linkages and business cycle co-movement...economia internazionale / international economics 2020...

ECONOMIA INTERNAZIONALE / INTERNATIONAL ECONOMICS 2020 Volume 73, Issue 2 – May, 275-306

Authors: EMILIE KINFACK Department of Economics and Econometrics, University of Johannesburg, South Africa LUMENGO BONGA-BONGA Department of Economics and Econometrics, University of Johannesburg, South Africa

TRADE LINKAGES AND BUSINESS CYCLE CO-MOVEMENT: ANALYSIS OF TRADE BETWEEN AFRICAN ECONOMIES AND

THEIR MAIN TRADING PARTNERS

ABSTRACT This paper seeks to uncover what drives the nature of the link between trade linkages and

business cycle synchronization by empirically assessing this link between African economies

and their main trading partners, namely China, Europe and the United States (US). Contrary to

past papers, this paper determines endogenously the magnitude of trade linkage by assessing

how trade shocks are transmitted between Africa and its main trading partners in the periods

before and after the 1990s. Moreover, the paper assesses the extent of business cycle

synchronization between Africa and these trading partners during the same periods. The global

vector autoregressive (GVAR) model and the Instantaneous Quasi Correlation (IQC) method are

used to this end. The results of the empirical analysis show that not only the nature of trade but

also the mode of trade financing or trade settlement should matter in determining the

relationship between trade linkages and business cycle synchronisation.

Keywords: Trade Linkages, GVAR Model, Business Cycle Synchronization, Africa JEL Classification: C32, C51, F44

RIASSUNTO

Legami commerciali e comovimenti del ciclo commerciale: analisi dei rapporti commerciali

tra le economie dell’Africa e ed i loro principali partner

Questo articolo ha lo scopo di far emergere che cosa guida la sincronizzazione del rapporto tra

commercio e ciclo economico tramite la valutazione dei rapporti commerciali delle economie

africane con i loro principali partner: Cina, Europa e Stati Uniti. Al contrario degli studi già

esistenti in materia, questo paper determina la dimensione del rapporto commerciale in

maniera endogena studiando in che modo gli shock commerciali si trasmettono dall’Africa ai

276 E. Kinfack – L. Bonga-Bonga

www.iei1946.it © 2020. Camera di Commercio di Genova

suoi partner a partire dagli anni ’90. Inoltre, l’articolo valuta il livello della sincronizzazione del

ciclo economico tra Africa e i suddetti partner durante lo stesso periodo con l’utilizzo del

modello GVAR (Global Vector Autoregressive) e del metodo IQC (Instantaneous Quasi

Correlation). I risultati dell’analisi empirica dimostrano che non soltanto la natura ma anche la

modalità di finanziamento o di implementazione del commercio dovrebbe essere presa in

considerazione per determinare le relazioni di sincronizzazione tra commercio e ciclo

economico.

1. INTRODUCTION The rise of globalization has led a growing body of literature to investigate the impact of trade

linkages on business cycle co-movement between countries and regions (see Jean Louis and

Simons, 2014; Kandil, 2011). While some of these studies acknowledge that trade linkages are

important aspects of shock transmission (Frankel and Rose, 1998; Çakir and Kabundi, 2013),

there is no universal view on whether strong trade linkages lead to more or less business cycle

synchronization. For a number of studies, the relationship between trade linkages and business

cycle synchronization depends on the type of trade (intra-industry trade or inter-industry trade)

and the nature of shocks (demand or supply shock). For example, Calderón et al. (2007), Kose

and Yi (2001) and Frankel and Rose (1997) show that when intra-industry trade dominates

bilateral exchange between two countries, any industry shock contributes to the rise in the level

of business cycle correlation among these countries. However, Krugman (1991), Kenen (1969),

Baxter and Kouparitsas (2005) point out that strong trade linkages actually reduce the

synchronization of business cycles between two countries whatever the type of industry-specific

shocks affecting these countries. García-Herrero and Ruiz (2008) show that theoretical models

do not provide a clear prediction as to how trade linkages affect business cycle synchronisation

and therefore this should be a matter of empirical analysis.

Most studies that assess the relationship between the extent of trade linkage and business cycle

synchronisation have focused mostly on developed countries. For example, Asteriou and

Moudatshou (2015) investigate the determinants of business cycle synchronisation across 21

countries of the European Union. The authors find that FDI has no direct effect on business

cycle synchronisation while international trade or trade linkage contributes to the

synchronisation of business cycle across these countries but only before the 2007-2008 financial

Trade linkages and business cycle co-movement: analysis of trade between African economies and their main trading partners 277


crisis. Antonakakis and Tondl (2014) estimate the effects of market integration and economic

policy coordination on business cycle synchronisation in the EU. The authors find that trade and

FDI have increasing positive effects on business cycle synchronisation in the EU. Moreover, the

authors find strong evidence that poor fiscal discipline of EU members is a major impediment of

business cycle synchronisation. Very few studies have been conducted to assess the extent of

trade linkages and business cycle synchronisation in African countries. For example, Eggoh and

Belhadj (2015) show that while trade intensity may help to harmonise business cycle in the

Maghreb countries, the extent of the harmonisation is lower than that of industrial countries.

Hence, the authors suggest that the acceleration of trade linkage is a prerequisite for the creation

of a monetary union among Maghreb countries. Tapsoba (2009) assesses the link between trade

intensity and business cycle synchronicity among 53 African countries from 1965 to 2004. The

author finds that trade intensity increases the synchronisation of business cycle in the African

continent. Ademola et al. (2009) analyse the impact of China-Africa trade relations both at the

aggregate African and at the national level of a selected sample of countries. The authors find

that natural resources exporters across Africa are the important beneficial of this trade

relationship. Drummond and Liu (2013) evaluates the impact of changes in China’s investment

growth on SSA’s exports. Using panel data analysis, the authors show that a one-percentage

point increase (decline) in China’s domestic investment growth is associated with an average 0.6

percentage point increase (decline) in SSA countries’ export growth. The impact is higher for

resource-rich countries, especially oil exporters. Samake and Yongzheng (2014) assess the

effects of shocks to BRICS key variables on Low-income Countries (LIC) in Africa, Asia, Middle

East, Latin America and Caribbean. The authors find a direct spillover from BRICS to LICs with

bilateral trade as the main channel of shocks transmission. Diallo and Tapsoba (2016) analyse

the degree of synchronisation of Sub-Saharan Africa (SSA)’s business cycles with those of BRICs

and G7 economies. The authors find that business cycles in SSA has co-moved with BRICs’ in

recent year. The co-movement is driven by growing trade linkages between the two blocs.

