Murky trade waters: Regional tariff commitments and non-tariff measures in Africa

Stender, Frederik; Vogel, Tim Working Paper Murky trade waters: Regional tariff commitments and non-tariff measures in Africa Discussion Paper, No. 13/2021 Provided in Cooperation with: German Institute of Development and Sustainability (IDOS), Bonn Suggested Citation: Stender, Frederik; Vogel, Tim (2021) : Murky trade waters: Regional tariff commitments and non-tariff measures in Africa, Discussion Paper, No. 13/2021, ISBN 978-3-96021-150-1, Deutsches Institut für Entwicklungspolitik (DIE), Bonn, https://doi.org/10.23661/dp13.2021 This Version is available at: https://hdl.handle.net/10419/234180 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Discussion Paper 13/2021 Murky Trade Waters Regional Tariff Commitments and Non-Tariff Measures in Africa Frederik Stender Tim Vogel Murky trade waters Regional tariff commitments and non-tariff measures in Africa Frederik Stender Tim Vogel Bonn 2021 • • • • • • • ····· ··•·· ··•··•·· ••••••• ··••· ·· :::· · JRF MEMBER Joh a nn es •RauOF' Forsc hung sg em ei nscha ft Discussion Paper / Deutsches Institut für Entwicklungspolitik ISSN (Print) 1860-0441 ISSN (Online) 2512-8698 Except as otherwise noted this publication is licensed under Creative Commons Attribution (CC BY 4.0). You are free to copy, communicate and adapt this work, as long as you attribute the German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) and the author. Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografie; detaillierte bibliografische Daten sind im Internet über http://dnb.d-nb.de abrufbar. The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data is available on the Internet at http://dnb.d-nb.de. ISBN 978-3-96021-150-1 (printed edition) DOI:10.23661/dp13.2021 Printed on eco-friendly, certified paper Dr Frederik Stender is a Researcher in the “Transformation of Economic and Social Systems” research programme at the German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE). Email: [email protected] Tim Vogel is a Research and Teaching Assistant at Ruhr-University Bochum, Faculty of Management and Economics, Chair of International Economics. Email: [email protected] Published with financial support from the Federal Ministry for Economic Cooperation and Development (BMZ). © Deutsches Institut für Entwicklungspolitik gGmbH Tulpenfeld 6, 53113 Bonn +49 (0)228 94927-0 +49 (0)228 94927-130 Email: [email protected] http://www.die-gdi.de Abstract In several African regions, economic integration has successfully reduced tariff protection by freezing the opportunity to raise applied tariffs against fellow integration partners above those promised. In this paper, we examine whether the regional tariff commitments on the continent have come at the expense of adverse side-effects on the prevalence of other – nontariff – trade barriers. More specifically, regional tariff commitments have not only amplified applied tariff overhangs – the difference between Most Favoured Nation (MFN) bound tariffs and effectively applied tariffs – for African members of the World Trade Organization (WTO), but have also sharply reduced their tariff policy space within Africa, thus leaving regulatory policies such as sanitary and phytosanitary (SPS) measures and technical barriers to trade (TBT) as two of the few legitimate options to level the playing field with market competitors. Comparing the effects of applied tariff overhangs towards all vis-à-vis African trading partners on SPS and TBT notifications of 35 African WTO members between 2001 and 2017, we find no overall relationship between tariff overhangs and import regulation in our preferred model setting. By contrast, larger tariff overhangs specific to intra-African trade relations have a significant share in increasing the probability of SPS measures and TBT. Our findings have important implications for future Pan-African integration under the recently launched African Continental Free Trade Area (AfCFTA) in that success in fostering continental economic integration does not exclusively depend on the realisation of tariff liberalisation, but at the same time on a mindful coordination with non-tariff provisions. Keywords: Economic integration, import regulation, non-tariff measures, tariff liberalisation, tariff overhang, trade policy substitution Acknowledgements We gratefully acknowledge valuable comments and suggestions from Clara Brandi, Matthias Busse, Niels Keijzer, David J. Kuenzel, Sabine Laudage, Gideon Ndubuisi, Magdalene Silberberger, Kasper Vrolijk, and Maurizio Zanardi. The views expressed in this paper are our own. Any remaining errors are the authors’ responsibility. Contents Abstract Acknowledgements Abbreviations 1 Introduction 1 2 A descriptive view and hypotheses 5 3 Empirical strategy 8 3.1 Estimation model 8 3.2 Data 10 4 Results and discussion 12 4.1 Baseline results 12 4.2 Extensions 16 5 Conclusions 20 References 23 Appendix 27 Table A1: Country sample 27 Table A2: Variable definition and sources 27 Table A3: Summary statistics 28 Figures Figure 1: Regional tariffs, SPS and TBT notifications of African WTO members, 2000-2017 6 Figure 2: Applied tariff overhangs of African WTO members, 2000-2017 7 Tables Table 1: Baseline logit model results (average marginal effects) 13 Table 2: HS2 logit model results (average marginal effects) for alternative dependent variables 15 Table 3: HS2 logit model results (average marginal effects) for alternative tariff overhang measure 17 Abbreviations AD anti-dumping AfCFTA African Continental Free Trade Area AU African Union CEN-SAD Community of Sahel-Saharan States COMESA Common Market for Eastern and Southern Africa EAC East African Community ECOWAS Economic Community of West African States EPA Economic Partnership Agreement EU European Union HIC high-income country HS Harmonised System IGAD Intergovernmental Authority on Development I-TIP Integrated Trade Intelligence Portal LDC least developed country LMIC lower middle-income country MFN Most Favoured Nation NTM non-tariff measure REC Regional Economic Community RTA regional trade agreement SADC Southern African Development Community SPS sanitary and phytosanitary STC specific trade concern TBT technical barriers to trade UMIC upper middle-income country WTO World Trade Organization 1 Murky trade waters: regional tariff commitments and non-tariff measures in Africa Introduction Over the past decades, the widening of the multilateral trading system and the proliferation of regional trade agreements (RTAs) have facilitated a considerable decline in tariffs. This holds true for all world regions, including the one still revealing the highest degree both of tariff protection and of isolation from world markets, namely Africa (Bouët, Cosnard, & Laborde, 2017; UNCTAD [United Nations Conference on Trade and Development], 2019, 2020). Today, not only are nearly all economies on the continent signatories to the World Trade Organization (WTO) but the average African country also has formal trade ties with another 25 neighbours. The latter results from the existence of partly overlapping Regional Economic Communities (RECs) – a situation which is further intensified by the recent official launch of the African Continental Free Trade Area (AfCFTA). Both club memberships notoriously freeze the opportunity to raise applied tariffs against fellow members above those promised. In this paper, we examine whether the regional tariff commitments within Africa have come at the expense of adverse side-effects on the prevalence of other – non-tariff – trade barriers. Our suspicion is led by the common notion that economic integration is seldom utterly harmonious. Conversely, while falling tariffs among regional partners render mutual trade less costly, they may also uncover rivalry as well as lock-in comparative and industrial location advantages, particularly in South-South integration schemes, thereby producing both winners and losers alike (Puga & Venables, 1997; Venables, 2003). Indeed, as trade liberalisation within Africa has concentrated regional trade surpluses on the side of economic powerhouses, such as Côte d’Ivoire, Egypt, Nigeria, and South Africa (UNCTAD, 2018a), it has equally put stress on export expansion in terms trade volumes, economic diversification and, with this, aspirations towards domestic industrialisation in the periphery. For example, Behuria (2019) notes that while Rwanda’s membership in the East African Community (EAC) and the Common Market for Eastern and Southern Africa (COMESA) has provided access to a larger integrated market, it has likewise accentuated competitive disadvantage with often better-financed and more advanced firms abroad, and thus hindered emancipation from existing comparative advantage. But it is not only countries threatened by marginalisation that preach commitment and practice restraint: even for South Africa, the all-overshadowing member of the Southern African Development Community (SADC), Nel and Taylor (2013) observe a steady preference for protection from (too much) intra-bloc competition. These experiences resonate with the widespread contradiction of national economic interests and regional commitments on the continent, which has fueled trade disputes among regional partners and raised concerns about the prospects of greater Pan-African integration (see, for instance, Byiers, Karaki, & Woolfrey, 2018; Himbara, 2020). A second motivation for our research question stems from the generally rather mixed track record of the trade effects of existing intra-African integration agreements. Despite partly substantial tariff liberalisation efforts, only a few studies attest to the creation of widespread trade (for example, Admassu, 2020; Carrère, 2004; Coulibaly, 2009) while others find no, little, or only REC-, member-or even sector-specific effects (see, for instance, Longo & Sekkat, 2004; Mayda & Steinberg, 2009; Musila, 2005; Riedel & Slany, 2019; Yang & Gupta, 2005). What is more, neither country coverage nor their lifetimes have been able to prevent intra-regional trade and the establishment of regional value chains in Africa from German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 1 Frederik Stender / Tim Vogel policy space for WTO countries to protect their domestic industries without resorting to NTMs (Kuenzel, 2020a). Intuitively, greater flexibility in tariff policy space translates into smaller demand for import market protection through NTMs. Our first hypothesis is thus the following: H1: A larger applied