361 
030362 
030363 
030364 
030365 
030366 
030367 
030368 
030369 
030371 
030372 
030373 
030374 
030375 
030376 
030377 
030378 
030379 
030380 
030381 
030382 
030383 
030384 
030389 
030390 
040110 
040120 
040130 

33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
KNO2009 

KNO2019 

PRONTO 

HS-6 code 
040140 
040150 
040210 
040221 
040229 
040291 
040299 
090111 
090112 
090121 
090122 
090130 
090140 
090190 
090210 
090220 
090230 
090240 
100110 
100111 
100119 
100190 
100191 
100199 
100510 
100590 
100610 
100620 
100630 
100640 
151110 
151190 
170111 
170112 
170113 
170114 
170191 
170199 
220110 
220190 
220210 
220290 
220300 
220410 
220421 
220429 
220430 
220510 
220590 
220600 
220710 
220720 
220810 
220820 
220830 
220840 
220850 
220860 
220870 
220890 

34 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A4. Number of NTMs (mean) by HS-4 code, country and NTM category 

NTM category  HS-4 

BFA  BWA 

ALL 

TEC 

NON-TEC 

BEN 
0303  18.00  4.00 
2.00 
9.00 
0401 
0402 
2.00 
9.00 
0901  13.00  2.00 
0902  11.00  2.00 
3.50 
4.00 
1001 
1005 
6.57 
5.11 
1006  14.50  5.50 
1511  11.00  2.00 
6.50 
7.00 
1701 
6.00 
9.00 
2201 
5.00 
9.00 
2202 
2.00 
9.00 
2203 
2.00 
9.00 
2204 
2.00 
9.00 
2205 
1.00 
9.00 
2206 
2208 
5.00 
9.00 
0303  13.09  4.00 
2.00 
4.50 
0401 
2.00 
4.50 
0402 
2.00 
7.22 
0901 
2.00 
5.50 
0902 
2.33 
2.00 
1001 
5.57 
2.89 
1005 
5.50 
8.06 
1006 
2.00 
5.50 
1511 
4.88 
4.20 
1701 
6.00 
4.50 
2201 
5.00 
4.50 
2202 
2.00 
4.50 
2203 
2.00 
4.50 
2204 
2.00 
4.50 
2205 
1.00 
4.50 
2206 
5.00 
4.50 
2208 
0.00 
4.91 
0303 
0.00 
4.50 
0401 
0.00 
4.50 
0402 
0.00 
5.78 
0901 
0.00 
5.50 
0902 
1.17 
2.00 
1001 
1.00 
2.22 
1005 
0.00 
6.44 
1006 
0.00 
5.50 
1511 
1.63 
2.80 
1701 

5.00 
5.00 
7.00 

7.00 

5.00 
7.00 
6.00 
6.00 
6.00 
6.00 

5.00 
5.00 
7.00 

7.00 

5.00 
7.00 
4.50 
4.50 
4.50 
4.50 

0.00 
0.00 
0.00 

0.00 

CMR 
7.21 
5.00 
5.25 
2.00 
2.00 
3.33 
4.00 
2.80 
1.00 

2.00 
2.00 
2.00 
1.86 
2.00 
2.00 
2.00 
5.16 
3.33 
3.63 
2.00 
2.00 
3.33 
4.00 
2.80 
0.00 

1.00 
1.00 
1.00 
0.86 
1.00 
1.00 
1.00 
2.05 
1.67 
1.63 
0.00 
0.00 
0.00 
0.00 
0.00 
1.00 

CIV 
1.00 
2.00 
2.00 
3.00 
2.00 
1.50 
1.00 
2.00 
2.00 
2.00 
2.00 
2.60 
2.00 
2.00 
2.00 
2.00 
3.00 
1.00 
2.00 
2.00 
3.00 
2.00 
1.50 
1.00 
2.00 
2.00 
1.00 
2.00 
2.60 
2.00 
2.00 
2.00 
2.00 
3.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
1.00 

