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Patterson C. Ekeocha , PhD Central Bank of Nigeria, Research Department

Trade Shock Transmission: A Study of Selected African Economies, the BRIC and the Rest of the Global Economy. Patterson C. Ekeocha , PhD Central Bank of Nigeria, Research Department p.ekeocha04@fulbrightmail.org ; pcekeocha@cbn.gov.ng +2348035487968 & Jonathan E. Ogbuabor , PhD

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Patterson C. Ekeocha , PhD Central Bank of Nigeria, Research Department

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  1. Trade Shock Transmission: A Study of Selected African Economies, the BRIC and the Rest of the Global Economy Patterson C. Ekeocha, PhD Central Bank of Nigeria, Research Department p.ekeocha04@fulbrightmail.org; pcekeocha@cbn.gov.ng +2348035487968 & Jonathan E. Ogbuabor, PhD Department of Economics, University of Nigeria, Nsukka, Nigeria jonathan.ogbuabor@unn.edu.ng +2348035077722 Presented at the 22nd Annual Conference on Global Economic Analysis (GTAP) on the Theme: “Challenges to Global, Social, and Economic Growth” June 19-21, 2019 Warsaw, Poland

  2. Abstract This study investigated trade shock transmission between Africa, the BRIC and the rest of the global economy with a view to understanding the likely disposition of African economies towards trade shocks. The study extended the network approach of Diebold and Yilmaz (2009) by constructing generalized trade linkage measures at various degrees of aggregation. The results indicate that the trade linkage between Africa and the rest of the global economy is quite substantial, with the total trade linkage index having an average value of 87%. We find also that China, USA, UK, Japan, EU, and Canada dominate Africa’s trade and therefore have the potential to spread trade shocks to it. The results further indicate that apart from the BRIC, other regional trading blocs such as Asia, the Americas, and Europe play influential roles in Africa’s trade. Overall, the findings show that African economies are predominantly net receivers of trade shocks originating from the aforementioned dominant sources. The study therefore concludes that the patterns of cross-country trade shock spillovers obtained in this study would likely influence Africa’s move from globalization to regional integration or multi-polarity in the years to come.

  3. Introduction • Intra-Africa’s trade performance is very infinitesimal compared to the rest of the world • E.g., Nigeria’s import from the rest of the world averaged US$42,642.77 million from 2010 to 2016; it was US$2,293.85 million within Africa • Nigeria’s intra-African export trade averaged US$10,465.65 million during 2010 to 2016, while the export to the rest of the world averaged US$81,240.03 • The low intra-Africa trade may have necessitated the move for Africa’s trade integration to fast-track its development • Thus, FTAs have become important elements in economic integration amongst most countries of the world. • Besides, the Economic Structure of virtually most African Countries, excluding South Africa, lack significant degree of diversification

  4. Introduction Cont’d • The FTAs allow nations to enter into bilateral or regional trade agreements to eliminate tariffs and non-tariff barriers • Presently, the AfCFTA is gathering momentum since globalization remains a contentious subject • Particularly, globalization renders some countries vulnerable to external shocks • As the business cycle evolves, shocks often occur • Since African countries have considerable trade linkage with the rest of the world, especially the BRIC, it is important to model the sources of foreign influence on them • Besides, the BRIC represent a model of economic development exemplified by strong economic growth and an enormous capacity to compete in a globalized world

  5. Introduction Cont’d • Furthermore, trade linkage has been adjudged an important feature of global economic integration • But there is no common view in the extant literature on whether global trade linkages would lead to business cycle harmonization • Unfortunately, World Bank’s Global Economic Prospects indicate that growth in emerging market and developing economies is expected to remain flat in 2019 • Growth in many other economies is also anticipated to decelerate • Clearly, unanticipated trade shocks could worsen the growth prospects of African economies

  6. Objectives of the Study • Broadly, this study examines trade shock propagation between Africa, the BRIC and the rest of the global economy. • Specifically, the study seeks to: • measure the degree of trade linkage between Africa, the BRIC and the rest of the global economy • determine the BRIC countries and other countries in the rest of the world that are dominating Africa’s trade and therefore have the potential to spread trade shocks to Africa and thus minimize its growth • determine the African economies that are most susceptible to trade shocks originating from the BRIC countries and the rest of the global economy • determine other trade blocs outside the BRIC that exert dominant influence on Africa and therefore have the prospects of spreading trade shocks to it