tariff overhang generally entails more tariff policy space for African WTO members, which reduces the demand for resorting to NTMs. The paramenters of trade within Africa, however, are more complex. Due to the widespread and often overlapping membership in the RECs, intra-African applied tariffs are on average not only at a lower level than Africa’s tariffs towards third countries. Even more significantly, intra-African tariff policy is in fact widely bound by the complex and naturally deeper commitments in the RECs rather than by WTO commitments. For many African WTO members, the RECs thus add an additional regional layer to the tariff commitments already made at multilateral levels (Sandrey, Karaan, & Vink, 2008). That being said, intraAfrican applied tariffs are largely locked-in by regional tariff commitments in the RECs. Hence, although African WTO members reveal large intra-African applied tariff overhangs, this does not generally come with greater tariff policy space towards neighbouring countries. Instead, a larger regional overhang could be interpreted as the discrepancy of a government’s actual (multilateral) tariff policy preference. This circumstance is coupled with the fact that intra-African trade is fairly different to the continent’s exports to third countries. While African countries supply world markets primarily with commodities and raw materials, intra-African trade is characterised by a comparatively large share of industrial goods (see, for example, Abrego et al. 2019; Slany & Riedel, 2019; UNECA [United Nations Economic Commission for Africa], 2015). The larger value-added in industrial trade means that there is more at stake, potentially bringing in a more competitive behavior. By implication, we hypothesise that there is a structurally different effect of intra-African applied tariff overhangs on NTMs compared to the case towards third countries. Our second hypothesis thus reads as: H2: A larger intra-African applied tariff overhang is the result of regional tariff commitments and represents a discrepancy with multilateral tariff preferences, which fosters stronger demand for NTMs to shield domestic markets. 3 Empirical strategy 3.1 Estimation model Taking into account the regional differences in applied tariff overhangs for African WTO members, we specify the incidence of SPS measures and TBT as a function of applied tariff overhangs towards all, that is, African and non-African, vis-à-vis African trading partners. Our baseline model thus reads as follows: 𝑗𝑗 𝑃𝑃𝑃𝑃(𝑁𝑁𝑁𝑁𝑁𝑁𝑖𝑖𝑖𝑖𝑖𝑖 = 1 | 𝑥𝑥𝑖𝑖𝑖𝑖𝑖𝑖 ) = 𝛽𝛽 ∙ (1 + 𝑂𝑂𝑂𝑂𝑂𝑂𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖,𝑖𝑖−1) (1) + 𝛾𝛾 ∙ 𝜲𝜲𝑖𝑖𝑖𝑖,𝑖𝑖−1 + 𝛿𝛿𝑖𝑖 + 𝜈𝜈𝑖𝑖𝑖𝑖 + 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 8 Murky trade waters: regional tariff commitments and non-tariff measures in Africa where 𝑁𝑁𝑁𝑁𝑁𝑁 is a binary variable for the composite occurrence of either an SPS or TBT notification to the WTO of imposing country 𝑖𝑖 in product 𝑘𝑘 (as a benchmark at the two-digit level) at year 𝑡𝑡, zero otherwise, and 𝜀𝜀𝑖𝑖𝑖𝑖𝑖𝑖 is the error term. We also test our model specification using both policy measures as separate dependent variables in a later extension. Our key explanatory variable is the applied tariff overhang which varies by imposing country, product category and time, but we also condition the measure with respect to the regional affiliation of trading partners. Formally, this is expressed as: 𝑗𝑗 𝑗𝑗 (2) 𝑂𝑂𝑂𝑂𝑂𝑂𝑃𝑃ℎ𝑎𝑎𝑎𝑎𝑎𝑎𝑖𝑖𝑖𝑖,𝑖𝑖−1 = 𝑁𝑁𝑀𝑀𝑁𝑁 𝑏𝑏𝑏𝑏𝑏𝑏𝑎𝑎𝑏𝑏𝑖𝑖𝑖𝑖,𝑖𝑖−1 − 𝐴𝐴𝐴𝐴𝐴𝐴𝑖𝑖𝑖𝑖,𝑖𝑖−1 where superscript 𝑗𝑗 indexes either overall (towards all trading partners) applied tariff overhangs or those specifically towards African trading partners. Applied tariff overhangs are the difference between ad-valorem product-specific MFN bound tariffs and effectively applied tariffs, denoted as 𝐴𝐴𝐴𝐴𝐴𝐴. For applied tariffs we use the trade-weighted average tariffs imposed on all and African trading partners, respectively. In technical terms, the latter classification thus operates as a restricted deviation from the overall effect. Although intraAfrican trade is generally low, given the often heterogeneous import relations of African countries, note that the overall effect occasionally includes a considerable portion of the regional effect. While MFN bound tariffs are multilateral commitments by definition, the regional variation in applied tariff overhangs entirely stems from the subtrahend of equation (2). Applied tariffs can reveal significant differences across trading partners. For WTO members, applied tariffs are shaped not only by non-discriminatory MFN applied tariffs, but to an even larger extent by the commitments and implementation progress in bilateral or regional trade agreements. According to the hypotheses formulated above, we expect a significant negative coefficient for the overall tariff overhang (H1). This would imply that larger tariff policy space generally, that is, as an average over all trading partners, led to fewer SPS and TBT initialisations. For H2 to hold, we expect a significant positive relationship between the incidence of SPS measures and TBT and tariff overhangs towards African trading partners. This is because H2 posits the discrepancy of regional tariff commitments and actual tariff preferences rather than tariff policy