GIN 

ETH 

11.00 

9.00 
4.13 
5.17 

LBR 
CPV 
GMB 
GHA 
8.12 
12.00  12.00  13.07  23.00  40.07 
8.00 
13.00  13.00  12.00  17.00  10.00 
8.00 
13.00  13.00  12.00  17.00  10.00 
7.00 
13.00  10.20  14.43  13.00  10.00 
7.00 
14.00  13.00  10.00 
13.00 
1.40 
11.00 
5.00 
9.00 
1.67 
8.00 
11.00 
3.80 
8.28 
20.00  10.50  14.75  14.00  12.48 
7.00 
13.00  10.00  14.00  15.00  14.00 
18.00  10.00 
7.00 
2.00 
2.00 
13.00  11.00  12.00  14.00  20.10  14.50 
13.00  11.00  13.00  14.00  20.10  10.00 
14.00  10.00  13.00  16.00  16.00  12.00 
14.00  10.00  12.00  16.00  16.50  12.00 
14.00  10.00  12.00  16.00  16.50  12.00 
14.00  10.00  12.00  16.00  14.00  12.00 
14.00  10.00  12.00  16.00  16.61  11.00 
8.12 
12.00  12.00  11.20  12.78  34.72 
8.00 
10.00 
13.00  13.00  10.29 
8.00 
10.00 
13.00  13.00  10.29 
7.00 
10.00 
12.49 
9.90 
13.00 
7.00 
12.00 
9.00 
13.00 
10.00 
1.00 
5.00 
3.38 
9.00 
1.00 
8.00 
3.80 
4.17 
7.97 
20.00  10.03  12.64 
12.48 
7.00 
13.00  10.00  12.00 
14.00 
2.00 
2.00 
8.89 
18.00 
7.00 
16.29  12.89 
10.50 
9.78 
13.00 
16.29  10.00 
10.11 
9.78 
13.00 
12.80  10.80 
10.11 
8.89 
12.44 
13.20  10.80 
10.50 
8.89 
12.44 
13.20  10.80 
10.50 
8.89 
12.44 
14.00  10.80 
10.50 
8.89 
12.44 
9.78 
13.39 
10.50 
8.89 
12.44 
0.00 
5.34 
1.87 
0.00 
0.00 
0.00 
0.00 
1.71 
0.00 
0.00 
0.00 
0.00 
1.71 
0.00 
0.00 
0.00 
0.00 
1.95 
0.30 
0.00 
0.00 
2.00 
0.00 
0.00 
0.00 
0.40 
0.00 
0.75 
0.00 
0.67 
0.00 
1.00 
0.00 
0.31 
2.11 
0.47 
0.00 
0.00 
2.00 
0.00 
0.00 
0.00 
0.00 
1.11 
0.00 

8.50 
8.50 
6.50 
6.50 
4.71 
4.71 
7.00 
8.33 
4.71 
7.78 
7.78 
8.00 
8.00 
8.00 
8.00 
8.00 
10.22 
8.50 
8.50 
6.50 
6.50 
6.29 
6.29 
7.00 
6.67 
6.29 

0.00 
0.00 
0.00 

SEN 

3.00 

6.00 
5.00 

3.00 
2.00 

5.60 
4.00 

ZWE 
1.00 

MLI 
MUS  NER  NGA 
TGO 
29.16  4.13  10.08  5.00  8.00 
7.08 
11.00  19.00  4.00  13.00  4.00  4.00 
11.00  19.00  4.00  13.00  4.00  4.00 
16.80  5.00  10.00  2.50  4.00 
8.00 
18.00  5.00  10.00  3.00  4.00 
8.00 
1.00 
3.00 
8.00 
3.80 
6.50 
1.00 
19.00  6.20  10.00  4.25  4.00 
8.75 
6.55 
16.00  5.00  11.55  5.50  4.00 
5.00 
5.26 
15.60  3.00 
4.00 
2.00 
4.00  12.56 
20.00  2.00  17.00 
6.00 
2.33 
4.00 
24.00  2.00  17.00 
6.00 
5.00 
4.00 
23.00  3.00  13.00 
7.00 
4.60 
4.00 
25.75  3.00  17.00 
7.00 
5.00 
4.00 
24.00  3.00  17.00 
7.00 
2.00 
4.00 
34.00  3.00  18.00 
7.00 
6.17 
4.00 
25.14  3.00  19.00 
7.00 
6.97 
1.00 
5.00  5.33 
9.93 
29.16  4.13 
11.00  19.00  4.00  13.00  4.00  2.67 
11.00  19.00  4.00  13.00  4.00  2.67 
15.12  5.00  10.00  2.50  2.67 
6.00 
16.20  5.00  10.00  3.00  2.67 
6.00 
1.00 
1.78 
5.33 
2.40 
4.33 
1.00 
15.55  6.20  10.00  4.25  2.67 
6.56 
14.40  5.00  10.46  5.50  2.67 
3.33 
14.04  3.00 
2.67 
2.00 
20.00  2.00  14.57 
4.50 
21.82  2.00  14.57 
4.50 
23.00  1.50  11.14 
5.60 
23.60  1.50  12.75 
5.60 
22.00  1.50  12.75 
5.60 
31.17  1.50  13.50 
5.60 
23.05  1.50  14.25 
5.60 
0.15 
0.00 
0.00 
0.11 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
1.68 
2.00 
0.00 
0.00 
1.80 
2.00 
0.00 
0.00 
1.22 
2.67 
0.00 
0.00 
1.40 
2.17 
0.00 
0.00 
3.46 
2.19 
1.09 
0.00 
1.60 
1.67 
0.00 
0.00 
1.56 
1.33 

5.91 
4.42 
2.67  11.00 
2.33 
2.67 
5.00 
2.67 
4.60 
2.67 
5.00 
2.67 
2.00 
2.67 
6.17 
2.67 
0.00  2.67 
0.00 
0.00  1.33 
0.00  1.33 
0.00  1.33 
0.00  1.33 
0.00 
0.00 
0.00  1.33 
0.00  1.33 
1.00 