However, there is no study that has ever been conducted to assess the link between trade

linkages and business cycle synchronisation between African countries and their main trading

partners, namely China, the US (United States) and the EU (European Union). Conducting such

a study will help to assess whether there is a single outcome on the link between the intensity of

trade linkage and business cycle synchronisation for countries dominated by inter-industry

trades. It is worth noting that trade linkage between Africa and the three main trading partners



is dominated by inter-industry trade, where African countries export mainly raw materials and

import variety of final products from the three partners. Moreover, assessing whether there is

more or less output synchronization between Africa and its main trading partners is important

for African economies. Firstly, more synchronized business cycles would presumably mean a

stronger and faster transmission of shocks across countries, which could provide an important

motivation for international policy coordination. Secondly, if business cycles in a country are

mostly driven by external factors, domestic policy aimed at economic stabilization will probably

have a smaller impact (see García-Herrero and Ruiz, 2008). Thus, it is important that

policymakers in Africa be aware of these realities when formulating economic policies.

The objective and contribution of this paper are threefold; firstly, to the best of our knowledge

this is the first paper that assesses the link between trade linkage and business cycle

synchronisation between Africa and its three main trading partners. Secondly, contrary to past

papers that have used different measures as proxy for the intensity of trade linkage, this paper

determines endogenously the extent of trade intensity by assessing how trade shocks transmit

between Africa and its main trading partners over the periods before and after the 1990s. The

Global VAR (GVAR) model is used to this end. Thirdly, the link between trade linkage and

business cycle synchronisation between Africa and the three main trading partners is

established by comparing periods before and after the emergence of China in the global

economy. Such a comparison helps to uncover factors that drive the link between trade linkage

and business cycle.

Our approach in investigating the issue of trade linkages between Africa and its main trading

partners is different from previous studies in that we make use of GVAR methodology to group

33 African countries in a region, referred to as Africa. In addition, the paper makes use of data

from eight European countries grouped as Euro area as well as data from 20 other countries in

the world, in addition to data from the U.S. and China. It is worth noting that a number of study

that assessed the link between trade linkage and business cycle rely on regression frameworks

and often ignored the dynamic nature of such a relationship. Moreover, the study assesses the

nature of business cycle synchronization between Africa and the three trading partners during

the corresponding sample period. Thus, the quasi correlation technique is used to assess the

extent of business cycle synchronization. In doing so, the study endeavours to assess whether

periods of strong trade linkages between Africa and its trading partners correspond to their



business cycle synchronization.

The empirical analysis is conducted in two different sample periods: the period 1980-1996 and

the period 1997-2012. The year 1996, as a cutting point of our empirical analysis, corresponds to

the economic and financial liberalization of a number of African economies and the rising

prosperity and global influence of the Chinese economy in the 1990s (Bonga-Bonga, 2012;

Compendium, 2005). In addition, the cutting point also coincides with the creation of the World

Trade organisation (WTO)1 in 1995. The establishment of WTO has permitted more trade

linkages among its members, mainly dominated by developing economies (Rena, 2012).

The rest of the paper is organised as follows. Section 2 outlines the relationship of trade between

Africa and its main partners. In this section, the study intends to show the significance of trade

between Africa and its main partners. Section 3 describes the methodology used as well as the

data sources and sample period. Section 4 presents the results and their interpretation, and the

last section concludes the study and provides some recommendations.

2. AFRICA’S RELATIONSHIP WITH ITS MAIN TRADING PARTNERS It is important to note that the relationships between Africa and Europe and Africa and the

United States can be traced back to the era of colonization. Most African countries have had

trade ties with European countries due to their colonial history and with the United States

because it was, and still is, the largest economy in the world. However, the recent global changes

in the world’s geopolitics, as shown by the resurgence of Asian economies, especially China, and

the creation of BRICS2, have dramatically altered both international relationships and world

trade. China’s quest for a closer relationship with the rest of the world has led the former to have

an influential position in the world economy, which needs to be seriously considered by the

former major players namely European Union and United States. This is especially evident in

the case of Africa, where the emergence of China has significantly altered Africa’s direction of

trade, which had been dominated by Europe and the United States (Obuah, 2012).

African countries, as an economic bloc, occupy a very low position in the global market. The

continent’s share of world trade is insignificant. According to the African Union Commission

1 Replacing the General Agreement on Tariffs and Trade (GATT). 2 Brazil, Russia, India, China and South Africa.



(2013), the Africa’s total imports account for only 1.8% of world imports, while its total exports

represent 3.6 % of world exports. Nonetheless, the three major African trading partners

continue to influence the economy of the continent to a varying extent. Figures 1 and 2 present

the trade share and total trade between Africa and each of the three trading partners,

respectively. By definition, the total trade is simply the sum of the values of export and import.

Figure 1 shows that, of the total trade between Africa and the three main partners, the trade

share between Africa and the Euro area decreased from 93% in 1970 to 33% in 2013. However,

the trade share between Africa and China increased from 1% in 1970 to 46% in 2013 making

China Africa’s number one trade partner since 2009 (Global Times, 2013). Figure 2 shows the

trend of total trade between Africa and the three main trading partners from 1970 to 2013. The

increasing trend in trade between the Euro area and Africa in the 1970s experienced a

remarkable decline in the early 1980s, due mainly to the negative effects of the 1980 global crisis.

Nevertheless, Figure 2 shows an increasing trend of trade in early 2000s between Africa and

each of the three main partners. This occurrence can be dubbed as a race for Africa’s trade share

from the three main partners as it is during this period that a number of trade agreements were

signed between Africa and each of the three partners. For example, the commitment made by

United States in 2000 in order to support the development of trade in Africa through the African

Growth and opportunity Act (AGOA) has resulted in an increase in African exports to the US.

The AGOA act provides duty-free market access to US for some qualifying African countries.

Since its approval in May 2000, total African exports to the US have more than quadrupled3 as

shown in Figure 2.

Through the forum on China-Africa cooperation (FOCAC) established in October 2000, China

signed bilateral trade and investment treaties and created joint economic commission

mechanisms to support African countries.

