Article: “Evaluation of the Effects of Regional Integration on the performance of
CEMAC member states”
Written by Amelle Sylvie TCHANA SENGUET
Engineer Statistician Economist
I.
Introduction
Faced with political and economic problems facing the African continent in general and
Central Africa in particular, regional integration must be a solution to any concern for
development. Broad and rather complex notion of regional integration can be defined as a
mechanism for developing countries, individually or collectively, it is a facilitator tool for
countries to allow Africa to become an industrial center and out of the shackles of chronic
current against imported products and provider of commodity dependence. Dealing with the
local processing of raw materials becomes a necessity. Viewed from this perspective,
regional integration facilitates the sharing of experiences to promote and support the
achievement of economies of scale for the rational exploitation of resources has our different
countries. It's a possibility that gives CEMAC countries to become a respectable, credible
and essential partner in the process of integration of world economies. Thus, regional
integration is a hope for the development of Africa. To this end how to measure training
effects of this regional integration on the economic performance of countries in the subregion? How to present the picture of the trade balance by country? It will be in this study
to examine the various possibilities offered by that regional integration for CEMAC
countries through a statistical description based on data from the World Bank.
An estimate of the effects of CEMAC in Cameroon's economic potential will also be done
through the estimation of imports based on gross national income and domestic and import
prices.
Study Objective: To measure the effect of regional integration (CEMAC) on the
performance of member countries.
Assumptions:
H1: Regional integration (CEMAC) promotes economic growth;
H2: The political, financial and economic crises in member countries negatively influence the
level of integration of the regional integration;
H3: The effects of integration in the CEMAC zone less influence the performance of member
countries.
Abstract
Confined in a narrow socio-economic space, African states have noted the need to go beyond
their national sovereignty by encouraging for most African Unity. In addition, much
remains to be carried out under political instability phenomena that hinder the economic
development of some countries. The challenge of development is an issue that arises in a
world where globalization is becoming increasingly urgent. Regional integration is the key
to that effect economic development issues that are facing our country. This idea in view of
the size of the CEMAC area would be set which, on the scale of a single state would have
been difficult or impossible to achieve. The countries of the CEMAC zone are facing a
multitude of issues: political stability, sustained economic growth, reduction in the volume of
imports, processing of natural resources, etc. One response to these concerns may reflect
numerous in regional integration as a main objective of development. In this study we
identified the effects of regional integration in the CEMAC zone in the specific performance
of Member States. For this purpose, the method of analysis adopted was the econometric
analysis of exports and imports outside CEMAC member countries. The results show that
the integration in the CEMAC zone did not significantly influenced the volume of imports /
exports of member countries. These results lead to fundamental questions about the
effectiveness of the integration between the member countries.
Keywords: Regional Integration, CEMAC, political stability, economic growth, natural
resource development.
II.
Regional integration in the CEMAC zone: the potential of countries in the
region
Difficult to know that Central Africa has an economy dominated by the political instability
and poverty all the more shocking when one recognizes in it many potential in terms of
natural resources. Regional integration to correct these negative factors to growth should
marry dimensions political, social and economic. Enhance economic efficiency and political
sub-region, improve the production process, bridging cultures and even change behaviors
become essential major challenges of regional integration should face. To do this there is the
potential of member countries.
1.
Performance of countries in the CEMAC zone
As CEMAC Economic and Monetary Community of Central Africa was signed on 16 March
1994. She replaces CACEU (the Customs and Economic Union of Central Africa), which
emerged in 1964. CEMAC is to: (i) to establish a union more among the peoples of Member
States to strengthen their geographical and human solidarity, (ii) promote national markets
by removing barriers to intra-Community trade, coordination of development programs, the
harmonization of industrial projects, and (iii) to develop solidarity of the member countries
for the benefit of disadvantaged countries and regions, (iv) to create a true African common
market. It includes six countries of Central Africa: Cameroon, Gabon, Congo, Central
African Republic, Equatorial Guinea and Chad.
The estimated effects of regional integration on the performance of the CEMAC are
centered in the first part of the analysis of some indicators of economic performance in the
CEMAC zone. It will conduct an analysis of: (i) the evolution of real GDP CEMAC trend
and cycle, and (ii) the level of the trade balance through exports / imports.
Analysis
data
are
drawn
from
the
database
of
the
World
Bank.
Analyses were made on the period 1980-2011 and for handling missing data for some
variables we used the method of imputation, imputation growth.
Gdpt +1 = (1 + a) GDPt with a growth rate of GDP between time t and t +1
The other missing for variables such as exports, imports data were imputed using the
average of two consecutive periods.