6.00 
5.00 

3.00 
2.00 

0.64 
0.84 

4.20 
3.00 

1.40 
1.00 

2.00 

35 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A 

NTM category  HS-4 
2201 
2202 
2203 
2204 
2205 
2206 
2208 
0303 
0401 
0402 
0901 
0902 
1001 
1005 
1006 
1511 
1701 
2201 
2202 
2203 
2204 
2205 
2206 
2208 
0303 
0401 
0402 
0901 
0902 
1001 
1005 
1006 
1511 
1701 
2201 
2202 
2203 
2204 
2205 
2206 
2208 

B 

BEN 
4.50 
4.50 
4.50 
4.50 
4.50 
4.50 
4.50 
9.82 
4.50 
4.50 
4.33 
4.13 
1.00 
1.11 
4.83 
4.13 
1.40 
4.50 
4.50 
4.50 
4.50 
4.50 
4.50 
4.50 
1.64 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 

4.00 

5.00 
5.00 
5.60 

0.00 
0.00 
1.50 
1.50 
1.50 
1.50 

BFA  BWA 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
4.00 
2.00 
2.00 
2.00 
2.00 
2.33 
3.71 
3.67 
2.00 
3.25 
4.00 
3.33 
2.00 
2.00 
2.00 
1.00 
3.33 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 

5.00 
7.00 
4.50 
4.50 
4.50 
4.50 

0.00 
0.00 
0.00 

2.00 

CIV 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
1.00 
1.00 
2.00 
1.00 
1.50 
1.00 
1.00 
1.00 
1.00 
1.00 
1.60 
1.00 
1.00 
1.00 
1.00 
1.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
1.00 

CMR 
1.00 
1.00 
1.00 
1.00 
1.00 
1.00 
1.00 
5.16 
3.33 
3.25 
2.00 
2.00 
3.33 
4.00 
2.80 
0.00 

0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.38 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 

1.00 
1.00 
1.00 
0.86 
1.00 
1.00 
1.00 

CPV 
0.00 
0.00 
1.56 
1.56 
1.56 
1.56 
1.56 
9.00 
9.75 
9.75 
9.75 
9.75 
6.43 
4.00 
12.00 
9.75 
10.80 
9.75 
9.75 
9.33 
9.33 
9.33 
9.33 
9.33 
3.00 
3.25 
3.25 
3.25 
3.25 
2.57 
4.00 
8.00 
3.25 
7.20 
3.25 
3.25 
3.11 
3.11 
3.11 
3.11 
3.11 

ETH 
1.22 
1.22 
1.11 
1.11 
1.11 
1.11 
1.11 
8.00 
7.43 
7.43 
5.00 
5.14 
0.00 
0.00 
5.47 
6.25 
4.44 
4.89 
4.89 
4.44 
4.44 
4.44 
4.44 
4.44 
4.00 
3.71 
3.71 
3.65 
2.57 
2.25 
3.00 
3.20 
2.50 
3.33 
3.67 
3.67 
3.33 
3.33 
3.33 
3.33 
3.33 

GHA 
1.50 
2.89 
2.89 
1.50 
1.50 
1.50 
1.50 
5.60 
5.14 
5.14 
5.84 
6.00 
3.33 
2.80 
6.32 
6.00 
2.00 
6.00 
5.78 
5.78 
6.00 
6.00 
6.00 
6.00 
5.60 
5.14 
5.14 
5.84 
6.00 
1.67 
1.00 
6.32 
6.00 
0.00 
4.50 
4.33 
4.33 
4.50 
4.50 
4.50 
4.50 

GIN 
6.22 
6.22 
8.00 
8.00 
8.00 
8.00 
8.00 
5.11 
4.25 
4.25 
3.25 
3.25 
1.57 
1.57 
3.50 
3.33 
1.57 
3.11 
3.11 
3.20 
3.20 
3.20 
3.20 
3.20 
2.56 
2.13 
2.13 
1.63 
1.63 
1.57 
1.57 
1.75 
1.67 
1.57 
1.56 
1.56 
1.60 
1.60 
1.60 
1.60 
1.60 

GMB 
3.81 
3.81 
3.20 
3.30 
3.30 
0.00 
3.22 
18.70 
7.14 
7.14 
7.14 
7.14 

8.84 
10.50 
2.00 
10.57 
10.57 
8.00 
8.25 
8.25 
8.75 
8.06 
16.03 
2.86 
2.86 
2.86 
2.86 

3.65 
3.50 
0.00 
5.71 
5.71 
4.80 
4.95 
4.95 
5.25 
5.33 

LBR 
1.61 
0.00 
1.20 
1.20 
1.20 
1.20 
1.22 
6.96 
6.86 
6.86 
5.83 
5.83 
1.00 
1.00 
6.83 
5.83 
5.83 
9.67 
7.50 
7.20 
7.20 
7.20 
7.20 
6.11 
1.16 
1.14 
1.14 
1.17 
1.17 
0.00 
0.00 
1.14 
1.17 
1.17 
3.22 
2.50 
2.40 
2.40 
2.40 
2.40 
2.44 