  7. Justification of the Study • While AU member states are signing the AfCFTA treaty, it is important for policy makers and governments to be aware of the potential trade shocks that may emanate from the trade linkages between Africa and the rest of the world

  8. Overview of the Literature • A growing literature has emerged on trade shock transmission in the global economic arena • Indeed, these spillovers are being hotly debated in global and national policy circles • Kose and Reizman (1999) examined the role of external shocks in explaining macroeconomic fluctuations in African countries, and find that adverse trade shocks induce prolonged recessions • The results also show that while trade shocks accounted for roughly 45% of economic fluctuations in aggregate output, financial shocks play only a minor role

  9. Literature Cont’d • Çakir and Kabundi (2011) examined the trade linkages among the BRICS (Brazil, Russia, India, China and South Africa) using the global VAR. • The results indicate that shocks from the BRIC countries have considerable impact on South Africa’s real imports and output • Huidrom et al. (2017) estimated the cross border spillovers from 7 largest emerging market economies (China, India, Brazil, Russia, Mexico, Indonesia, and Turkey) using a Bayesian VAR model since these economies constitute more than one-quarter of global output and more than half of global output growth during 2010–15. • The results indicate that: spillovers from these economies are quite sizeable, but still smaller than those from G7 countries; and among the 7 economies, spillovers from China are the largest and permeate globally

  10. Literature Cont’d • Ogbuabor et al. (2016) examined the real and financial connectedness of selected African economies with the global economy using a network approach. • The results show that: the connectedness of African economies with the global economy is quite sizable; the GFC increased the connectedness measures above their pre-crisis levels; U.S., EU and Canada dominate Africa’s equity markets, while China, India and Japan dominate Africa’s real activities • Interestingly, Lubik and Teo (2003) found that world interest rate shocks are the main drivers of business cycles in small open economies while terms of trade shocks are not. The study therefore challenged the existing literature on the roles of terms of trade and world interest rate shocks in output fluctuations in small open economies

  11. Gaps in the Literature • The foregoing overview of the literature indicates that the literature on trade linkages and trade shock transmission between Africa and the rest of the global economy is still evolving. • This study contributes to this emerging literature in several ways. First, it quantifies the degree of trade linkage between Africa, the BRIC and the rest of the global economy. • Two, apart from exposing the patterns of trade shock spillovers between Africa and the rest of the world using total trade statistics, this study also disaggregates the trade shock spillovers using exports and imports statistics. • This disaggregation provides deeper insights into the trade linkages between Africa and the rest of the world. • The study extends the empirical method by constructing trade linkage measures in a coherent and transparent manner for ease of replication

  12. The Data • The data for this study consists of the log of exports, imports, and total trade from 1990Q1 to 2016Q4 • African economies included in the study are DR Congo, Egypt, Ghana, Morocco, Nigeria, Tanzania, Tunisia and South Africa • The BRIC economies in the study are Brazil, Russia, India, and China • The rest of the global economy is accounted for by Australia, Canada, the Eurozone (EU), Indonesia, Japan, Malaysia, United States of America (USA), and United Kingdom (UK) • The selected African economies account substantially for Africa’s GDP; while the BRIC and the economies selected from the rest of the world account for the larger chunk of Africa’s trade • The data started from 1990Q1, which is the earliest available for all the countries • The entire data were converted into indices (2010Y = 100) and logged prior to estimation

  13. Methodology • A number of methodologies have been employed in the study of macroeconomic linkages and shock propagation among entities in the global economy • Examples include: cross-country correlations of Kose et al. (2003); Granger Causality measures of Caraiani (2013); etc • These methodologies captures only pairwise relations and generally dwell exclusively on testing rather than measurement and estimation of macroeconomic linkages, which are the main issues in this study • This study follows the network approach of Diebold & Yilmaz (2009), which transparently uses the size and direction of shocks to build both directional and non-directional trade linkage measures • Accordingly, this study used the forecast error variance decompositions distilled from an underlying VAR to build trade linkages measures

  14. The VAR Model • The underlying VAR model is given by: • Where: is the logged total trade of the countries selected for the study; is the vector of intercepts; is the coefficient matrix; is the lag order selected by SIC; while is the residual vector • , this study adopts the order-invariant generalized forecast error variance decompositions (GFEVDs)

  15. The Model Cont’d • Since interest is in the shocks to the disturbances, this study follows Diebold & Yilmaz (2016) and Greenwood-Nimmo et al (2015) to adopt the order-invariant generalized forecast error variance decompositions (GFEVDs) • Thus, upon estimating the VAR model, the GFEVDs are generated • Since shocks are rarely orthogonal in the GFEVD environment, we follow Diebold and Yilmaz (2014) and normalize the GFEVDs for each country in the system to 100%.