3 African Union (2013).



FIGURE 1 - Trade Share between Africa and its Main Trading Partners from 1970 to 2013

Source: Direction of Trade statistic’s international financial statistic (online databases various years).

FIGURE 2 - Total Trade between Africa and its Main Trading Partners from 1970-2013

(billion US dollar)

Source: Direction of Trade statistic’s international financial statistic (online databases various years). It is important to note that China’s trade with Africa was insignificant between 1970 and early

1990s, due to the limited relationship between the African continent and China. However, the

emergence of China a significant force in the global economy has changed the direction of trade

0

20

40

60

80

100

1970 1975 1980 1985 1990 1995 2000 2005 2010

CHINA-AfricaEURO-AfricaUS-Africa

0

50

100

150

200

250

300

1970 1975 1980 1985 1990 1995 2000 2005 2010

CHINA-AfricaEURO-AfricaUS-Africa



in a number of countries, including African economies. Many countries in Africa have shifted

their direction of trade from the US and EU towards China (Obuah, 2012). As illustrated in

Figure 1, China’s trade share with Africa started to rise from 1997 onwards to become the largest

Africa’s trade partner in 2009. Figure 2 indicates that the global financial crisis of 2007 did not

affect China’s trade share with Africa largely as it did with the US and the Euro area. China

dominates the market as Africa’s biggest destination of oil and mineral exports (Lin and Farrell,

2013). However, trade between Africa and China is not without controversy. For example, Moyo

(2012) questioned the nature of the China-Africa trade relationship. The author points out that

Chinese firms are desperately in search of natural resources, which might have some negative

repercussions for the world in general and Africa in particular in the near future. Nonetheless,

trade linkages between China and Africa have helped African countries to establish an

upstream-downstream-integrated industry chain transforming resource advantages into

economic growth opportunities. For example, in the Democratic Republic of Congo and other

energy- and mineral resources-endowed African economies, Chinese enterprises have built up

infrastructure in response to the extraction and exploitation of mineral resources (Global

Times, 2013). A number of studies show that trade deal between Africa and China contributed to

the rise of infrastructure development and sustained economic growth in a number of African

countries. This is not necessarily the case for trade between Africa and the US and Europe

(Acemoglu et al., 2001, 2002; and Boopen, 2006).

TABLE 1 - African Exports to Selected Countries in the World (million USD)

2013 2014 2015 2016

China 75980.99 65703.89 40351.07 41130.99

United States 36307.57 25707.05 20231.13 22530.98

France 36008.23 32722.49 24041.74 21718.54

Spain 31731.03 34563.40 27223.20 20238.45

Italy 45179.46 33455.59 20319.61 19289.01

Singapore 3985.06 5331.89 4111.09 16004.51

India 32225.77 33989.13 24955.37 22752.89

Brazil 15341.51 15340.19 7048.48 4019.81 Source: African Union (2018).



Table 1 displays the value of exports between African countries, members of African Union (AU)

and selected extra-African Union countries. It is clear from the table that China, United States

and members of EU remain the important trade partners of African economies, with China as

their most influential trade partner.

3. METHODOLOGY AND DATA

3.1 Methodology The key contribution of the GVAR model, as proposed by Pesaran et al. (2004), and further

developed by Dees et al. (2007), is its capacity to model domestic variables together with global

observed and unobserved factors represented by foreign variables. In doing so, the GVAR model

is suitable for examining international shocks and their spillover effect among countries (Dees et

al., 2007; Bussière et al., 2012; Galesi and Lombardi, 2009; Vasishtha and Maier, 2013). The

GVAR modelling strategy consists of two main steps; firstly, a VARX model is estimated for each

country, whereby a VAR model is augmented with weakly exogenous variables, mainly foreign

variables. The VARX model is expressed as:

ddxxxtaax itt,itit,iiitit,iiiiit εΓΓΛΛφ +++++++= −∗

−∗

− 110110110 (1)

Where itx is a ( )1×ik vector of domestic variables for each country 𝑖 at time 𝑡. *

itx is a ( )1×*ik

vector of foreign variable. ioa is a ( )1×ik vector of fixed intercept coefficient. 1ia is a ( )1×ik

vector of coefficients of the deterministic time trend, iφ is a ( )ii kk × matrix of coefficient

associated with lagged domestic variables. 0iΛ and 1iΛ are ( )*ii kk × matrices of coefficients

related to foreign and lagged foreign variables respectively. dt is a set of common global

variables assumed to be weakly exogenous to the global economy, but should be endogenous to

only one country, the reference country. As the United States is taken as the reference country in

this paper, thus, td is endogenous for this specific country. i0Γ and 1iΓ are matrices of fixed

coefficients. The error term itε is a ( )1×ik vector of shocks specific to each country, which is

assumed serially uncorrelated with average equal zero, and with a non-singular covariance

matrix that is ( )→ iiit 0, .d.i.iε . The VARX model represented in Equation 1 can be re-written

in the error correction form to allow for cointegration relationship within and across countries

included in the GVAR system.



Secondly, country-specific VARX models are stacked to form the global model. In the global

model, country-specific foreign variables are calculated as weighted averages of the

corresponding variables in other countries such as:

𝑥∗ = ∑ 𝑊 𝑥 (2) Where Wi is the weighting matrix used to construct foreign variables from the cross-country

domestic variables. To construct the global VAR model from the individual country-specific

models, we assume a matrix Z that combines domestic and foreign variables for each country

within a single vector, such as:

( )∗= ititit x,xZ

Therefore, Equation 1 can be rewritten as:

𝐴 𝑍 = 𝑎 + 𝑎 𝑡 + 𝐵 𝑍 , + ∑ Γ 𝑑 + 𝜀 (3)

Given Equation 2, Equation 3 can be expressed as

𝐴 𝑊 𝑥 = 𝑎 + 𝑎 𝑡 + 𝐵 𝑊 𝑥 , + ∑ Γ 𝑑 + 𝜀 (4)

Or

𝐺 𝑥 = 𝑎 + 𝑎 𝑡 + 𝐺 𝑥 , + ∑ Γ 𝑑 + 𝜀 (5)

Where 𝐺 = ... and 𝐺 = ...

It is important to note that G0 should be singular to allow the solving of the model represented in

Equation 5 from which the interdependence and dynamics of variables can be assessed with the

aid of the impulse response functions (IRF). This study makes use of the generalised impulse

response function (GIRF) as suggested by Pesaran and Shin (1998).