space. As competitive pressure appears as a natural determinant for trade protectionism, we capture the value of imposing countries’ imports by two variables in vector 𝜲𝜲. Following our approach for tariff overhangs, we differentiate between overall and intra-African imports. Lastly, 𝛿𝛿𝑖𝑖 are HS sections fixed effects, and 𝜈𝜈𝑖𝑖𝑖𝑖 are country-time fixed effects. The inclusion of country-time fixed effects accounts for unobserved heterogeneity at the country level, including the rolling number of RECor extra-African RTA partners and the occurrence of economic crises, both of which may determine a country’s trade policy decisions. HS sections fixed effects are employed to control for differences in the propensity to initiate German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 9 Frederik Stender / Tim Vogel SPS measures and TBT in different sectors.9 To mitigate possible reverse causality, all variables are lagged by one period. While our fixed effects specification comprehensively addresses a potential bias stemming from omitted variables, further time-varying country-product-specific factors could influence both our tariff policy measures and the notification of SPS measures and TBT. These determinants include other NTMs that are either imposed complementarily to or in exchange for SPS measures and TBT. However, neither is it possible to explicitly capture all such effects due to data limitations, especially within Africa, nor can we proxy other NTMs with country-product-time fixed effects (in exchange for those included) as this would perfectly predict our dependent variable. Due to the dichotomous nature of the dependent variable, equation (1) is implemented in a non-linear model framework using the logit estimator. The logistic regression model relates the effects of explanatory variables to the probability occurrence of a dependent variable. Since non-linear estimators are prone to the incidental parameter problem when involving a large number of fixed effects (Greene, 2012), we additionally present estimates from a linear probability model as a robustness check. Notably, given our fixed effects specification, the logit estimator only uses information on the HS categories in which at least one SPS measure and TBT was initiated over time. This leads to a relatively high prevalence of SPS and TBT initialisation in our HS2 estimation sample, and we observe SPS and TBT notifications for 10.9 per cent and 16.2 per cent, respectively, of all observations. 3.2 Data In our empirical analysis, we utilise annual country-level panel data of 35 African countries which notified either amendments or new impositions of SPS measures and TBT to the WTO between 2001 and 2017. In accordance with WTO rulebooks, the public notification of regulatory changes is obligatory in the case of divergence from international standards and a (potentially) significant impact on trade, but naturally restricted to its members.10 Imposing countries are, therefore, considered in our sample only upon their accession to the WTO, which leaves us with a highly unbalanced panel. We refer the reader to Appendix Table 1 for the full list of sample countries and their initial years of observation. For data on SPS measures and TBT, we draw on the dataset compiled by Ghodsi et al. (2017). This dataset is a user-friendly compilation of NTM notifications from the WTO’s Integrated Trade Intelligence Portal (I-TIP) and comes with the advantage of fully imputed HS codes of affected products. Imputation procedures by the authors provide for an 9 An overview of HS classifications by sections can be found at https://unstats.un.org/unsd/tradekb/Knowledgebase/50043/HS-2002-Classification-by-Section. 10 However, coverage of SPS and TBT notification is still far from complete in Africa. Grübler and Reiter (2020) note that Angola, Chad, D.R. Congo, Djibouti, Guinea-Bissau, Lesotho, Mauritania, Niger and Sierra Leone have not reported SPS measures and TBT although being WTO members. According to Aisbett and Pearson (2012), a lack of national notification authorities might be one reason for this. German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 10 Murky trade waters: regional tariff commitments and non-tariff measures in Africa allocation at the HS6 level of aggregation, but we carry out our analysis at the HS2 level as our benchmark and at the HS4 level as a data validation check because many, especially developing countries, originally report at highly aggregated sectoral levels to the WTO.11 Further, as our unit of observation is the unilateral country-product-level, we consider only those SPS measures and TBT which have been imposed multilaterally, and remove all bilateral measures. The editing, however, concerns only a handful of notifications to the WTO for African countries. The dataset by Ghodsi et al. (2017) shares with the WTO’s I-TIP the lack of a precise disentanglement of trade-hampering versus trade-facilitating SPS measures and TBT. Aiming at assessing the potential substitution between one form of trade protectionism with another, ideally, our emphasis should be on the former. While a distinction is generally possible from textual analysis of the individual measures’ descriptions, nevertheless, they are frequently complex and often