The variables used are:
 Real GDP in the CEMAC (PIB);
 the trade balance of the country i (Balance_i);
 exports of country i (export_i);
 imports of country i (import_i);
 Report
on
national
awards
import
prices
of
country
i
(Rpdpi_i).
a. Analysis of real GDP in the CEMAC
Figure 1: Evolution of GDP
trend and cycle
PIBin
réel_CEMAC
7.0E+09
6.0E+09
5.0E+09
4.0E+09
3.0E+08
3.0E+09
2.0E+08
2.0E+09
1.0E+08
0.0E+00
-1.0E+08
-2.0E+08
-3.0E+08
1980
1985
1990
PIB
1995
2000
Trend
2005
2010
Cycle
Source: World Bank and our calculations on Eviews
The actual production of the CEMAC had a sluggish growth of 3.5% on average during the
study period, many changes are observed; these marked by periods of growth (1980-1986)
and (1996 - 2011) and periods of decline (1987-1995). However, the study period can be
divided into three.
Period 1: 1980-1986
It is characterized by an average growth target of around 6.3% and represents a period of
strong economic growth. This growth is mainly due to good rainfall in the countries of the
area, which has favored the satisfactory results of Agriculture and a slight improvement in
terms of trade. It is also favored by the commodity boom over the period which induces a
shock to demand by improving revenue.
Period 2: from 1987 to 1995
During this period, the growth rate dropped to an average annual rate of about 1.1%. The
first part of this period saw the implementation of the stabilization program, while the
second part, beginning in 1990, marks the beginning of the implementation of Structural
Adjustment Programmes (SAPs) in the majority of countries of the CEMAC in general and
Cameroon in particular. The fall in the growth rate and promotes the devaluation of the
CFA franc in 1994.
Period 3: from 1996 to 2011
Actual production is growing at a relatively low rate averaged at 4.6%. This renewed
growth after the recession of 1987-1994 was to a lesser extent by the January 1994
devaluation but also NOT have improved the performance of CEMAC performance partly
due to favorable conditions development of private initiative. Growth for the period 20002011 is attributed to the implementation of structural reforms in several countries of the
CEMAC and also global growth.

Cyclical fluctuations of real GDP growth in the CEMAC as presented in Figure 1
suggest that the economic system of the area can be divided into three periods of cyclical
downturn: 1986-1994, 1998-2003 and 2005-2009 three periods of cyclical expansion: 19801996 and 1994-1998 and 2009-2011. In the last phase of cyclical expansion of 2009-2011 is
especially trend GDP progressing and deviations of real actual GDP around the trend GDP
are quantitatively less significant.
2.
The decomposition of real GDP CEMAC trend by using the method of
Hodrick-Prescott filter (HP)
The HP filter method is justified by the fact that:

It is easy to implement and summarizes all statistical methods;

the assumption of growth of real GDP series is realistic;

the phenomenon of edge effect can be overcome by extending the series to end with
the forecasts.
This method allows us to estimate the potential output of the CEMAC. The series of annual
real GDP is used and from 1980 to 2011. The smoothing parameter is λ = 100 since the data
are annual. Figure 1 shows the potential output of the estimated CEMAC. This estimate by
the HP filter confirms the subdivision made during the analysis of the actual production
effect three inflection points can be noticed at the yield curve in 1983, 1993 and 2003. The
general observation is that potential output does not follow the movements of the actual
production. But after 1997 the actual production is almost confused potential output.
The evolution of the GDP trend allows us to better assess the effects of integration into the
growth of the CEMAC countries. Since 1997 the curve of potential GDP is almost confused
with the actual production proof that integration in the CEMAC zone benefited the area to
reduce to zero the output gap (potential GDP = real GDP). However, the question that
arises is whether this growth is observed in the countries themselves, hence the need to
study the effects of the CEMAC countries' performance.
III.
Modeling: The effects of the creation of the CEMAC specific performance of
member
countries.
In this section we will estimate the import of CEMAC member countries based on gross
national income, domestic prices and imports to measure the effects of integration on the
performance of member countries and measure gap volume of imports in case.
In this context three countries will attract our attention, Gabon, Congo and Cameroon. It
should be noted here that in the context of this work is the integration confused with
membership in CEMAC.
a. Choices of individuals
The main question that arises is why choosing the Gabon, Congo and Cameroon?
The analysis restricted to these three countries is that previous studies have shown
statistically that the Cameroon and Congo are the main beneficiaries of the Community;
Gabon has also been added to the list because it has good performance presented in our
analysis of the evolution of the trade
balance in the post-CEMAC period.
