EXPLORING TRADE LINKAGES AMONGST AFRICAN ECONOMIES:

EVIDENCE FROM A GLOBAL VECTOR AUTOREGRESSIVE

(GVAR) ANALYSIS

E.C Kinfack and Dr A. Pholo

2nd International Conference on Sustainable Development in Africa

26-27 november 20015, Dakar

OUTLINE OF THE STUDY

Motivation

Objective(s) and contribution

Methodology

Results and interpretation

Conclusion

Motivation African countries considered regional integration as a key strategy in order to

improve intra-African trade and to stimulate economic growth.

Despite the large number of RECs in Africa the region still trade heavily with countries outside the continent (ECA, 2012).

The trade linkage is an important feature of any economic integration.

The synchronisation of business cycles among members within a REC is considered to be an important criterion for countries to form a successful union and trade linkage is an important channel of transmission for such synchronisation (Marcus, 2010).

Intra-regional trade, together with geographically diversified trade linkages, can strengthen the capacity of African countries to absorb global shocks (Ncube et al, 2014).

Objective(s) and contribution

Main objective • To explore the trade linkages among members within some selected RECs in Africa

Contribution •First study to explore the trade linkages in Africa•Among the few studies that implemented the GVAR models in Africa and perhaps the first that actually focuses on RECs in Africa.

Methodology

GVAR was proposed by Pesaran et al (2004), and developed by Dees et al (2007)The importance of GVAR:

1.The model is suitable for large data sample. GVAR was developed to handle large-scale variables and quantify global interdependencies that exist between countries.

2.The Global Impulse Response Functions (GIRFs) obtained from the GVAR model are invariant to the order of the variables (Pesaran et al. 2004; Cesa-Bianchi et al. 2011).

3.The model has the ability to combine individual country- specific model into a global framework and allows for the analysis of the interaction amongst them while avoiding any dimensionality problems.

Methodology cont…..The model consist of three main steps:

1.each country is modelled individually.

2. Building the GVAR modelFrom equation (1), if we assume

Each country i is modelled as a VARX specification of the form:

)1(1,101,10 xxxtx ittiiititiiiiit

.....T , ........, , = with t 210

itx are 1ik and*itx 1*

ik domestic variables and foreign variables for each country at time .

io and 1i 1ik fixed intercept coefficient and coefficients of the deterministic time trend respectively

i is a ii kk matrix of coefficient associated with lagged domestic variables.

0i and 1i are *ii kk matrices of coefficients related to foreign and lagged foreign variables respectively.

it is a 1ik vector of shocks specific to each country, which iiit 0, dii ... .

*, ititit xxH

Methodology cont…..(1) 21,110 ttA ittiiiiiiti

Where 0ikii IA , and 1, iii are *iii kkk

We can now write country-specific variables in terms of the global variable vector tx , to obtain

the following identity:

tiit xM 3 .........N 3, 2, 1, 0,i

iM is a iii kkk * matrix of weights 0,1,2....Nji, wij .

(2) 41,10 xMtxMA ittiiiiitii

Where iiMA and ii M are both kk dimensional matrices.

5110 xtKx ttt

611

11

10

1 KxKtKKx itt

Thus, equation (6) can also be written as follows:

7110 Txtbbx ttt

With 01

0 Kb , 11

1 Kb , 1KT and itt K 1

Methodology cont...

3. Estimation of the model, data and data sources

  Variables Short Name Formula Source Real GDP y

)ln(cpi

gdpy

World Bank and IMF

Inflation Dp

1

1

t

tt

CPI

CPICPIDp

IMF

Real exchange rate ep

d

t

p

peep

*

ln

IMF

Short term Interest rate

r

1001

4

1 Rr ln

IMF

Real export of goods and services

x

tCPI

xx ln

WDI

Real import of goods and services

m

tCPI

mm ln

WDI

Oil price poil price oilpoil ln OECD

GIRF’s results of positive shock on CEMAC trade’s variables

Export shock from Cameroon Import shock from Cameroon

Export shock from Gabon Import shock from Gabon

GIRF’s results of positive shock on CEMAC trade’s variables

Export shock from Equatorial guinea

Import shock from Equatorial guinea

Export shock from Congo Import shock from CAR

GIRF’s results of positive shock on EAC’s trade variables

Import shock from Kenya Export shock from Rwanda

Export shock from Tanzania

GIRF’s results of positive shock on SADC’s trade variables

Export shock from Botswana

GIRF’s results of positive shock on SADC’s trade variables

Import shock from Lesotho Export shock from Mozambique

GIRF’s results of positive shock on SADC’s trade variables

Export shock from Namibia Import shock from South Africa

GIRF’s results of positive shock on SADC’s trade variables

Export shock from Zimbabwe

Conclusion There is evidence of trade linkages amongst members within RECs in Africa and the magnitude varies from one country to another and from one region to another.

There are very few countries that share bilateral trade with RECs in Africa. Trade relationships are mostly unilateral in Africa

Based on The GIRFs, trade linkages are still low within RECs in Africa

African countries should diversified their trade.

Thank you