touch upon a wider set of objectives. Despite the allaying clarification in Aisbett and Silberberger (2020) that trade-facilitating NTMs are a rather rare occurrence, and the broad consensus that Africa’s NTMs generally act as de facto barriers to trade, one shortcoming of our analysis is the latent confusion with trade-facilitating measures. In view of recent advancements in the recording of NTMs, other databases provide more explicit information of their (likely) effects on trade. For example, the Global Trade Alert database (https://www.globaltradealert.org/) is a high-frequency source which allows a specific break-down of various trade policy measures to the product-level and affected countries. However, the database only starts in 2008, that is, at a time when tariff liberalisation within the RECs was already fairly advanced, and moreover has a strong focus on high-income countries, with only a handful of observations on SPS measures and TBT for African countries. An alternative way to identify trade-hampering NTMs could include the exploitation of data on STCs raised at the WTO. However, the publically available database contains hardly any complaints about the NTMs of developing countries, a circumstance described by Boza and Fernandes (2016). Reasons for this finding include low trade volumes and lacking legal capacity of developing countries (see, for instance, Sattler & Bernauer, 2011; Busch, Reinhardt, & Shaffer, 2008). We match SPS and TBT notifications with product-specific MFN bound tariffs and effectively applied tariffs from the UNCTAD Trade Analysis and Information System (TRAINS) database provided through the World Bank’s (2021) World Integrated Trade Solution (WITS). Although the database is the most comprehensive source in its coverage of tariffs, data availability essentially depends on the reporting of imposing countries, and African countries especially are notoriously negligent in this respect. Data gaps are present particularly at lower levels of product aggregation but diminish at higher ones, adding another substantial justification to our preference for the HS2 and HS4 levels of aggregation. Lastly, the Base Pour l’Analyse du Commerce International (BACI) dataset provided by the Centre d’Études Prospectives et d’Informations Internationales (CEPII) (2020) is used for 11 Our dependent variable equals unity regardless of the actual number of SPS measures or TBT at lower HS levels. For example, multiple NTM notifications at the HS6 level translate into unity for our dependent variable at both the HS2 and the HS4 level. We do not make use of count data models, since the imputation based on broadly designed NTMs could lead to misleading NTM initialisations at lower HS levels. German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 11 Frederik Stender / Tim Vogel trade data. The BACI dataset is a cleaned dataset with trade data originally from United Nations Comtrade, building on the methodology of Gaulier and Zignago (2010). Full variable descriptions and respective data sources are given in Appendix Table A2. We provide summary statistics for our sample in Appendix Table A3. Emphasising the descriptive picture of Figure 2, the summary statistics show that intra-African trade is characterised by stronger tariff commitments than overall trade. While the average intra-African tariff overhang in our estimation sample is 43.6 per cent, overall tariff overhangs are on average 6 per cent lower. 4 Results and discussion 4.1 Baseline results We present our baseline logit model results as average marginal effects in Table 1. The initial four columns show findings at the HS2 level whereas the latter four indicate their respective replications at the HS4 level. Across columns, we use the composite observation of regular SPS measures and TBT as dependent variable. Given the nested relationship of the overall and regional tariff overhang variables in equation (1), a bias stemming from multicollinearity could be inherent in their joint estimation. As for the estimations in which both measures are included separately however, magnitudes of coefficient estimates are only marginally different to the ones in the full model specifications while hardly showing alteration in the comparatively small standard errors, which prompts us to advocate for the validity of our full model specification.12 Turning towards our hypotheses raised above, we begin our discussion at the HS2 level. Except for column (3), there is no statistical indication of a general relationship for applied tariff overhangs and the utilisation of NTMs by African WTO members at any of the conventional significance levels. In other words, generalised over all their trading partners, tariff policy space does not lower the demand for NTMs of African WTO members and vice versa. Although coefficient signs are predominantly negative, we thus do not find H1 to statistically hold when analysed at the highly aggregated HS2 level. A different picture is painted at the HS4 level where we find statistically significant support for H1 in the full model specifications. 