From information at time t-1 , the GIRF is obtained by using:

𝐺𝐼𝑅𝐹 𝑥 , 𝑡 + ℎ, 𝛿 , 𝐼 = 𝐸(𝑥 / 𝜇 , , = 𝛿 , 𝐼 ) − 𝐸(𝑥 /𝐼 ) (6)

Where It-1 represents the information available at time t-1, h is the forecast horizon, 𝑥 represents

the variables included in our GVAR model and 𝛿 denotes the shock to the 𝑗 variable.



Moreover, the solution of Equation 5 can help to examine the contribution of a variable in

explaining the shocks to other variables through the forecast error variance decomposition

(FEVD) analysis.

3.2 Data and Modelling The global VAR applied in this paper contains 63 countries, 33 of which are African countries

while the remaining 30 are from other regions in the world. Table 1A in the appendix displays the

list of countries included in the sample. The sample period is divided into two sub-periods: the

first sub-period 1980Q1-1996Q4, which coincides with an increasing international trade

relationship between Africa and of the US and Europe. The period also reveals a time of financial

repression for most African economies. On the other hand, the second sub-period 1997Q1-

2015Q44, illustrates the financial liberalization of many African countries (Bonga-Bonga, 2012)

and the emergence of China as dominant force in the global economy. Given the main objective

of the paper to assess the link between trade linkage and business cycle synchronisation between

Africa and its three main trading partners, the variables included in the estimation are the

following: real GDP, real export, real import, inflation rate and the oil price (see Table 2A for the

names and codes of these variables). With these variables, this paper determines endogenously the

extent of trade intensity by assessing how trade shocks transmit between Africa and its main

trading partners over the periods before and after the 1990s.

The first stage in the construction of the model is to define the domestic and foreign variables.

For country .N3......... 2,,i 1= , the following country-specific domestic itx and foreign variables

∗itx are considered:

𝑥 = (𝑦 , 𝑒𝑥 , 𝑚 , 𝐷𝑃 , 𝑒𝑝 ) and 𝑥∗ = (𝑦∗ , 𝐷𝑝∗ , 𝑝𝑜𝑖𝑙 )

Where y is the real Gross Domestic Product, Dp is the inflation rate, ep is the real exchange rate, 𝑒𝑥 is the real export, m represents total imports and poil is the oil price. The oil price is treated as

an exogenous variable for all the countries included in the sample except for the US since it is

considered as the reference country in our GVAR model.

4 While the end period may be due to data availability, nonetheless, it provides sufficient data sample to observe period post 1996.



In our GVAR model, the country-specific foreign variables are built using fixed trade weights.

The country-specific foreign variables are defined as follows:

==

N

jitij

*it ywy

0; =

=N

jitij

*it DpwDp

0 and =

=N

jitij

*it epwep

0.

Where the weight ijw is computed as the share of country j in the total trade of country i .

Although Dees et al. (2007) used the time-varying trade weights in their study, however, the

authors concluded that these have a small impact on the results of the GVAR models. It is

important to note that the number of missing data in the trade flows of some African hampered

the use of time-varying trade weights for this paper. The trade shares for the Africa economies

with its main partners and the rest of the world are presented in Table 3A.

Given its aim, the paper aggregates countries as follows: firstly, eight Europeans countries are

put in a single regional model and secondly all African countries are specified in a single regional

VARX*. Thus, the regional variable such as 𝑦 , 𝑒𝑥 , 𝑚 , 𝑒𝑝 , 𝐷𝑝 and 𝑝𝑜𝑖𝑙 are built from the

country-specific variables using the following weighted averages:

ipt

N

pipit ywy

i

=

=1

0

Where ipty indicates output of country p in region i and 0ipw are the Purchasing Power Parity-

GDP weights (PPP-GDP). Since the study estimates the region in two periods, 1980Q1-1996Q4

and 1997Q1-2015Q4, the regional weight is constructed for each of these periods. The weights

are constructed by averaging the PPP-GDP for each country over a period of three years,

depending on the sample period covered by the study. For example, in the first sub-period, 1980-

1996, the PPP-GDP weight is for the period 1990-1992, while for the second sub-sample the PPP-

GDP used is computed for the period 2006-2008. It is important to note the weights (PPP-GDP)

used to group countries into one region is different from the weight (trade weight) used to

generate foreign-specific variables.

Before proceeding with the estimations, different tests are performed. The unit root test is

conducted to ascertain the level of integration of variables. The study conducts the unit root test



using both the Augmented Dickey-Fuller (AD) and the Weighted Symmetric Augmented Dickey-

Fuller (WS-ADF) test. The latter uses the time reversibility of stationary autoregressiveness.

The lag order for both tests (ADF and WS-ADF) is determined by the minimization of the Akaike

Information Criterion (AIC), for which the maximum lag allowed is set to 4. The study reports

results from the WS-ADF test (see Table 4A)5. The results from the unit root test show that most

of the variables are stationary in difference.

Having verified the stationarity of the variables, the next step is to determine whether there is

cointegration between variables. The study then uses the Johansen (1992, 1995) reduced-rank

procedure. The cointegration rank is derived by employing the trace test statistic at the 95%

critical values and the maximum eigenvalue statistics. Table 5A presents the number of

cointegrating ranks obtained for each of our focus economy VARX* model as well as the lag

orders for each of their domestic and foreign variables. The study also conducts the weak

exogeneity test for foreign and global variables. This test is the key assumption of the GVAR

approach. The weak exogeneity assumption in the context of co-integrating models implies no

long-term feedback from itx to ∗itx , without necessarily ruling out lagged short-run feedback

between the two sets of variables. With reference to Dees et al. (2007), we employ the weak

exogeneity tests proposed by Johansen (1992) and Harbo et al. (1998). The results of F-statistics

for testing the weak exogeneity of Africa and its main partner’s country-specific foreign

variables and the oil price are reported in Table 6A. The results show that most of the weak

exogeneity assumptions are accepted.

It is important to note that the variables used in this paper are collected from the International

Financial Statistics (IMF) database, the Direction of Trade Statistics (DoT) of the IMF and the

World Bank database. We also use interpolation in some cases, with the cubic spline method, in

order to convert real GDP annual data into quarterly data. This was done to construct data for

some African countries, where real GDP quarterly data is not available.