It should be noted that the integration in CEMAC zone is favored by the intra-Community
trade. To this end the country since the establishment of CEMAC a better profile in terms of
the level of trade balance are those who could have benefited from better integration. In the
light of Figure 2, Chad shows a negative balance of trade until 2002, the CAR for its present
throughout the period ever negative trade balance against Equatorial Guinea whose trade
balance begins to be positive in 2000 not truly reflect the integration. At the opposite,
countries like Gabon, Congo and Cameroon lesser extent are the most integrated for since
the creation of the CEMAC have the best relationship in terms of trade balance, one might
think that integration has fostered the best performance.
Figure 2: Evolution of the trade balance in the countries of the CEMAC
BALANCE_TCHAD
BALANCE_GE
4.00E+08
2.40E+09
2.00E+09
0.00E+00
1.60E+09
-4.00E+08
1.20E+09
8.00E+08
-8.00E+08
4.00E+08
-1.20E+09
0.00E+00
-4.00E+08
-1.60E+09
-8.00E+08
-2.00E+09
1980
1985
1990
1995
2000
2005
2010
-1.20E+09
1980
1985
BALANCE_GABON
1990
1995
2000
2005
2010
BALANCE_CONGO
2.80E+09
1.60E+09
2.40E+09
1.20E+09
2.00E+09
1.60E+09
8.00E+08
1.20E+09
8.00E+08
4.00E+08
4.00E+08
0.00E+00
0.00E+00
-4.00E+08
-8.00E+08
1980
1985
1990
1995
2000
2005
2010
-4.00E+08
1980
1985
BALANCE_CMR
1990
1995
2000
2005
2010
BALANCE_CENTRAF
1.20E+09
0.00E+00
8.00E+08
-2.00E+07
-4.00E+07
4.00E+08
-6.00E+07
0.00E+00
-8.00E+07
-4.00E+08
-1.00E+08
-8.00E+08
-1.20E+08
-1.20E+09
-1.60E+09
1980
-1.40E+08
1985
1990
1995
2000
2005
2010
-1.60E+08
1980
Source: World Bank and our calculations on Eviews
1985
1990
1995
2000
2005
2010
In light of this statistical analysis and evidence gathered in the literature, our econometric
analysis is limited to these three countries.
b. Empirical analysis of the estimation method
The different methods used to measure the effects of an economic union in the literature are
based on the Balassa (1967). The method of Balassa (1967) is centered on the comparison of
income elasticity of imports between the periods pre-integration and post-integration of
these three countries. To this end, he said all things being equal the creation net effect is
accompanied by an increase in the income elasticity of total import demand from member
countries while the effect of deviation results in a decrease of the income elasticity of demand
for imports from non-member countries. Balassa calculated elasticity for the period are preintegration, taking care to separate imports from member countries and non-member
countries. It then uses the total imports to measure the net effect of the creation of the
Community. Indeed, it assumes that the lack of integration income elasticity is unchanged.
Thus, income elasticity of demand for total imports during the higher than pre integration
post-integration period indicates an effect of trade creation.
Box 1: The model BALASSA (1967)
Balassa uses the following formula:
Eit =
∂ ln Mi
∂ ln Yi
(a)
with
 Eit is the income elasticity of import demand total country i in year t,
 Half the total imports of country i

Yi, the gross national product of country i.
a
Assuming Eit
is the income elasticity of total real imports in the period before
p
integration and Eit the income elasticity of total imports of the post-integration period
p
a
then Eit >Eit
it has indicated that there is an effect of net job creation.
Balassa method has been criticized on at least one point.
It can not isolate phenomena such as growth or inflation on the income elasticity
(Dayal R. and N. Dayal, 1977). Indeed, if for example the rate of growth of gross
domestic product is higher than the pre-integration period postintégration in the
period, while that of imports remained stable, the value of the income elasticity is also
higher in the postintégration period compared to the pre-integration period without
this being due to the creation of the economic union. The same phenomenon occurs in
periods of inflation. Indeed, the increase in the elasticity of an increase in import
demand is due to the additional income caused by lower prices. However, if at the
time the rates are reduced inflation increases by the same amount of products, we can
not in these circumstances given the increase in elasticity (if in place) to the effect of
lower rates (common market effect), it is already canceled by the effects of inflation.
The model for the econometric estimation of import demand based on the assumption that
the creation of a customs union causes a structural shock to the flow of imports. So the
method is to test this hypothesis directly. Variables affecting imports are first determined
econometrically for pre integration period, to estimate imports during this period as well as
imports during the post-integration assuming the relationship found is the same for both
periods. Comparing these values with the observed values of imports, the differences are
attributed to the formation of the customs union.