12 Furthermore, the inclusion of import control variables reduces the number of observations and leads to different estimation samples across columns. Estimations using the estimation samples of columns (4) and (8), however, yield nearly identical results to those reported. German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 12 Table 1: Baseline logit model results (average marginal effects) Regular SPS and TBT (HS2) Regular SPS and TBT (HS4) VARIABLES (1) (2) (3) (4) (5) (6) (7) (8) MFN bound versus AHS World 0.0145 -0.102** -0.0545 -0.00102 -0.118*** -0.100*** (0.0196) (0.0470) (0.0424) (0.00545) (0.0226) (0.0263) Africa 0.0354* 0.124*** 0.0951*** 0.0117* 0.125*** 0.107*** (0.0183) (0.0433) (0.0368) (0.00680) (0.0228) (0.0264) Import controls World 0.0344*** 0.00649*** (0.00411) (0.000902) Africa 0.00264 -0.000492 (0.00281) (0.000761) Observations 7,455 6,954 6,946 6,293 78,636 52,451 52,443 40,192 Pseudo R2 0.3012 0.2958 0.2969 0.3307 0.4044 0.3941 0.3948 0.4051 Notes: Robust standard errors in parentheses. Asterisks denote the level of statistical significance with *** p<0.01, ** p<0.05, * p<0.1. Country-year and HS sections fixed effects always included but not reported. Source: Authors Frederik Stender / Tim Vogel Possible explanations for the insignificant overall tariff overhang estimate at the HS2 level include the neutralisation of opposing effects at more disaggregated levels and the occasionally considerable portion of intra-African trade in overall imports. Another explanation could be the dependence of African countries on imports from third countries in broadly aggregated product categories. Not least the ongoing Covid-19 pandemic has emphasised the continent’s lack of self-reliance in aggregate food and medical supplies (see, for example Akiwumi, 2020; Banga, Keane, Mendez-Parra, Pettinotti, & Sommer, 2020). More generally, effective replacement of imports from third countries is a challenge for most African economies for reason of insufficient own productive capacities. Following this line of argumentation, even in the absence of tariff policy space, it is reasonable to assume that trade protection in the form of NTMs would not appear as being desirable. Next – as we speculated under H2 – we find a positive relationship between applied tariff overhangs towards African trading partners and NTMs. The effect is estimated to be statistically significant throughout, at both the HS2 and the HS4 levels. As we discussed in Section 2, our explanation for these findings is that a larger intra-African tariff overhang could be interpreted as the enforced discrepancy of a government’s actual (multilateral) tariff policy preference, resulting from multi-layered tariff commitments in the RECs. Coupled with the fact that intra-African trade is more competitive than the continent’s trade with the rest of the world, our estimates suggest that this discrepancy fosters stronger demand for NTMs to shield domestic markets. Although we report logit model results as average marginal effects, due to the non-linear influence of explanatory variables on outcome probabilities in logit regressions, mindful interpretation of logit models should be restricted to coefficient signs and significance. This is because the inherent non-linear relationship means that average marginal effects are highly ambigiuous as true marginal effects vary significantly depending on the values of righthand-side variables. What is more, although coefficients for the intra-African tariff overhang variable are larger by magnitude than those for the overall tariff overhang variable and, with this, providing strong evidence for opposing effects on NTMs, this does not necessarily imply that tariff liberalisation undertaken by African WTO members increases the total probability of their utilisation of NTMs. Instead, more modestly, our estimations suggest that increases in intra-African tariff overhangs have a significant share in increasing the probability of SPS measures and TBT. Given that intra-African tariff overhangs are also implicit in overall tariff overhangs, with the extent depending on the share of intra-African in total imports, the net effect of tariff liberalisation on SPS measures and TBT in Africa remains ambiguous as long as coefficients for the overall tariff overhang are statistically significant. With regard to our control variables, estimates suggest that increasing overall imports are a significant driver for SPS measures and the TBT of African WTO members. The coefficient for overall imports is always positive and statistically significant, at both the HS2 and HS4 levels. The regional conditioning for intra-African imports, by contrast, is consistently insignificant. Although competition with similar countries could be attached to a comparatively larger threat to domestic industries than that with third countries, we do not find evidence for a deviating effect of imports from African trading partners on NTMs. One explanation could be that, for some regions in Africa, trade is not recorded in official German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 14 Murky trade waters: regional tariff commitments and non-tariff measures in Africa statistics but rather occurs informally, and there is empirical evidence that NTMs even increase this informality further (Bensassi, Jarreau, & Mitaritonna, 2019). Table 2: HS2 logit model results (average marginal effects) for alternative dependent variables