4. RESULTS AND INTERPRETATION This section presents the empirical results of the degree of trade linkage and the effects of shock

transmissions between Africa and its main trading partners based on the generalized impulse

5 Other unit roots results can be obtained on request.



response functions (GIRFs). The GIRFs are used to assess the effect of the different shocks on

variables of interest over a time horizon of 40 quarters. Nonetheless, the paper focuses on the

results over eight quarters, which is a reasonable period for making inferences about short-term

macro-economic dynamics (Dees et al., 2007). The results of the GIRFs reported in Figures 3 to

8 include the confidence intervals at the 95 % significance level, calculated using the bootstrap

technique with 1000 replications. Moreover, this section presents the empirical findings of the

business cycle co-movement between Africa and its trading partners.

4.1 Generalized Impulse Response Function of Shocks in the Context of Trade Linkages

In order to consider the extent of trade linkage between Africa and its three main trading

partners, we consider two positive shocks, namely shocks to exports and imports from the three

main trading partners, and their dynamic effects on African imports and exports. It is important

to note that when two countries are linked through trade, an increase in exports or imports in

one country is translated into an increase in imports or exports in the other country. Figures 3, 4

and 5 present the dynamic responses of exports and imports of Africa to shocks to trade

variables from the Euro area, the US and China, respectively during the sub-sample periods

1980-1996 and 1997-2015.

Figure 3 displays positive real export and import shocks from the Euro area. It shows that the

export shock from the Euro area has a positive impact on African imports during the sub-sample

period 1980-1996. The effect is statistically significant from the second to the fourth quarter.

Nonetheless, the positive response of African’s import to shocks from Euro area is short lasting

and statistically insignificant for most of the time horizon. Likewise, positive import shocks

from the Euro area translate into a rise in Africa’s real exports, with the effect being statistically

significant on impact and for more than 15 quarters during the sub-sample period 1980-1996.

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20 Volume 73, I

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ranslated



4.3 Business Cycle Synchronization between Africa and its Main Trading Partners

As stated earlier, section 5.2 provided a basis from which business cycle synchronization

between Africa and the three main trading partners could be assessed. For example, the finding

that positive shocks to China’s GDP has a positive effect on African countries’ GDPs during the

sub-period 1997-2015 might indicate the possibility of business synchronization between China

and Africa during this period. However, such an extrapolation needs to be validated by an

appropriate method for business cycle synchronization.

There are number of different methods for measuring synchronization of business cycle

between individual and groups of countries. To measure the degree of business cycle

synchronization between Africa and each of its three main trading partners, this paper makes

use of the Instantaneous Quasi Correlation (IQC) method employed by Duval et al. (2014).

According to Duval et al. (2014) the quasi correlation is defined as follows:

gj

gi

*jjt

*iit

ijt)gg()gg(

QCORRσσ ×

−×−=

Where ijtQCORR is the quasi-correlation of real GDP growth rates of country i and j in year t.

itg denotes the output growth rate of country i in year t. *ig and g

iσ represent the mean and

standard deviation of output growth rate of country i respectively, during the sample period.

Duval et al. (2014) show that this measure is a better proxy of business cycle synchronization

than the others for the following reasons. Firstly, it provides a dynamic correlation measure, as it

enables the calculation of co-movement at every point in time rather than over an interval of

time. Secondly, the quasi correlation is not bounded between -1 and 1. A number of authors show

that when the business cycle synchronization measure lies between -1 and 1, the error terms in

the regression explaining it are unlikely to be normally distributed (Otto et al., 2001 and Inklaar

et al., 2008).

The bilateral business cycle synchronization between Africa and each of its main partners is

presented in Figure 9 below. Figure 9 shows a lack of steady business synchronization between

Africa and its three main trading partners during the sub-period 1980-1996. The correlation

between Africa’s GDP and the GDP of its three main trading partners swerves between positive

and negative values, thus signalling the lack of persistent business cycle synchronization.



Afric-US Afric-china Afric-euro Afric-US Afric-china Afric-euro

Nonetheless, in the sub-period 1997-2015, there is regular business cycle synchronization

between Africa and China, especially from the year 2000 onward. High business

synchronizations between Africa and the US and Africa and the Euro area occur during periods

of economic and financial crises, during which the US and Euro experience the decline of their

economic activities. For example, Figure 9 shows that in the early 1980s the global financial

crisis that led to a severe recession in the US also affected African economies. It is also clear from

Figure 9 that the high correlations or business synchronizations between Africa and the US and

Africa and the Euro area is mostly attributed to the effects of contagion, such as the spillover

effects of the 2007 global financial crisis, which originated from the US. This indicates that

business cycles between Africa and the US and Africa and the Euro area mostly synchronize due

to the direct effect of contagion. However, Figure 9 shows an abrupt decrease in business

synchronisations between Africa and the Euro area and Africa and the US from 2008, indicating

the remarkable resilience of African countries from the global financial crisis. This reality

supports the finding of many studies showing that a number of emerging market economies have

‘decoupled’ from developed economies during the recent financial crisis, with the correlation

among emerging markets and developed economies being larger in bullish than in bearish

markets (see Levy-Yeyati and Williams, 2012).

FIGURE 9 - GDP Synchronization between Africa and its Main Trading Partners

(1980-1996) (1997-2015)



To test the robustness of our results in relation to the extent of business cycle synchronisation

between Africa and its three main trading partners, we make use of the rolling window

correlation, with 4 quarters windows, to assess the dynamic correlation between Africa’s GDP

and the GDP of each of its trading partners. The results reported in Figure 10 confirm those in

Figure 9 with a steady synchronisation of business cycle between Africa and China during the

sub-period 1997-2015.

The findings of this paper raise a question on whether trade linkage necessarily links to business

cycle synchronization. While periods of high trade linkage between Africa and China coincide

with their business cycle synchronization, this is not true for the case of Africa–US and Africa–

Euro. This finding shows, that contrary to past studies (Calderón et al., 2007, Kose and Yi, 2001

and Frankel and Rose, 1997), the link between trade linkages and business cycle is not

necessarily related to the type of trade (inter- or intra-industry trade). The results of this paper

also show that there are different outcomes regarding the relationship between trade linkages

and business cycle even for countries involved in inter-industry type of trade relationship.