The creation effect is given by the sum of the positive difference between total current
imports (and non-members from member countries) and estimated during the postintegration period imports. The diversion effect is the negative sum of the difference
between imports from non-member countries and the estimated imports. Kreinin is one of
the authors who developed
this method. The equation to use is as follows:
Pmi
ln(Mi ) = a + blnYi + cln(
)
(b)
P𝑛 𝑖
Where: a is the constant, b is the income elasticity
country i, c the price elasticity of country i, Mi the total imports of country i and Pmi, Pni, price
indices for imports and domestic country i. By the annual summation of effects creation and
diversion from 1962 to 1965 the countries of the Community, Kreinin also a net trade creation.
It is important to note that the parameters are based on data from the pre-integration and
therefore, on a limited set of information even within the selected range. Imports estimated the
post-integration period can lead to results similar to those discussed for the Balassa method bias.
Finally, the import demand function as a predictor the Pmi / Pni ratio.
Equation (b) can be written as follows:
ln(Mi ) = a + blnYi + cln(Pmi ) − cln(Pni )
(c)
Following this empirical review, we estimate the effects of integration into the performance
of the CEMAC. However, some changes will be made to test the significance of the model.
c.
Modeling
We base year 1994, the year of creation of the CEMAC. Considering the period 1980-1994
as
before-integration
CEMAC
and
1995-2011
post-integration
CEMAC.
Formulation:
For even though i, country j can export only when national wealth that has allows him to
buy the property and the purchase of this property i is based on the cost of i as follows:
Mij = f ( Pmij) + g (Rnj) (1) with Mij the import demand of good i in country j, Pmi, the
import price of good i in country j and Rni, the national income of country j.
Similarly, it must first meet its domestic consumption is based on the domestic price of good
i before importing, where: Mij = h (PNij) (2) with PNij national price of good i in country j .
Both equations (1) and (2) give us:
Mt=a1+a2Rnt+a3Pmt+a4Pnt+ut
(3)
Thus, the demand for imported products outside the CEMAC is explained by the Gross
National Product and import prices and domestic as specified in equation (3).
If the creation of the CEMAC has changed this request, the change will be captured by a
binary
variable,
according
to
the
following
equation:
Mt=a1+a2Xt+a3Rnt+a4Pmt+a5Pnt+a6XtRnt+a7PtPmt+a8XtPnt+et (4)
où
0 si i=1980-1994
𝑥𝑖 =
1 sinon
With introduction of the variable Xi in equation (4), the sum of squares of the errors will be
always less than or equal to that of equation (4). If UDEAC / CEMAC had not been created,
or that its creation has been no economically significant effect, equation (4) produce for the
period 1960-2005 a sum of squared errors approximately equal to that of equation (4) and
Fisher's exact test reveal that the introduction of the binary variable adds nothing more.
IV.
Estimation
1.
Analysis of the stationarity
The application of the methodology Balassa requires the use of stationary variables.
Stationarity tests were performed on the variables that lead to the conclusion that they are
all stationary in level or first difference. Two tests were used: Augmented Dickey-Fuller
(ADF) and Phillips-Perron (PP).
The
following
table
provides
the
results
of
the
various
tests,
(*) Means the rejection of the null hypothesis. For ADF and PP tests are tests for the
presence of unit root. The results of the various tests indicate that apart from Lrdpi_Cmr
lrdpi_Congo and the other series are not stationary level at 5%. Indeed, for lImport and Lpib
variables Lrdpi_Gabon the ADF and PP statistics are greater than the critical values at 5%.
In other words these tests do not reject the hypothesis of the presence of unit root. By cons
tests on differentiated series lead to different matching: for all tests the null hypothesis is
rejected. The hypothesis of non-stationarity in the first difference can be rejected at 5%.
Therefore, the study variables are assumed stationary in difference. The stationarity test of
Phillips-Perron is consistent with the results obtained.
Table 1: Results of the stationarity tests
Variables
Title
Model used
LIMPORT
Model selected
PP
ADF
-2,414
-7,386*
-16,194*
(-3,581)
(-3,568)
(-2,972)
With Constant
-1,899
-1,899
With Constant
-6,322*
-6,322*
and Trend
(-3,563)
(-3,563)
without Trend
(-2,964)
(-2,964)
-2,178
-2,178
-6,189*
-6,189*
(-3,563)
(-3,563)
(-2,964)
(-2,964)
-2,936
-1,358
-3,868*
-3,258*
(-3,622)
(-2,963)
LIMPORT_CONGO
With Constant
and Trend for ADF
PP
and with constant
LPIB_CMR
(-3,581)
(-3,563)
without trend for
With Constant
LPIB
First difference
ADF
LIMPORT_CMR
LIMPORT_GABON
A level
and Trend
PP
-2,747
-2,747
With Constant
-3,86*
-5,713*
(-3,563)
(-3,563)
without Trend
(-2,964)
(-2,964)
-2,38
-2,38
With Constant
-3,86*
-3,86*
(-3,563)
(-3,563)
without Trend
(-2,964)
(-2,964)
-5,175*
-2,021*
(-3,612)
(-1,952)
With Constant
-1,484
-1,68
With
and Trend
(-3,588)
(-3,563)
without Trend
With Constant
-6,873*
7,069*
and Trend
(-2,964)
(-2,964)
LPIB_GABON
LPIB_CONGO
With Constant
and Trend for
LRDPI_CMR
ADF and
without
Constant and
LRDPI
trend for PP
LRDPI_GABON
LRDPI_CONGO
Source: World Bank and our calculations on Eviews
Constant -7,821*
(-2,964)
-8,051*
(-2,964)
2.