SEN 

0.00 

2.00 
2.00 

1.50 
1.50 

MUS  NER  NGA 
2.43 
0.00 
0.00 
2.43 
0.00 
2.18 
1.86 
1.50 
0.00 
4.25 
1.50 
2.15 
4.25 
1.50 
2.00 
4.50 
1.50 
2.83 
4.75 
1.50 
2.10 
9.93 
21.40  4.13 

TGO 
MLI 
1.33 
1.50 
1.33 
1.50 
1.33 
1.40 
1.33 
1.40 
1.33 
1.40 
1.33 
1.40 
1.33 
1.40 
6.97 
3.33  2.67 
11.00  14.25  4.00  10.83  4.00  0.00 
11.00  14.25  4.00  10.83  4.00  0.00 
10.08  2.50  10.00  2.00  0.00 
6.00 
10.80  2.50  10.00  2.00  0.00 
6.00 
0.00 
0.00 
5.33 
0.00 
4.33 
0.00 
10.36  2.30  10.00  2.83  0.00 
6.56 
2.50  10.46  3.30  0.00 
9.60 
3.33 
1.00 
1.50 
9.36 
2.67 
15.00  2.00  12.14 
4.50 
13.09  2.00  12.14 
4.50 
15.33  1.50 
4.20 
9.29 
12.88  1.50  10.63 
4.20 
12.00  1.50  10.63 
4.20 
17.00  1.50  11.25 
4.20 
12.57  1.50  11.88 
4.20 
0.00 
0.00 
7.76 
0.00 
2.17 
0.00 
4.75 
0.00 
2.17 
0.00 
4.75 
0.00 
0.00 
0.00 
5.04 
0.00 
0.00 
0.00 
5.40 
0.00 
0.00 
3.00 
1.78 
0.00 
0.00 
2.00 
2.40 
0.00 
0.00 
1.60 
5.18 
0.00 
0.00 
0.00 
4.80 
0.00 
0.00 
0.00 
4.68 
0.00 
2.43 
0.00 
5.00 
0.00 
2.43 
0.00 
8.73 
0.00 
1.86 
0.00 
5.11 
1.40 
2.13 
0.00 
8.58 
1.40 
2.13 
8.00 
1.40 
0.00 
2.25 
11.33  0.00 
1.40 
2.38 
0.00 
8.38 
1.40 

0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
1.67  2.67 
0.00  2.67 
0.00  2.67 
0.00  2.67 
0.00  2.67 
0.00 
0.00 
0.00  2.67 
0.00  2.67 
0.00 

2.67 
2.67 
2.67 
2.67 
2.67 
2.67 
2.67 

ZWE 
1.56 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
1.00 

1.40 
2.00 

2.36 
1.68 
6.22 
1.00 
0.00 
1.80 
1.67 
0.00 
2.13 
0.00 

2.80 
1.00 

3.55 
2.74 
4.78 
1.33 
5.00 
2.80 
3.33 
2.00 
4.04 

0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
Source: own calculations based on UNCTAD-TRAINS-ITC database. 

Notes: ALL includes all NTMs categories, except P type (NTMs affecting exports); TECH includes NTM categories A, B and C; and NON-TECH includes NTM categories E, F, G, H, and I.  

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A5. Number of NTMs (mean) by GTAP sector code, country and NTM category 

NTM category  GTAP code 

B_T 
CMT 
CTL 
C_B 
FSH 
GRO 
MIL 
OAP 
OCR 
OFD 
OMT 
OSD 
PCR 
PDR 
PFB 
SGR 
VOL 
V_F 
WHT 
WOL 
B_T 
CMT 
CTL 
C_B 
FSH 
GRO 
MIL 
OAP 
OCR 
OFD 
OMT 
OSD 
PCR 
PDR 
PFB 
SGR 
VOL 
V_F 
WHT 
WOL 
B_T 
CMT 
CTL 
C_B 

ALL 

TECH 

NON-TECH 

4.00 

6.35 
7.00 

2.00 
4.38 
4.00 

2.92 
7.00 
5.09 
4.33 
6.37 
5.06 
4.50 

BFA  BWA 
BEN 
5.50 
3.69 
7.88 
11.51  1.98 
4.00 
1.00 
2.00 
7.25 
5.57 
14.93  3.57 
5.84 
4.19 
2.21 
9.13 
1.10 
3.95 
9.36 
4.51 
12.74  4.61 
10.76  3.33 
8.99 
5.15 
14.00  5.67 
16.00  5.00 
4.43 
5.02 
5.52 
6.67 
9.65 
2.13 
19.87  4.12 
4.00 
3.50 
3.12 
3.12 
4.12 
1.98 
6.86 
1.00 
1.00 
3.63 
4.71 
10.45  3.57 
5.61 
2.39 
2.21 
4.56 
1.10 
2.01 
4.19 
5.30 
4.61 
7.90 
3.33 
6.25 
5.15 
4.57 
5.67 
7.78 
5.00 
8.89 
4.43 
1.51 
4.96 
3.96 
5.11 
2.13 
13.98  4.08 
2.00 
2.33 
0.56 
3.76 
4.65 
1.00 
3.63 