Hence, this paper hypothesises that the mode of trade financing or trade settlement might be the

important factor that relates business cycle to trade linkages. This hypothesis is substantiated by

the fact that the growth in trade between Africa and China has been triggered by the use of

resource-for-infrastructure swap agreement, also known as Angola-mode deals (see

Habiyaremye, 2016). With this agreement, Chinese companies finance and build infrastructure

in African countries in exchange for access to natural resources. Infrastructure deficiency has

been the bottleneck for economic growth in many African countries. Thus, the resource-for-

infrastructure swap agreement does not only provide an opportunity to intensify trade linkages

between Africa and China, but has also provided an opportunity for economic growth in a

number of African countries through infrastructure development. With the resource-for-

infrastructure swap agreement a number of African countries have acquired modern

infrastructure such as roads, power generation and electricity coverage, which contribute to

enhance their productivity.

Trade link

ECONOMIA

FIGURE

kages and busine

A INTERNAZION

10 - Rolling

-1

-0,5

0

0,5

1

1980

q119

81q3

-1,5

-1

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3

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1980

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ess cycle co-movem

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g Window Co

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q319

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q319

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orrelation be

1987

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1993

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1999

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2 005

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ea

es and their main

20 Volume 73, I

the GDP of i

2007

q120

08q3

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n trading partner

Issue 2 – May, 2

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2013

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rs 297

275-306

Partners



5. CONCLUSION This paper endeavoured to investigate the extent of trade linkages and business cycle

synchronization between Africa and its three main trading partners, namely China, the Euro

area and the US, during two different periods, namely 1980 to 1996 and 1997 to 2015. The paper

makes use of GVAR methodology to assess the extent of shocks transmission between Africa and

each of the three main trading partners. Particularly, the paper assesses how import and export

shocks from each of the three main trading partners affect the dynamics of exports and imports

in Africa. The results of the GVAR methodology makes plausible inferences as to the extent of

trade linkages between Africa and its three trading partners during the two periods. Moreover,

the paper makes use of the Instantaneous Quasi Correlation method to measure the degree of

business cycle synchronization between Africa and each of the three trading partners during the

same periods. The results based on the generalized impulse response functions indicate an

increasing trade linkage between Africa and the Euro area in the periods 1980-1996 and 1997-

2015 where it is shown that trade linkages between Africa and China become more significant

during the period 1997-2015 than during the period 1980-1996.

The results based on Instantaneous Quasi Correlation and dynamic correlation show the

synchronization of business cycles between Africa and China during the period 1997-2015.

However, the results show that there is no consistent business cycle synchronization between

Africa and the US and Africa and the Euro area in the two periods and that the observed

infrequent business cycle co-movements between Africa and the US and Africa and the Euro

area can mostly be attributed to the direct effect of contagion.

This paper contributes to the literature of trade linkages and business cycle synchronization by

showing that not only the nature of trade but also the mode of trade financing or trade

settlement matters in determining the relationship between trade linkages and business cycle

synchronisation. Policymakers in Africa need to establish proper mechanisms that could

improve the way their exports are financed, which could result to wealth creation. Moreover,

proper policy coordination are needed by African countries to mitigate possible contagion that

could result from negative shocks to their main trading partners.

For further studies, we suggest that the effects of trade shocks to African main trading partners

be considered on regional trading blocs in Africa.



REFERENCES

Acemoglu, D., S. Johnson and J.A. Robinson (2001), “The Colonial Origins of Comparative

Development: An Empirical Investigation”, American Economic Review, 91(5), 1369-1401.

Acemoglu, D., S. Johnson and J.A. Robinson (2002), “Reversal of Fortune: Geography and

Institutions in the Making of the Modern World Income Distribution”, Quarterly Journal

of Economics , 117(4), 1231-1294.

Ademola, O., A. Bankole and A. Adewuyi (2009), “China-Africa Trade Relations: Insight from

AERC Scoping Studies” European Journal of Development Research, 21(4), 485-505.

African Union (2013), Status of Integration in Africa (SIA IV), Africana Union Commission:

Addis-Ababa.

African Union (2018), African Trade Statistics Yearbook 2018, African Union Commission:

Addis-Ababa.

Antonakakis N. and G. Tondl (2014), “Does Integration and Economic Policy Coordination

Promote Business Cycle Synchronization in the EU?”, Empirica, 41(3), 541-575.

Asteriou, D. and A. Moudatsou (2015), “Business Cycle Synchronisation in the Enlarged EU: The

Role of Bilateral Trade and FDI”, Review of Development Economics, 19(1), 196-207.

Baxter M. and M. Kouparitsas (2005), “The Determinants of Business Cycle Co-Movement: A

Robust Analysis”, Journal of Monetary Economics, 52(1), 113-157.

Bonga-Bonga, L. (2012), “Equity Prices, Monetary Policy, and Economic Activities in Emerging

Market Economies: The Case of South Africa”, Journal of Applied Business Research,

28(6), 1217-1228.

Boopen, S. (2006), “Transport Infrastructure and Economic Growth: Evidence from Africa

Using Dynamic Panel Estimates” , The Empirical Economics Letters, 5(1), 37-52.

Busse, M., C. Erdogan and H. Muehlen (2016), “China’s Impact on Africa - The Role of Trade,

FDI and Aid”, Kyklos, 69(2), 228-262

Bussière, M., A. Chudik and G. Sestieri (2012), “Modelling Global Trade Flows: Results from a

GVAR Model”, Federal Reserve Bank of Dallas Working Paper N0. 119.

Çakir, M.Y. and A. Kabundi (2013), “Trade Shocks from BRIC to South Africa: A Global VAR

Analysis”, Economic Modelling, 32, 190-202.

Calderón, C., A. Chong and E. Stein (2007), “Trade Intensity and Business Cycle

Synchronisation: Are Developing Countries any Different?”, Journal of International



Economics, 71(1), 2-21.

Compendium (2005), China Compendium of Statistics 1949-2004, China Statistics Press:

Beijing.

Cook, N.P.S. and J.C. Jones (2015), “The African Growth and Opportunity Act (AGOA) and

Export Diversification”, Journal of International Trade and Economic Development, 24(7),

947-967.

Darvas, Z. and G. Szapáry (2008), “Business Cycle Synchronization in the Enlarged EU”, Open

Economies Review, 19(1), 1-19.