Analysis of cointegration with the endogenous variable lImport
The variables of our model are I(1), it should test for cointegration them. If the
cointegration test indicates a long-term relationship between the variables, the methodology
can be improved by estimating a model error that reflects the stationary long-run
relationship between the variables correct. The multivariate approach of Engel and Granger
was used for this purpose. Had to be estimated by OLS (ordinary least squares) the longterm relationship between two sets and after recovering residues and apply the unit root
test. The test results are summarized in the table below, the residuals are I (0) while the
variables are mostly integrated of order 1. The results indicate the presence of a
cointegrating relationship between the long-term log of imports, the logarithm of GDP and
the price ratio for all three countries.
Table 2: Presentation of the unit root test on the residuals by country estimated by
OLS
Residues
Model used
Resid_Cmr
Resid_Congo
A level
PP
Statistics
-2,818
(-1,953)
sans constante ni
-2,768
tendance
(-1,952)
Resid_Gabon
ADF
-3,208
(-1,952)
Conclusion
I(0)
I(0)
I(0)
Source: World Bank and our calculations
The results indicate the presence of a cointegrating relationship between the long-term log
of imports, the logarithm of GDP and the logarithm of the ratio of prices. The relationship is
expressed as follows for each country:
limport_cmr = 1,941510797*lpib_cmr – 0,06343018352*lrpdpi_cmr – 23,13950712+
ut ;
limport_congo
=
2,417332237*lpib_congo
+
0,2205276771*lrpdpi_congo
–
=
0,1887918638*lpib_gabon
–
0,5024983652*lrpdpi_gabon
+
31,86401766+ vt;
limport_gabon
17,07933629+ zt.
with ut, vt and zt stationary series.
This long-term relationship assumes that the GDP has a positive long-term impact on the
volume of imports required by a country this is explained by the fact that when the wealth of
a country increases its demand for imported goods increases. However, the model estimation
error correction shows the irrelevance of this long-term relationship because the estimated
coefficients are either insignificant (the t-stat is less than 1.96 in absolute value) or negative
(long term variable does not tend to the estimated long-term relationship).
The () indicate standard deviations, and [ ] the Student t.
Table 3: Coefficients of the long-term relationship of the structural model
Variables
CointEq1
CointEq1
CointEq1
D(LIMPORT_GABON) D(LPIB_GABON) D(LRPDPI_GABON)
-0.136827
-0.040915
-0.171319
(0.09807)
(0.04016)
(0.13464)
[-1.39525]
[-1.01876]
[-1.27240]
D(LIMPORT_CMR)
D(LPIB_CMR)
D(LRPDPI_CMR)
-0.470001
-0.042611
1.440306
(0.13210)
(0.05779)
(0.24168)
[-3.55799]*
[-0.73737]
[ 5.95963]*
D(LIMPORT_CONGO)
D(LPIB_CONGO)
D(LRPDPI_CONGO)
-0.428002
-0.002491
0.253168
(0.11402)
(0.02289)
(0.15988)
[-3.75375]*
[-0.10881]
[ 1.58353]
Source: World Bank and our calculations on Eviews
(*) Indicates coefficients significant at the 5% level with absolute value of the t-stat ˃ 1.96.
3.
Estimated relationships long and short term
a. Selecting the optimal number of delay estimation
For choosing the optimal number of delay, we use the information criterion, three of the six
criteria give p * = 1 as the optimal delay for each country, we can use the delay p * = 1 as
the optimal delay Following our modeling.