2.92 
7.00 
5.09 
3.75 
6.37 
5.06 
4.50 

0.57 
0.00 
0.00 
0.86 

2.00 
1.13 
0.00 

6.35 
7.00 

4.00 

CIV 
2.31 
2.00 
1.00 
1.67 
1.20 
1.55 
2.00 
1.16 
1.73 
1.91 
2.00 
2.04 
2.00 
2.00 
1.00 
1.42 
1.89 
2.01 
1.50 
1.00 
2.31 
2.00 
1.00 
1.67 
1.20 
1.55 
2.00 
1.16 
1.73 
1.90 
2.00 
2.04 
2.00 
2.00 
1.00 
1.00 
1.89 
2.01 
1.50 
1.00 
0.00 
0.00 
0.00 
0.00 

CMR 
1.88 
9.00 
2.11 
2.00 
7.12 
2.73 
5.00 
4.17 
2.53 
5.90 
8.54 
2.70 
2.00 
4.00 

2.00 
4.00 
2.04 
3.33 
1.00 
1.00 
4.50 
0.56 
2.00 
5.07 
2.73 
3.35 
2.09 
2.08 
4.11 
5.23 
2.70 
2.00 
4.00 

2.00 
2.79 
2.04 
3.33 
0.00 
0.88 
4.50 
1.56 
0.00 

GIN 

5.55 

9.00 

GMB 

11.00 

1.58 
9.72 

3.71 
7.84 

6.87 
8.07 

ETH 
9.18 

10.39 
11.08 

GHA 
LBR 
10.58  14.59  16.56  11.31 
8.93 
5.59 
6.54 
8.27 
1.80 
8.17 
5.37 
7.33 
8.27 
8.54 
7.43 
8.00 
9.00 

CPV 
12.09 
13.96  12.05  12.96  17.35  10.00 
3.75 
3.00 
2.00 
14.98 
11.89 
11.30  12.07  10.00 
11.97  11.79  13.66  22.01  41.67 
4.79 
4.36 
13.00  12.82  12.05  16.81  10.22 
7.17 
6.63 
13.45  15.97 
11.04 
11.64  12.49  10.00 
12.74  10.00  13.22  15.56  27.07 
13.67  11.56  13.93  16.13  12.43 
11.97  12.51  14.80  12.33  10.42 
20.00 
14.00  14.00  13.21 
20.00  14.00  17.00  14.00  10.00 
3.00 
2.00 
7.68 
2.00 
2.00 
17.80 
7.03 
12.44  11.26  12.65  13.90  18.06 
6.98 
13.91  13.53  10.00 
14.72 
9.35 
1.40 
11.00 
5.00 
9.00 
4.13 
1.00 
10.52 
3.00 
1.00 
4.06 
13.33  10.21 
7.31 
8.83 
7.98 
10.67 
8.93 
10.00 
8.67 
13.96  12.05  11.11 
4.54 
2.00 
7.68 
2.75 
2.88 
3.00 
6.46 
11.89 
10.00 
5.67 
9.90 
7.53 
8.27 
11.97  11.79  10.75  12.06  36.12 
1.00 
4.79 
4.46 
4.36 
8.10 
13.00  12.82  10.35 
5.09 
5.81 
6.52 
6.63 
6.95 
11.04 
10.07 
7.72 
8.24 
12.74  10.00  11.29 
8.54 
13.67  11.56  12.08 
6.44 
11.97  10.32  12.80 
8.00 
20.00 
12.00 
9.00 
20.00  12.44  14.57 
7.88 
3.00 
2.00 
1.58 
2.00 
8.74 
17.80 
12.44  11.26  10.87 
11.93 
9.14 
14.72 
5.00 
3.38 
9.00 
0.00 
4.06 
3.00 
1.75 
1.20 
1.42 
1.85 
0.00 
0.00 
1.00 
0.82 
0.00 
1.40 
0.32 
0.00 

4.71 
8.41 
6.37 
5.90 
8.07 
8.09 
5.94 
7.00 
7.00 
3.96 
4.81 
7.42 
6.75 
4.71 
4.04 
7.28 
8.67 
7.30 
6.40 

10.22 
14.41 
10.00 
24.10 
12.18 
10.42 
13.21 
10.00 

7.68 
7.03 
6.98 
1.00 
1.00 
1.10 
0.00 
1.05 
0.08 

3.23 
0.00 
0.00 
0.00 

2.00 
17.11 
10.00 

37 

TGO  ZWE 
5.93 
1.75 
2.87 

SEN 

1.32 
2.85 

MUS  NER  NGA 
21.57  3.53  16.07  1.75  3.78 
14.92  4.00  11.05  4.67  7.91 
8.78  2.00 
1.73 
2.63 
1.00 
2.00  4.00 
8.50 
13.70  5.67 
4.81  7.70 
9.85 
27.27  4.12 
2.86 
1.00 
5.84 
4.25 