  16. Construction of the Generalized Trade Linkage Measures (GTLMs) ? • Variance decomposition permits the splitting of the forecast error variances of each variable in the VAR system into parts attributable to the various system shocks • By so doing, it becomes easy to answer the question: What fraction of the h-step-ahead error variance in forecasting is due to shocks to • Thus, our approach marries VAR variance decomposition theory and network topology theory by recognizing that variance decompositions of VARs form networks and also characterizing linkages in those networks. • This in turn characterizes trade linkages of the variables in our VAR system

  17. The Generalized Trade Linkage Measures (GTLMs) • With the normalized GFEVDs, this study constructed various GTLMs such as: • The from-effect, which measures the total directional linkage from other variables (countries) in the system to a given country • The to-effect: which measures the total directional linkage from a given country to other countries in the system • The net-effect: which shows the net transmitters/receivers of trade shocks in the system • The total trade linkage index: which captures the degree of trade linkage between Africa, the BRIC and the rest of the global economy • The dependence index: which shows the dependence (or openness) of a given country to external shocks • The influence index: which shows how influential a given country in the system is

  18. Results • The Phillips-Perron unit root tests showed that all the imports, exports and total trade series are I(1) for all the countries • The Johansen System Cointegration test showed that both the Trace and Maximum Eigenvalue statistics returned full rank, indicating that the data are not cointegrated • This study therefore estimated a VAR in first differences rather than a vector error correction model (VECM) • We set the maximum forecast horizon at 16 quarters in order to capture the long-run results better • The average values of the GTLMs over all the horizons are reported • We estimated the underlying model and computed the GTLMs separately for imports, exports and total trade

  19. Measuring the degree of trade linkage between Africa, the BRICS and the rest of the global economy • The Total Trade Linkage Index based on the total trade data shows how the most aggregated generalized trade linkage measure in this study evolved from the short-run through the long-run. • We find that: • The index rose smoothly from 84% at horizons 1 and 2 to 90% at horizons 15 and 16. • The total trade linkage index is higher in the long-run than in the short-run, indicating African economies become more interlinked in the long-run as the business cycles become more synchronized. • This finding is consistent with the trend of global macroeconomic interlinkage reported by Greenwood-Nimmo et al (2015), Diebold and Yilmaz (2016) and Ogbuabor et al (2018) that the ongoing globalization process is engendering more significant comovement in industrial production fluctuations.

  20. Measuring the degree of trade linkage between Africa, the BRICS and the rest of the global economy • The index recorded an average value of 87%, which shows that the trade linkage between Africa, the BRICS and the rest of the global economy is quite substantial • The above patterns remained qualitatively unchanged when the underlying model is estimated using the exports and imports data respectively. • However, we find that the index recorded higher values across all the horizons for exports than for imports, reflecting Africa’s trade pattern, which shows that Africa’s export trade in dollar terms is higher than its import trade

  21. Determining the countries dominating Africa’s trade and therefore have the potential to spread trade shocks to Africa • The results of the from-effect linkage show that: • Among the BRIC, China is the highest contributor to the normalized GFEVDs of the African economies. • China contributed at least 5% to each African economy, except for Tanzania. • India also made remarkable contributions. It contributed 3% to Nigeria and at least 5% to all other African economies, except for Tanzania • The roles of Brazil and Russia are only notable in South Africa, Egypt and Tunisia. • As a trading bloc, the BRIC contribute at least 20% to each African economy, except for Tanzania that showed 7%. • Thus, the BRIC play dominant role in Africa’s trade and therefore have the potential to spread trade shocks to it.