Table 4: Determination of Optimal delay
Countries
Lag
LogL
LR
FPE
AIC
SC
HQ
CMR
1
113,98
116,53*
1,78e-07*
-7,03*
-6,46
-6,86
2
142,96
43,96
4,60e-08
-8,41
-7,42
-8,10
3
160,24
22,65
2,75e-08
-8,98
-7,57*
-8,54*
1
55,13
116,36*
8,28e-06*
-3,20
-2,61*
-3,02*
2
61,78
9,85
1,01e-05
-3,02
-2,01
-2,72
3
71,26
11,94
1,05e-05
-3,06
-1,62
-2,63
1
84,09
88,47*
9,69e-07*
-5,34*
-4,76*
-5,16*
2
87,99
5,77
1,46e-06
-4,96
-3,95
-4,66
3
93,55
6,99
2,02e-06
-4,71
-3,27
-4,28
CONGO
GABON
Source: World Bank and our calculations on Eviews
* Optimal model according to the criterion
LR: sequential modified LR test statistic
FPE: Final prediction error
AIC: Akaike information criterion
SC: Schwarz information criterion
HQ: Hannan-Quinn information criterion
4.
Estimation of the model error-correction
The estimated model is similar to Equation 3, and is specified as follows:
ln(M)=c0+c1,ln(PIB)t+c2,ln(Rpdpi)t+c3,ln(M)t-1+c4,ln(PIB)t-1+c5,ln(Rpdpi)t-1+ ut
(5)
Given that should capture the effect of the creation of the CEMAC business performance of
the three countries, we will introduce a binary variable X in equation (5) as specified in
equation 4, defined as follows:
ln(M)=c0+c1ln(PIB)t+c2ln(Rpdpi)t+c3ln(M)t-1+c4ln(PIB)t-1+c5ln(Rpdpi)t-1+
ut+c6Xt+c7Xtln(PIB)t+c8Xtln(Rpdpi)t+c9Xt-1ln(M)t-1+c10Xt-1ln(PIB)t-1+c11Xt-1ln(Rpdpi)t-1+vt
(6)
It should be noted that when i = 1980-1994, the model with binary variable is similar to
equation (5) as for that Xi = 0.
a.
Analysis results
The results given in the following table are obtained after estimating the model (6) where X
embodies the binary variable introduced in the model to capture the effect of the creation of
the CEMAC trade performance of individual countries.
Table 5: Results of the estimation of equation (6)
Variables
Congo
Gabon
Cameroon
Constante
18,125(1,38)
3,636(0,593)
-2,343 (-0,41)
lPIB
0,555(0,479)
1,052(3,209)*
0,955(2,226)*
LRPDPI
-0,182(-1,317)
-0,276(-2,542)*
-0,296(-2,922)*
X
-22,79(-1,475)
-30,645(-4,616)*
14,67(-2,204)*
LIMPORT(-1)
0,355(1,937)*
0,444(2,529)*
-0,011(-0,046)
LPIB(-1)
-0,784(-1,059)
-0,685(-2,061)*
0,081(0,201)
LRPDPI(-1)
0,321(2,287)*
-0,143(-0,965)
0,023(0,242)
R2
0,95
0,93
0,99
R ajusted
0,92
0,89
0,98
Durbin-Watson
2,15
2,61
2,03
2
Source: World Bank and our calculations on Eviews
(*) Indicates coefficients significant at the 5% level, the values in brackets mark the Student
statistic.
Result1: statistics R2, adjusted R2 and Durbin-Watson are good against by the t-stat
affected in Congo are not always significant in contrast to those of Gabon and Cameroon,
which have better results variables.
Results2 The binary variable is significant for the Cameroon and Congo, this constant leads
us to believe that the creation of the CEMAC has had a significant effect for both countries.
To better measure the impact of the creation of the CEMAC performance of these countries
we will evaluate the GAP import demand as measured by the difference between the
estimated imports taking into account the effect of creating the CEMAC that you specified
in the equation -6) and the demand for real imports observed in these countries.
The Gap import is measured by the following equation:
GapLimport_i = limport_ei - limport_i
(7)
limport_ei with the logarithm of the estimated effect of the country with CEMAC i imports.
It should be noted that Gap is performed when the CEMAC actually takes effect that is to
say in 1995.
The results are shown in the following table:
Table 6: Identification of GAP with CEMAC imports estimated effect and real
imports
Periods
Gaplimport_Congo Gaplimport_Gabon Gaplimport_Cmr
1995
0,014
0,010
0,005
1996
0,024
0,030
0,023
1997
-0,029
0,010
0,004
1998
0,002
-0,005
-0,054
1999
-0,071
-0,037
-0,014
2000
0,013
-0,026
0,003
2001
0,000
0,014
-0,047
2002
0,017
-0,053
0,016
2003
-0,008
-0,001
0,030
2004
0,042
-0,006
-0,020
2005
0,038
0,063
0,019
2006
-0,003
-0,009
0,052
2007
-0,057
0,013
0,033
2008
-0,009
0,017
-0,064
2009
0,039
-0,066
0,012
2010
0,007
0,051
0,017
2011
-0,018
-0,005
-0,015
Source: World Bank and our calculations on Eviews
We find that the influence of the CEMAC is reflected in place however is marginal on
imports from member countries from non-member countries. This influence is not
significant leads us to ask relevant about the actual effectiveness of this integration issues, in
other words, the member countries of CEMAC are they really in the integration process?