2.00 
3.00 
1.00 
1.42 
14.85  3.00 
14.65  4.39  10.32  4.28  4.00 
3.74  4.00 
9.96 
17.06  5.05 
3.00 
1.00 
6.00 
3.00 
1.00 
1.59 
1.00 
19.75  2.28  12.39  1.00  2.74 
4.67  5.27 
9.22 
14.92  4.00 
8.78  2.00 
1.00 
1.81 
1.00 
2.00  2.67 
8.50 
12.17  5.67 
4.81  5.13 
9.63 
27.27  4.12 
2.86 
1.00 
5.84 
2.75 

MLI 
6.43 
5.00 
2.67 
8.00 
7.03 
7.70 
11.06  20.91  3.91  12.95  3.37  4.00 
6.80  3.20 
3.34 
4.57 
10.43  1.89 
13.30  5.00 
7.29 
3.02  4.35 
8.79 
20.94  3.97  11.39  3.47  5.39 
6.77 
15.24  4.41  11.44  5.09  6.95 
5.75 
10.00  17.01  7.24 
2.36  4.00 
9.27 
8.00 
18.00  5.00  10.00  4.00  4.00 
11.00  22.00  8.00  10.00  5.00  4.00 
2.78 
3.97 
5.06 
8.00 
8.00 
1.41 
5.28 
5.00 
2.13 
5.71 
6.87 
5.13 
10.96  20.91  3.91  12.95  3.37  2.67 
6.80  2.56 
2.80 
3.29 
9.83 
1.71 
11.70  5.00 
5.20 
3.02  2.74 
8.54 
20.41  3.97  11.13  3.47  3.59 
5.53 
5.09  4.63 
9.75 
15.24  4.41 
4.83 
15.14  7.24 
7.33 
2.36  2.67 
9.27 
14.73  5.00  10.00  4.00  2.67 
6.00 
18.00  8.00  10.00  5.00  2.67 
8.25 
1.00 
1.00 
1.78 
3.00 
1.42 
14.24  3.00 
3.03 
9.72 
13.47  4.39 
3.49 
9.96 
15.35  5.05 
5.99 
1.00 
6.00 
5.33 
1.78 
1.00 
1.00 
0.69 
3.68 
1.25 
1.15 
1.83 
0.00 
0.00 
0.73 
0.00 
0.53 
0.00 
0.00 
2.29 

1.32 
1.92 
4.22  2.67 
3.74  2.67 
3.00 
1.00 
0.75  1.04 
0.00  2.64 
0.00  0.00 
0.00  1.33 

1.83 
0.00 
0.81 
1.52 

5.72 
2.00 
1.33 
4.08 
3.70 
7.91 
6.72 
2.00 

2.00 
4.21 
4.85 
1.77 

5.70 
1.69 
2.53 

5.71 
1.00 
0.17 
3.58 
2.49 
7.14 
5.64 
2.00 

1.00 
3.23 
4.56 
0.52 

0.23 
0.06 
0.33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
NTM category  GTAP code 

FSH 
GRO 
MIL 
OAP 
OCR 
OFD 
OMT 
OSD 
PCR 
PDR 
PFB 
SGR 
VOL 
V_F 
WHT 
WOL 
B_T 
CMT 
CTL 
C_B 
FSH 
GRO 
MIL 
OAP 
OCR 
OFD 
OMT 
OSD 
PCR 
PDR 
PFB 
SGR 
VOL 
V_F 
WHT 
WOL 
B_T 
CMT 
CTL 
C_B 
FSH 
GRO 
MIL 
OAP 
OCR 

A 

B 

BEN 
4.48 
1.81 
4.56 
1.94 
4.06 
4.85 
4.52 
4.42 
6.22 
7.11 
3.51 
2.71 
4.54 
5.89 
2.00 
2.56 
3.45 
5.36 
1.00 
3.63 
7.46 
1.10 
4.48 
1.94 
3.45 
6.15 
5.04 
3.94 
4.67 
5.33 
0.51 
1.36 
4.35 
5.98 
1.00 
0.56 
0.27 
0.00 
0.00 
0.00 
1.49 
0.00 
0.00 
0.00 
0.66 

BFA  BWA 
0.00 
0.00 
0.00 
0.23 
0.00 
0.00 
0.58 
0.00 
0.00 
0.32 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.57 
0.00 
0.04 
1.17 

0.00 
0.00 

0.00 

0.00 
4.38 
4.00 

2.92 
4.00 
5.09 
2.58 
4.84 
4.55 
4.50 

3.00 

5.15 
5.60 

2.00 
0.00 
0.00 

0.00 
2.00 
0.00 
1.17 
0.24 

2.27 
1.98 
1.00 
3.14 
3.57 
3.74 
2.09 
1.10 
2.93 
3.44 
2.68 
3.44 
3.78 
3.33 
2.13 
3.30 
2.00 
2.72 
2.33 