  22. Determining the countries dominating Africa’s trade Cont’d • In the rest of the global economy, we find that: • UK, USA, Japan, EU, and Canada play important roles in Africa’s trade. • UK contributed at least 6% to all the African economies, except for DR Congo • Each of USA, Japan, EU, and Canada contributed at least 4% to majority of the African economies • Overall, we find that China, UK, USA, Japan, EU, and Canada dominate Africa’s trade and that the BRIC as a trading bloc also exert a dominant influence on Africa. • These findings are consistent with the bulk of the established literature, such as Greenwood-Nimmo et al (2015), Diebold and Yilmaz (2016), Ogbuabor et al (2016), and Ogbuabor et al (2018) • These studies shows that China, USA, Japan, EU and UK are important real activity shock transmitters and therefore dominate global economic activities. • These result remain consistent when in the case of exports and imports data

  23. Determining other trade blocs dominating Africa’s trade apart from the BRIC • We find that apart from the BRIC: • The Asian trading bloc exert the most dominant influence on Africa, with China, Japan and India accounting for the bulk of this influence • The contribution of this bloc ranged from 14% in Tanzania to 33% in South Africa • The Asian bloc is followed by the Americas whose influence ranged from 5% in Tanzania to 20% in South Africa, with USA and Canada playing important roles • The role of the EU trading bloc cannot be called negligible. The European trading bloc comprises UK and EU, whose joint contribution to majority of the African economies is at least 12%. • Overall, we find that though the Asian trading bloc is playing a dominant role in Africa, the roles of the Americas and Europe are also important. • These findings remained robust when the exports and imports data were used in separate regressions

  24. Determining the African economies that are most susceptible to trade shocks from the BRIC and the rest of the World • The dependence and influence indices show that: • Except for South Africa and Egypt, all the other African economies in the system reported a negative influence index, indicating that rather than being influential, they are all vulnerable or susceptible to trade shocks • The notable influential economies in the system include China, UK, USA, Japan, Canada, EU and South Africa. These economies have positive influence index of at least 5%, and thus dominate the system. • Among the BRIC, China has the highest influence index of 16%, and in the rest of the global economy, UK and USA recorded the highest influence index of 18% and 11% respectively.

  25. Determining the African economies that are most susceptible to trade shocks from the BRIC and the rest of the World • The dependence index of African economies ranged from 71% to 92%, except for Tanzania which recorded 55%. • This indicates that the African economies are considerably open to external shocks, particularly the trade shocks originating from the dominant economies in the system. • The BRIC have dependence index ranging between 91% and 92%, while the rest of the global economy have dependence index ranging between 87% and 92%, showing that these economies are highly open economies.

  26. Determining the African economies that are most susceptible to trade shocks Cont’d • The results are consistent with studies like Ogbuabor et al (2016) • They show that African economies are mainly small open economies, deeply interlinked with the rest of the world but systemically unimportant and vulnerable to headwinds • The results remain consistent when exports and imports data are used in separate regressions • The results are also robust to the net-effect linkage of African economies

  27. Conclusion • We examined the dynamics of trade shock propagation between Africa, the BRIC and the rest of the global economy using the network approach of Diebold & Yilmaz (2009) • We extended the empirical method by constructing generalized trade linkage measures at various levels of aggregation • First, we find that African economies become more interlinked in the long-run as the business cycles become more synchronized and that the trade linkage between Africa and the rest of the global economy is quite substantial, with the total trade linkage index having an average value of 87% • Second, we find that China, USA, UK, Japan, EU, and Canada dominate Africa’s trade and therefore have the potential to spread output shocks to it.

  28. Conclusion • Third, we find that apart from the BRIC, other regional trade blocs (Asia, the Americas, and Europe) play influential roles in Africa’s trade • Fourth, we find that African economies are predominantly open but vulnerable to global trade shocks, especially those originating from the dominant sources • Our results support earlier research findings that intra-Africa’s trade has been relatively low as a result of low level of structural complementarities among these economies. • Overall, our findings suggest that African economies are predominantly net receivers of trade shocks rather than net transmitters.

  29. Policy Implications • Policymakers in Africa are able to see that the stability of the continent depends somewhat on the actions of the rest of the global economy, which are generally outside the control of the continent • Policymakers in Africa should be mindful of the chances of adverse trade shocks emanating from the aforementioned dominant sources, particularly the Asian trade partners. • Policymakers in Africa should therefore coordinate policies towards safeguarding the continent from future crisis. For example, deliberate and well-coordinated policy efforts should target the diversification of African economies as a form of insurance against future trade shocks.

  30. Policy Implications • Policymakers in Africa and the rest of the global economy can see the likely sources of future trade shocks so that appropriate policy responses to such shocks can be designed • Such policies and strategies should in turn ensure that the Africa’s common goal of shared prosperity and enhanced living standards for all its citizens are achieved on a sustainable basis. • Policymakers across the globe can see how measurement and evaluation of macroeconomic linkages can be used to improve risk measurement and management, public policy, regulatory oversight and overall economic integration

  31. Thank you

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