The reasons for these results are applied to bind to certain behaviors disseminated by some
countries that do not make effective integration in the CEMAC zone, the plurality of
creations integration process in Africa as of which those countries can also be a cause these
results. Should we rethink the CEMAC not really benefit?
V.
GENERAL CONCLUSION
After this analysis, it is natural to realize that the process of economic integration in the
CEMAC zone barely cross the course of the perfect realization. Analysis of the effects of the
creation of the CEMAC trade performance of individual Member States amounts to prove
the ineffectiveness of the integration process in the CEMAC zone. The selfishness of the
CEMAC member states disadvantage. A legal framework that can provide these institutions
and independent bodies, structuring and functionality needed for research and protection of
"common interest" is essential as a result redeployment of human resources.
VI.
NOTES
1. Stationarity test results
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-1.985300
0.5838
Test critical values:
1% level
-4.323979
5% level
-3.580623
10% level
-3.225334
Variable
Coefficient
Std. Error
t-Statistic
Prob.
LIMPORT_CMR(-1)
-0.198861
0.100167
-1.985300
0.0597
D(LIMPORT_CMR(-1))
0.481315
0.184017
2.615605
0.0158
D(LIMPORT_CMR(-2))
-0.246094
0.180184
-1.365788
0.1858
D(LIMPORT_CMR(-3))
0.424516
0.152015
2.792593
0.0106
C
4.106831
2.079616
1.974803
0.0610
@TREND(1980)
0.009435
0.004113
2.293640
0.0317
R-squared
0.412407
Mean dependent var
0.045452
Adjusted R-squared
0.278863
S.D. dependent var
0.113838
S.E. of regression
0.096671
Akaike info criterion
-1.647602
Sum squared resid
0.205595
Schwarz criterion
-1.362129
Log likelihood
29.06642
F-statistic
3.088180
Durbin-Watson stat
2.071547
Prob(F-statistic)
0.029226
The stationarity test Augmented’s Dickey-Fuller performed on the log of imports shows
that
it
is
not
stationary
level
at
5%
with
constant
and
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-4.241350
0.0033
Test critical values:
1% level
-3.752946
5% level
-2.998064
10% level
-2.638752
Variable
Coefficient
Std. Error
t-Statistic
Prob.
D(LIMPORT_CMR(-1))
-1.088831
0.256718
-4.241350
0.0008
D(LIMPORT_CMR(-1),2)
0.338343
0.239452
1.412990
0.1795
D(LIMPORT_CMR(-2),2)
0.046366
0.207144
0.223834
0.8261
D(LIMPORT_CMR(-3),2)
0.472186
0.203154
2.324279
0.0357
D(LIMPORT_CMR(-4),2)
0.420675
0.175252
2.400400
0.0308
D(LIMPORT_CMR(-5),2)
0.558933
0.171816
3.253087
0.0058
D(LIMPORT_CMR(-6),2)
0.261220
0.123808
2.109876
0.0533
D(LIMPORT_CMR(-7),2)
0.524969
0.098123
5.350104
0.0001
C
0.053698
0.014502
3.702879
0.0024
R-squared
0.879893
Mean dependent var
0.015976
Adjusted R-squared
0.811261
S.D. dependent var
0.127848
S.E. of regression
0.055542
Akaike info criterion
-2.657168
Sum squared resid
0.043189
Schwarz criterion
-2.212845
Log likelihood
39.55744
F-statistic
12.82035
Durbin-Watson stat
1.658236
Prob(F-statistic)
0.000031
trend.
The stationarity test Augmented 's Dickey-Fuller performed on the log of imports shows
that it is stationary difference at 5% with no consistent trend.
2. Analysis of cointegration
t-Statistic
Prob.*
Augmented Dickey-Fuller test statistic
-2.127755
0.0342
Test critical values:
1% level
-2.650145
5% level
-1.953381
10% level
-1.609798
Variable
Coefficient Std. Error
t-Statistic
Prob.