Regular SPS Regular TBT Regular and emergency SPS and TBT VARIABLES (1) (2) (3) MFN bound versus AHS World -0.00546 -0.0712 -0.0537 (0.0450) (0.0542) (0.0414) Africa 0.0809** 0.136*** 0.0936*** (0.0400) (0.0491) (0.0360) Import controls World 0.0321*** 0.0411*** 0.0330*** (0.00505) (0.00504) (0.00397) Africa -0.000138 0.00400 0.00254 (0.00334) (0.00348) (0.00273) Observations 3,759 4,656 6,523 Pseudo R2 0.3859 0.3254 0.3336 Notes: Robust standard errors in parentheses. Asterisks denote the level of statistical significance with *** p<0.01, ** p<0.05, * p<0.1. Country-year and HS sections fixed effects always included but not reported. Source: Authors We provide full model linear probability estimates at both the HS2 and the HS4 levels as robustness checks in Appendix Table A4. For ease of comparison, we use the same sample composition as for the logit regressions.13 Our baseline findings supporting hypothesis H2 are confirmed throughout. Moreover, in unreported logit model robustness checks, we also excluded outliers in terms of overly large tariff overhangs, that is, tariff overhangs greater than 200 per cent, and used changes in imports rather than their levels, but found no significant changes to the results reported. Also, as SPS and TBT notifications could be correlated within product categories, we have additionally run estimations using clustered standard errors at the HS section and HS section-country level, with both leaving our above findings unchanged. While the above estimation results are more pronounced at the HS4 level, developing countries mainly report SPS measures and TBT at more aggregated levels (see discussion in Section 3). Moreover, data quality for trade and tariffs becomes poorer with the level of 13 Linear probability estimates using all observations – including from those HS categories without any inititation of SPS measures and TBT over time – lead to very similar results compared to those reported. German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 15 Frederik Stender / Tim Vogel disaggregation, and data are also missing for a considerable amount of years. In the following, we hence focus on the more conservative estimations at the HS2 level. Our findings thus far employ the composite observation of SPS measures and TBT as dependent variable. Columns (1) and (2) in Table 2 show estimations for the separate consideration of regular SPS measures and TBT, respectively, with no qualitative difference to our baseline results. Omitted variables could be an issue in separate regressions if we assumed substitution effects between NTMs. More specifically, countries could be reluctant to impose further SPS measures if TBT were already in place and vice versa. Traditionally, however, SPS measures and TBT affect different sectors, that is, SPS measures mainly affect agriculture while TBT affect the manufacturing trade. We have nevertheless also run model specifications taking into account the notifications of the respective other NTM, with no changes to our baseline results. Moreover, in Table 1, we focused exclusively on regular SPS and TBT notifications. However, WTO members can generally apply regulatory changes on either a permanent or temporary basis. The latter – known as emergency measures – are nevertheless highly exceptional cases and are restricted to reactions to sudden and unforeseen domestic or international incidences. In the course of the Covid-19 pandemic, for instance, several WTO members temporarily imposed more stringent SPS requirements for the import of live animals. Given their short-lived nature, there is no reason to assume a systematic relationship between emergency measures and tariff policy. For the sake of completeness, however, we have also included emergency SPS and TBT measures in the estimations. Column (3) in Table 2 reports findings when using composite regular and emergency SPS and TBT notifications as dependent variable. Yet, observation size increases only marginally and we do not find changes to the above results. 4.2 Extensions Thus far, our two key explanatory variables have been specified as the difference between MFN bound tariffs and effectively applied tariffs, thereby expressing either hypothetical overall tariff policy space or the specific deviation from multilateral commitments for intraAfrican trade relations. In the context of regional tariff commitments however, the evaluation of a country’s positioning in applied tariffs is arguably not primarily led by the comparison with multilateral commitments. In fact, even in the absence of RTA tariff commitments, WTO members rarely fall back on the application of MFN bound tariffs and instead often widely apply lower non-discriminatory MFN applied tariffs. A more relevant measure of the discrepancy between effective and desired tariffs could hence build on actual tariffs towards third countries. We proxy this hypothetical reference tariff by MFN applied tariffs and model applied tariff overhang in an alternative to our baseline specification as the difference between MFN applied tariffs and effectively applied tariffs. Following our above procedure, we define the measure for both overall tariff policy and intra-African trade relations. Estimation results are presented in Table 3. While the