Dees, S., F. di Mauro, M.H. Pesaran and L.V. Smith (2007), “Exploring the International

Linkages of the Euro Area: A Global VAR Analysis”, Journal of Applied Econometrics, 22(1),

1-38.

Diallo, O. and S.J.A. Tapsoba (2016), “Rising BRIC and Changes in Sub-Saharan Africa’s Business

Cycle Patterns”, The World Economy, 39(2), 260-284.

Diaw, D. and A. Lessoua (2013), “Natural Resources Exports, Diversification and Economic

Growth of CEMAC Countries: On the Impact of Trade with China”, African Development

Review, 25(2), 189-202.

Didia, D., M. Nica and G. Yu (2015), “The Gravity Model, African Growth and Opportunity Act

(AGOA) and US Trade Relationship with Sub-Saharan Africa”, Journal of International

Trade and Economic Development, 24(8), 1130-1151.

Drummond, P. and E.X. Liu (2013), “Africa’s Rising Exposure to China: How Large are Spillovers

through Trade?”, IMF Working Paper No. WP/13/250.

Duval, R., K. Cheng, K.H. OH, R. Saraf and D. Seneviratne (2014), “Trade Integration and

Business Cycle Synchronization: A Reappraisal with Focus on Asia”, IMF Working Paper

No. WP/14/52.

Eggoh, J. and A. Belhadj (2015), “Business Cycles in the Maghreb: Does Trade Matter?”, Journal

of Economic Integration, 30(3), 553-576.

Fidrmuc, J. and I. Korhonen (2006), “Meta-Analysis of the Business Cycle Correlation between

the Euro Area and the CEECs”, Journal of Comparative Economics, 34(3), 518-537.

Frankel, J.A. and A.K. Rose (1997), “Is EMU More Justifiable Ex Post than Ex Ante?”, The

European Economic Review, 41(3-5), 753-760.

Frankel, J.A. and A.K. Rose (1998), “The Endogeneity of the Optimum Currency Area Criteria”,

Economic Journal, 108(449), 1009-1025.



Galesi, A. and M. Lombardi (2009), “External Shocks and International Inflation Linkages: A

Global VAR Analysis”, European Central Bank, Working Paper No 1062 .

García-Herrero, A. and J.M. Ruiz (2008), “Do Trade and Financial Linkages Foster Business

Cycle Synchronization in a Small Economy”, Bank of Spain Working Paper No. 0810.

Global Times (2013), China-Africa Economic and Trade Cooperation, available at

<http://www.globaltimes.cn/content/807255.shtml>, accessed 27 December 2018.

Habiyaremye, A. (2016), “Is Sino-African Trade Exacerbating Resource Dependence in Africa?”,

Structural Change and Economic Dynamics, 37(C), 1-12.

Harbo, I., S. Johansen, B. Nielsen and A. Rahbek (1998), “Asymptotic Inference on Cointegrating

Rank in Partial Systems”, Journal of Business & Economic Statistics, 16(4), 388-399.

Inklaar, R., R. Jong-A-Pin and J. de Haan (2008), “Trade and Business Cycle Synchronization in

OECD Countries: A Re-Examination”, European Economic Review, 52(4), 646-666.

Jean Louis, R. and D. Simons (2014), “Business Cycles Synchronicity and Income Levels: Has

Globalisation Brought us Closer than Ever?”, The World Economy, 37(5), 592-624.

Johansen, S. (1992), “Cointegration in Partial Systems and the Efficiency of Single-Equation

Analysis”, Journal of Econometrics, 52(3), 389-402.

Johansen, S. (1995), Likelihood-Based Inference in Cointegrated Vector Autoregressive Models,

Oxford University Press: Oxford.

Kandil, M. (2011), “Trade Flows, Financial Linkage, and Business Cycles in Latin America”,

Journal of Economic Integration, 26(3), 526-553.

Kenen, P.B. (1969), The Theory of Optimum Currency Area: An Eclectic View, in: R. Mundell, A.K.

Swoboda (Eds), “Monetary Problems of the International Economy”,, University of

Chicago Press: Chicago, IL.

Kose, A. and K-M. Yi (2001), “International Trade and Business Cycles: Is Vertical

Specialization the Missing Link?”, American Economic Review, 91(2), 371-375.

Krugman, P.R. (1991), Geography and Trade, MIT Press: Cambridge, MA.

Levy-Yeyati, E. and T.Williams (2012), “Emerging Economies in the 2000s: Real Decoupling and

Financial Recoupling”, Journal of International Money and Finance, 31(8), 2102-2126.

Lin, X. and C. Farrell (2013), “The Internationalisation Strategies of Chinese State and Private

Sector Enterprises in Africa”, Journal of African Business, 14(2), 85-95.

Moyo, D. (2012), Winner Takes All: China’s Race for Resources and what it Means for Us, Allen

Lane: London.



Obuah E. (2012), “Trade between China and Africa: Trends, Changes, and Challenges”,

International Journal of China Marketing, 2(2), 74-88.

Otto, G., G. Voss and L. Willard (2001), “Understanding OECD Output Correlations”, Reserve

Bank of Australia Research Discussion Paper No. 2001-05.

Pesaran, M.H., T. Schuermann and S.MC. Weiner (2004), “Modelling Regional

Interdependencies Using a Global Error-Correcting Macroeconomic Model”, Journal of

Business and Economic Statistics, 22(2), 129-162.

Pesaran, M.H. and Y. Shin (1998), “Generalized Impulse Response Analysis in Linear

Multivariate Models”, Economics Letters, 58(1), 17-29.

Rena, R. (2012), “Impact of WTO Policies on Developing Countries: Issues and Perspectives”,

Transnational Corporations Review, 4(3), 77-88

Samake, I. and Y. Yongzheng (2014), “Low-Income Countries’ Linkages to Brics: Are there

Growth Spillovers?”, Journal of Asian Economics, 30(C), 1-14.

Tapsoba, S.J. (2009), “Trade Intensity and Business Cycle Synchronicity in Africa”, Journal of

African Economies, 18(2), 287-318.

UNCTAD (2013), World Investment Report, United Nations: Geneva.

Vasishtha, G. and P. Maier (2013), “The Impact of the Global Business Cycle on Small Open

Economies: A FAVAR Approach for Canada”, The North American Journal of Economics

and Finance, 24(C), 191-207.