0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 

CIV 
0.00 
0.00 
0.00 
0.00 
0.00 
0.01 
0.00 
0.00 
0.00 
0.00 
0.00 
0.42 
0.00 
0.00 
0.00 
0.00 
1.07 
1.00 
1.00 
1.00 
0.22 
1.55 
1.00 
0.98 
1.12 
0.96 
1.00 
1.17 
1.00 
1.00 
1.00 
1.00 
1.03 
1.01 
1.50 
1.00 
0.45 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 

CMR 
2.05 
0.00 
1.65 
2.09 
0.44 
1.79 
3.31 
0.00 
0.00 
0.00 

0.00 
1.21 
0.00 
0.00 
1.00 
0.08 
4.50 
0.56 
2.00 
5.07 
2.73 
3.26 
1.65 
2.07 
4.06 
5.23 
2.70 
2.00 
4.00 

2.00 
2.79 
2.04 
3.33 
0.00 
0.92 
0.00 
0.00 
0.00 
0.00 
0.00 
0.09 
0.44 
0.01 

CPV 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
7.81 
10.47 
3.00 
9.00 
8.98 
3.27 
9.75 
5.43 
8.35 
9.54 
10.26 
9.05 
12.00 
12.00 
3.00 
10.70 
9.39 
10.26 
6.43 
3.00 
2.87 
3.49 
0.00 
2.89 
2.99 
1.09 
3.25 
1.20 
2.57 

ETH 
0.00 
1.09 
0.00 
0.35 
0.35 
0.00 
0.00 
2.19 
0.00 
1.56 
0.00 
0.99 
0.00 
0.20 
0.75 
0.00 
3.77 
7.97 
2.77 
3.58 
7.87 
0.00 
7.33 
3.51 
3.79 
6.22 
7.71 
4.77 
5.14 
6.22 
0.00 
4.42 
7.00 
6.00 
0.00 
2.29 
3.09 
3.99 
0.00 
2.74 
3.90 
3.27 
3.66 
1.97 
2.68 

GHA 
2.91 
0.00 
1.71 
1.36 
1.57 
1.93 
1.85 
2.00 
2.00 
2.43 
0.00 
0.00 
1.78 
1.98 
0.00 
1.00 
5.09 
5.55 
1.38 
5.20 
5.38 
3.24 
5.22 
2.82 
5.27 
6.02 
5.98 
6.53 
6.00 
7.29 
2.00 
2.00 
5.52 
5.97 
3.33 
0.00 
3.74 
5.55 
0.00 
4.70 
5.37 
1.55 
5.13 
2.17 
4.74 

38 

GIN 
9.94 
6.29 
8.41 
7.08 
6.59 
7.49 
8.04 
6.39 
7.00 
7.00 
6.42 
6.26 
6.48 
6.78 
6.29 
6.48 
2.71 
4.34 
2.40 
2.47 
4.75 
1.57 
4.20 
2.12 
2.58 
3.59 
4.02 
2.75 
3.50 
3.50 
0.75 
1.68 
2.91 
3.36 
1.57 
0.81 
1.56 
2.17 
1.83 
1.60 
2.49 
1.57 
2.10 
1.77 
1.65 

GMB 
5.56 

0.00 
1.56 
0.00 
2.97 
0.25 
0.00 
0.00 
0.00 

0.00 
0.96 
0.00 

8.30 
7.14 
2.00 
7.14 
19.45 

7.23 
8.64 
7.14 
13.67 
8.11 
7.35 
9.33 
7.14 

2.00 
11.47 
7.14 

5.03 
2.86 
0.00 
2.86 
16.67 

2.99 
5.76 
2.86 

LBR 
0.00 
0.80 
0.07 
0.28 
0.38 
0.03 
0.00 
0.99 
0.00 
1.13 

0.00 
0.00 
0.00 
0.40 
0.00 
6.82 
7.65 
3.41 
5.39 
6.93 
1.00 
6.89 
4.03 
5.36 
6.52 
7.04 
5.37 
6.86 
6.75 

6.45 
5.86 
5.82 
1.00 
1.00 
2.41 
1.28 
0.00 
1.08 
1.17 
0.00 
1.22 
0.37 
1.49 

TGO  ZWE 
0.01 
1.00 
1.17 
0.50 
1.21 
0.77 
1.09 
0.00 

SEN 
0.00  2.57 
0.00 
0.00  1.33 
0.00  0.64 
0.00  1.61 
0.00  1.80 
0.00  2.32 
0.00  1.33 
0.00  1.33 
0.00  1.33 
0.00 
0.92 
0.07  1.33 
0.00  1.33 
0.00 
0.00 

MUS  NER  NGA 
0.21 
0.00 
0.00 
0.00 
0.00 
1.50 
0.00 
0.00 
0.00 
1.28 
0.18 
0.59 
0.26 
0.00 
1.60 
0.26 
0.00 
0.53 
1.69 
0.00 
0.00 
0.00 
0.00 
1.87 
0.00 
0.00 
3.27 
0.00 
0.00 
4.00 
0.00 
0.00 
1.00 
0.00 
0.00 
0.61 
0.60 
0.00 
1.18 
0.00 
0.00 
1.71 
0.00 
0.00 
1.22 
0.59 
0.00 
11.10  1.08  10.25  0.25  0.00 
4.67  2.61 
9.14 
11.19  4.00 
7.32  2.00 
0.00 
1.00 
1.81 
2.00  0.00 
8.33 
7.83 
1.83 
3.21  2.47 
9.60 
20.15  4.12 
2.00 
0.00 
1.50 
0.00 