modification of the overhang measures increases the observation size in comparison to our baseline results in Table 1, resulting from the fact that even at a level of high aggregation tariff lines for African WTO members are not bound entirely, our above findings are confirmed throughout. Note, however, that the results in Table 3 have to be read with German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 16 Murky trade waters: regional tariff commitments and non-tariff measures in Africa caution. While regional integration within Africa has led to a discrepany between intraAfrican and MFN applied tariffs for most countries on the continent, except for the EU under the EPAs, non-African trading partners hardly ever receive tariff preferences that go beyond MFN applied tariffs. That being said, the variation for the overall tariff overhang measures mainly comes from those resulting from intra-African tariff policy, which could explain its statistical insignificance. Table 3: HS2 logit model results (average marginal effects) for alternative tariff overhang measure Regular SPS and TBT VARIABLES (1) (2) (3) (4) MFN applied versus AHS World 0.0446 -0.0416 -0.0671 (0.0622) (0.0741) (0.0836) Africa 0.0616*** 0.0692** 0.0608** (0.0239) (0.0280) (0.0248) Import controls World 0.0316*** (0.00338) Africa 0.00336 (0.00248) Observations 10,631 9,925 9,925 9,116 Pseudo R2 0.2922 0.2862 0.2862 0.3174 Notes: Robust standard errors in parentheses. Asterisks denote the level of statistical significance with *** p<0.01, ** p<0.05, * p<0.1. Country-year and HS sections fixed effects always included but not reported. Source: Authors Moreover, tariff liberalisation in the course of regional economic integration may put adverse effects on industrialisation aspirations especially in lower-income countries and LDCs. While lower-income countries and LDCs are generally often scarcely equipped with highly-skilled labour or capital, resulting in comparative advantage mainly for the production and export in low-value sectors, regional economic integration with more advanced (developing) countries offers little scope for the expansion of and diversification to higher-value economic output for these countries. By implication, with falling regional tariffs, lower-income countries and LDCs in particular may find motivation to substitute tariffs with NTMs to shield domestic (infant) industries. A natural question that arises is thus whether there are significant differences for the effect of tariff overhangs on NTMs across African countries based on their development status. 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Table A2: Variable definition and sources Variable Definition Source SPS regular Dummy = 1 if regular SPS initiated in respective HS2 line in a given year Ghodsi et al. (2017) TBT regular Dummy = 1 if regular TBT initiated in respective HS2 line in a given year SPS and TBT emergency Dummy = 1 if emergency SPS or TBT initiated in respective HS2 line in a given year OverhangWorld Difference of trade-weighted MFN bound rate and trade-weighted AHS applied rate against all trading partners World Bank (2021) OverhangAfrica Difference of trade-weighted MFN bound rate and trade-weighted AHS applied rate against African trading partners OverhangWorld (alternative) Difference of trade-weighted MFN applied rate and trade-weighted AHS applied rate against all trading partners OverhangAfrica (alternative) Difference of trade-weighted MFN applied rate and trade-weighted AHS applied rate against African trading partners ImportsWorld Value of overall imports in HS line (in thousands current USD; CIF). CEPII (2020) ImportsAfrica Value of imports from African Exporters in HS line (in thousands current USD; CIF). Notes: USD = US dollars; CIF = cost, insurance, freight German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 27 Frederik Stender / Tim Vogel Table A3: Summary statistics Data at HS2 level Data at HS4 level Variable Obs. Mean Std. Dev. Obs. Mean Std. Dev. SPS Regular 7,463 0.109 0.312 78,644 0.0738 0.261 TBT Regular 7,463 0.162 0.369 78,644 0.0951 0.293 SPS and TBT Emergency 7,463 0.0229 0.150 78,644 0.0132 0.114 OverhangWorld 7,377 37.52 46.46 67,952 29.83 31.42 OverhangAfrica 6,961 43.58 73.97 46,660 34.42 34.81 OverhangWorld (alternative) 7,441 3.567 6.947 69,237 3.024 7.555 OverhangAfrica (alternative) 7,006 9.193 49.06 47,317 6.517 14.90 ImportsWorld 6,827 380,978 1,386,492 71,788 32,358 167,540 ImportsAfrica 6,483 36,581 328,894 67,672 2,295 60,861 Source: Authors German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE) 28 Table A4: Baseline linear probability model results Regular SPS and TBT (HS2) Regular SPS and TBT (HS4) VARIABLES (1) (2) (3) (4) MFN bound versus AHS World -0.152** -0.0838 -0.135*** -0.119*** (0.0595) (0.0585) (0.0267) (0.0300) Africa 0.173*** (0.0551) 0.127** (0.0541) 0.0950*** (0.0267) 0.0753** (0.0300) Import controls World 0.0300*** (0.00384) 0.00532*** (0.000998) Africa 0.00228 (0.00273) 0.00138* (0.000790) Constant 0.192*** (0.00851) -0.143*** (0.0327) 0.139*** (0.00285) 0.105*** (0.00737) Observations 6,946 6,293 52,443 40,192 Adjusted R 2 0.276 0.302 0.321 0.336 Notes: Estimations performed with Ordinary Least Squares (OLS). 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