APPENDIX

TABLE 1A - List of Countries Included in the Sample

Region : Africa Region : Euro area Other countries

Algeria Gabon Niger Austria Australia United Kingdom

Benin Gambia Nigeria Germany Brazil Peru Burkina Faso Ghana Senegal France Canada Philippines

Burundi Guinea-Bissau Seychelles Belgium Chile Singapore Cameroon Kenya Sierra Leone Finland China USA Cape Verde Madagascar South Africa Italy Cyprus Japan Chad Malawi Tanzania Netherlands Denmark Malaysia Congo DRC Mali Togo Spain Greece Mexico Congo Mauritius Tunisia Iceland Switzerland Cote d'Ivoire Morocco Uganda India Thailand

Egypt Mozambique Zambia New Zealand Norway

TABLE 2A - Variables Used, Code and Data Sources

Variables Short Name Formula Source

Real GDP y cpigdpln=γ World Bank

and IMF

Inflation Dp

−=

−

−

1

1

t

ttCPI

CPICPIDp IMF

Real export of goods and services x

=

tCPIxx ln WDI

Real import of goods and services m

=

tCPImm ln WDI

Real exchange rate ep

=

d

*t

pp

eep ln IMF

Oil price poil ( )oilpricepoil ln= OECD



TABLE 3A - Trade Weight

Period (1980Q1-1996Q4)

Country Africa China Euro US Africa 0.0000 0.012793 0.091852 0.029326 China 0.016944 0.00000 0.029275 0.038108 Euro 0.559896 0.207162 0.0000 0.181649 US 0.172002 0.228654 0.202271 0.000 Rest

Period (1997Q1-2012Q4)

Country Africa China Euro US Africa 0.0000 0.021469 0.092427 0.038761 China 0.087623 0.0000 0.130436 0.159716 Euro 0.429643 0.21549 0.0000 0.161093 US 0.199994 0.256891 0.179148 0.0000 Rest 0.28274 0.50615 0.597989 0.64043 Note: Trade weights are displayed in column by country. Rest: accumulates the remaining countries. Source: Direction of Trade Statistics IMF, 1990-1992 for period (1980Q1-1996Q4) and 2006-2008, for the period (1997Q1-2012Q4).



TABLE 4A - WS-ADF Unit Root Test Statistics for Domestic and Global Variables

Period (1980Q1-1996Q4)

Domestic variables

Variables Code Africa China Euro-area US Real GDP y -3.21 -3.50 -2.133 -2.92

∆y -2.92 -1.77 -2.038 -4.21 inflation Dp -4.78 -3.98 -0.776 -0.75

∆Dp -6.95 -5.06 -2.864 -10.66 Real exchange rate ep -2.05 -1.49 -1.73

∆ep -2.35 -3.14 -3.57 Real import m -3.67 -3.96 -2.25 -3.00

∆m -2.73 -3.26 -2.81 -3.34 Real export x -2.12 -3.63 -1.71 -2.57

∆x -6.51 -3.14 -2.62 -2.45 Foreign variables

Real GDP ys -2.59 -2.78 -3.38 -3.10 ∆ys -2.24 -2.40 -2.13 -2.65 inflation Dps -3.32 -4.79 -4.79 -4.79 ∆Dps -7.77 -6.95 -6.95 -6.95 Real exchange rate eps -1.33 -2.49 -2.46 -3.35 ∆eps -4.84 -5.32 -4.32 -4.22

Global variables

Poil poil -1.71 -1.71 -1.71 -1.71 ∆poil -5.36 -5.36 -5.36 -5.36

Period (1997Q1-2012Q4)

Domestic variables

Africa Euro China US Real GDP y -0.91 -2.08 -0.71 -0.91

∆y -2.11 -1.88 -2.72 -2.13 inflation Dp -5.54 -5.36 -2.62 -9.03

∆Dp -7.81 -9.15 -12.56 -7.41 Real exchange rate rer -2.18 -1.61 -2.04

∆rer -4.91 -3.30 -3.89 Real import m -2.11 -4.04 -2.82 -4.05

∆m -3.16 -3.72 -4.69 -5.99 Real export x -2.16 -3.67 -2.68 -2.74

∆x -3.82 -3.24 -4.32 -4.38 Real GDP ys -1.06 -1.45 -1.20 -1.63

∆ys -2.91 -3.27 -2.87 -3.22 inflation Dps -7.59 -8.00 -6.60 -6.38 ∆Dps -9.60 -8.83 -7.98 -7.98 Real exchange rate eps -0.92 -1.87 -1.59 -1.59 ∆eps -5.76 -3.16 -4.75 -6.21

Global variable

Poil poil -3.739 -3.739 -3.739 -3.739 ∆poil -4.760 -4.760 -4.760 -4.760

Note: WS-ADF test statistics are chosen by the modified AIC with 5% significant level. The 95% critical value of the WS-ADF statistics for regressions with trend is -3.24 and without trend is - 2.55.



TABLE 5A - VARX* Order and Co-Integrating Relationship in the Country Specific Models

Country Lag order of domestic variables Lag order of foreign variables Co-integrating

relations

Period (1980Q1-1996Q4)

Africa 2 1 2 China 1 1 2

Euro area 2 1 1 US 1 1 2

Period (1997Q1-2012Q4)

Africa 2 1 2 China 1 1 1 Euro area 2 1 1 US 2 1 2 Note: The rank of the cointegrating orders for each country/region is computed using Johansen’s trace statistics at the 95% critical value level.

TABLE 6A - Weak Exogeneity Tests of Country Specific Foreign and Global Variables

Country F-test Critical-value Country specific foreign and global variables

Real GDP inflation Real exchange

rate Oil prices

Period (1980Q1-1996Q4)

Africa F(2,48) 3.190727 1.408543 1.644644 0.98792 China F(2,53) 3.171626 1.469507 0.18599 1.170186 Euro F(1,49) 4.038393 1.995176 0.910853 0.11558 US F(2,54) 3.168246 0.041026 0.694813 1.605656

Period (1997Q1-2012Q4)

Africa F(2,44) 3.209278 1.001954 3.705879 2.507005 China F(1,50) 4.03431 3.306091 0.04841 0.096122 Euro F(1,45) 4.056612 0.144824 3.888188 2.693925 US F(2,45) 3.204317 1.376722 0.20522 0.165741 Note: the critical values are at the 5% level of significance.

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