RESID02(-1)
-0.706420
0.332002
-2.127755
0.0434
D(RESID02(-1))
-0.209717
0.221108
-0.948484
0.3520
D(RESID02(-2))
-0.546707
0.144681
-3.778720
0.0009
R-squared
0.822217
Mean dependent var
0.004825
Adjusted R-squared
0.807994
S.D. dependent var
0.096655
S.E. of regression
0.042353
Akaike info criterion
-3.384606
Sum squared resid
0.044844
Schwarz criterion
-3.241870
Log likelihood
50.38449
Durbin-Watson stat
1.987806
The residue obtained after OLS estimates (OLS) of the long-term relationship between two
sets and after recovering residues and apply the unit root test, we find that of Cameroon is I
(0) this result indicates the presence of a cointegrating relationship between the long-term
log of imports, the logarithm of GDP and the logarithm of the ratio of prices.
3. Results of the model estimation error correction (equation (6)) applied to the
three countries
LIMPORT_ECMR=-2.342670443+0.95457676*LPIB_CMR0.2955719661*LRPDPI_CMR-0.01127417898*LIMPORT_CMR(1)+0.08068716184*LPIB_CMR(-1)+0.02315334738*LRPDPI_CMR(-1)14.67070374*X+0.3373479578*X*LIMPORT_CMR+0.3373479578*X*LPIB_CMR+0.2313
01641*X*LRPDPI_CMR+
0.258071995*X(-1)*LIMPORT_CMR(-1)-0.2410726095*X(-
1)*LPIB_CMR(-1)+0.2754381485*X(-1)*LRPDPI_CMR(-1)
LIMPORT_ECONGO=18.12530313+0.5553665132*LPIB_CONGO0.1822628926*LRPDPI_CONGO+0.3552727794*LIMPORT_CONGO(-1)0.7844338195*LPIB_CONGO(-1)+0.3211905166*LRPDPI_CONGO(-1)22.79345817*X+0.5545534271*X*LIMPORT_CONGO+0.5545534271*X*LPIB_CONGO+
0.1537589311*X*LRPDPI_CONGO-
0.2448707053*X(-1)*LIMPORT_CONGO(-
1)+0.2087829883*X(-1)*LPIB_CONGO(-1)-0.330974725*X(-1)*LRPDPI_CONGO(-1)
LIMPORT_EGABON=3.636225052+1.052109258*LPIB_GABON0.2755445826*LRPDPI_GABON+0.4437788172*LIMPORT_GABON(-1)0.6845410293*LPIB_GABON(-1)-0.1431399911*LRPDPI_GABON(-1)30.6453577*X+0.6988545553*X*LIMPORT_GABON+0.6988545553*X*LPIB_GABON+0
.6113345611*X*LRPDPI_GABON-
0.5098727281*X(-1)*LIMPORT_GABON(-
1)+0.4919515303*X(-1)*LPIB_GABON(-1)+0.1028702302*X(-1)*LRPDPI_GABON(-1)
These results show that all three countries the logarithm of the volume of imports is an
increasing function of the logarithm of GDP and decreasing the ratio of prices. It remains to
see the statistics of student test to see if these coefficients are significant or not.
4. Study of the Significance of the coefficients
LIMPORT_CMR=C(1)+C(2)*LPIB_CMR+C(3)*LRPDPI_CMR+C(4)
*LIMPORT_CMR(-1)+C(5)*LPIB_CMR(-1)+C(6)*LRPDPI_CMR(
-1)+C(7)*X+C(8)*X*LIMPORT_CMR+C(8)*X*LPIB_CMR+C(9)*X
*LRPDPI_CMR+ C(10)*X(-1)*LIMPORT_CMR(-1)+C(11)*X(-1)
*LPIB_CMR(-1)+C(12)*X(-1)*LRPDPI_CMR(-1)
Coefficient
Std. Error
t-Statistic
Prob.
C(1)
-2.342670
5.668539
-0.413276
0.6840
C(2)
0.954577
0.428895
2.225666
0.0383
C(3)
-0.295572
0.101141
-2.922381
0.0087
C(4)
-0.011274
0.245028
-0.046012
0.9638
C(5)
0.080687
0.400550
0.201441
0.8425
C(6)
0.023153
0.095544
0.242331
0.8111
C(7)
-14.67070
6.655031
-2.204453
0.0400
C(8)
0.337348
0.151798
2.222351
0.0386
C(9)
0.231302
0.202895
1.140005
0.2685
C(10)
0.258072
0.404429
0.638115
0.5310
C(11)
-0.241073
0.373720
-0.645062
0.5266
C(12)
0.275438
0.198949
1.384468
0.1823
R-squared
0.987022
Mean dependent var
21.41828
Adjusted R-squared 0.979508
S.D. dependent var
0.400991
S.E. of regression
0.057401
Akaike info criterion
-2.592854
Sum squared resid
0.062603
Schwarz criterion
-2.037762
Log likelihood
52.18923
Durbin-Watson stat
2.028836
Constants assigned to the logarithms of GDP and price ratio are significant at the 5% level
for Cameroon.
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