MLI 
0.16 
2.57 
0.09 
0.54 
2.10 
1.24 
0.92 
2.67 
2.00 
2.75 
1.00 
0.94 
1.58 
2.01 
2.67 
0.72 
3.50 
5.00 
2.13 
5.71 
6.87 
5.13 
10.96  15.68  3.91  10.79  3.37  0.00 
1.71 
7.55 
5.51  1.60 
2.80 
7.35 
1.88 
2.64  0.00 
5.10 
14.83  2.76 
2.53  0.92 
5.53 
11.43  4.41 
4.43  1.97 
4.83 
2.14  0.00 
1.86 
9.77 
7.33 
9.82 
6.00 
2.50  10.00  2.67  0.00 
12.00  2.00  10.00  3.33  0.00 
8.25 
0.00 
0.00 
1.78 
1.50 
0.42 
10.23  1.50 
3.03 
9.66 
2.37 
9.21 
3.49 
9.95 
10.22  2.45 
5.99 
0.00 
1.50 
5.33 
0.00 
0.00 
1.00 
0.69 
2.05 
1.00 
1.78 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
2.00 
0.00 
0.00 
0.00 
0.00 
0.00 
2.84 
0.00 
2.16 
0.00 
0.00 
0.00 
0.00 
0.00 
0.32 
1.24 
0.10 

0.32 
0.08 
2.94  0.00 
3.59  0.00 
2.00 
0.00 
0.00  2.74 
0.00  2.67 
0.00  0.00 
0.00  2.67 
1.60  2.67 
0.00 
0.00  2.67 
0.05  0.96 
0.03  2.74 

7.14 
3.73 
0.00 
4.35 
7.12 
2.75 
5.23 
2.28 
4.35 

2.57 
8.00 
9.79 
9.26 
9.18 

1.00 
0.98 
0.29 
1.24 

2.27 
0.63 
1.73 

4.34 
1.00 
0.00 
2.05 
0.34 
4.51 
2.21 
1.00 

1.00 
0.89 
1.78 
0.52 

3.43 
1.06 
0.80 

1.38 
0.00 
0.17 
1.10 
1.49 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
NTM category  GTAP code 

OFD 
OMT 
OSD 
PCR 
PDR 
PFB 
SGR 
VOL 
V_F 
WHT 
WOL 

BEN 
0.71 
0.05 
0.00 
0.00 
0.00 
0.00 
0.00 
0.19 
4.53 
0.00 
0.00 

BFA  BWA 
0.02 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
1.23 
0.00 
0.00 
0.00 
0.00 

0.00 
0.00 

0.00 

0.00 

CIV 
0.01 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 

CMR 
0.04 
0.00 
0.00 
0.00 
0.00 

0.00 
0.00 
0.00 
0.00 
0.00 

CPV 
3.19 
3.42 
2.91 
8.00 
8.00 
0.00 
7.11 
3.06 
3.42 
2.57 
0.00 

ETH 
3.12 
3.86 
4.14 
2.57 
4.67 
0.58 
3.20 
2.81 
3.11 
2.25 
0.50 

GHA 
5.27 
5.54 
6.27 
6.00 
7.29 
0.00 
0.00 
5.35 
5.96 
1.67 
0.00 

GIN 
1.87 
2.01 
1.59 
1.75 
1.75 
1.61 
1.57 
1.62 
1.70 
1.57 
1.62 

GMB 
10.43 
4.07 
3.06 
3.88 
2.86 

0.00 
5.63 
2.86 

LBR 
1.72 
1.50 
1.07 
1.14 
1.13 

1.23 
1.17 
1.16 
0.00 
0.00 

MLI 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 
0.00 

MUS  NER  NGA 
1.27 
0.00 
5.58 
0.50 
0.00 
3.81 
0.00 
3.52 
5.37 
0.00 
0.00 
4.91 
0.00 
4.00 
6.00 
0.00 
0.00 
1.00 
0.00 
0.00 
4.02 
0.00 
0.00 
4.26 
0.00 
0.14 
5.13 
0.00 
3.00 
1.78 
0.00 
0.00 

SEN 
0.86  2.67 
0.00  2.67 
0.00  2.67 
0.00  2.67 
0.00  2.67 
0.00 
0.00 
0.00  2.67 
0.00  2.67 
0.00 
0.00 

TGO  ZWE 
2.58 
3.43 
1.00 

0.00 
2.35 
2.78 
0.00 

Source: own calculations based on UNCTAD-TRAINS-ITC database. 

Notes: ALL includes all NTMs categories, except P type (NTMs affecting exports); TECH includes NTM categories A, B and C; and NON-TECH includes NTM categories E, F, G, H, and I.  

39 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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doi:10.2760/11470 

ISBN 978-92-76-38678-0