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TRADE ORIENTATION AND ECONOMIC GROWTH OF CEMAC COUNTRIES:
OPPORTUNITIES FROM THE CHINESE MARKET
Diadié Diaw
Centre for Analysis and Economic Research
Globalisation and Regulations Team
University of Rouen.
3, avenue Pasteur 76186 Rouen Cedex 1.
Tel: +33 2 32 76 96 64
Fax : +33 2 32 76 96 63
Email: [email protected]
and
Albert Lessoua
Ecole Supérieure du Commerce Extérieur
PU Léonard de Vinci, 92916 Paris-La Défense
Tel: +33 1 41 16 76 66
Email : [email protected]
Abstract
This paper deals with the dynamics of growth in the countries of the Economic and
Monetary Community of Central Africa (CEMAC), focusing on external trade, in
particular with China. Its aim is to shed light on the increasing influence of China in
Africa. It uses dynamic panel estimations to measure the impact of trade orientation on
economic growth in the CEMAC countries and concludes that specialization in natural
resources affects negatively economic growth. But this effect is somewhat mitigated by the
orientation towards China. Moreover, the weak interregional trade between CEMAC
countries has failed to contribute to their economic growth.
Keywords: Regional integration, Natural resources, International trade, CEMAC
JEL Classification: F, O, Q
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1. Introduction
Since the early 1990s, there has been an increasing trend towards economic regionalism
with the aim of increasing international and regional trade. In 2011 approximately 490
trade agreements were notified to the WTO, 200 of which, dealing with goods and
services, are now in force. Globalization has accelerated regional integration as a
necessary and major driver in the development of emerging economies. According to
Balassa (1961), regional economic integration can be seen as both a process and a state:
a process because it works to eliminate all forms of economic discrimination among the
countries’ economic units and a state because it is defined as the absence of economic
discrimination among national economies.
Regional integration has played an important role in Africa; since the 60s, African
countries have been eager to enter into economic agreements. In central Africa, this
process began in 1962, when the treaty establishing the Customs and Economic Union of
Central Africa (UDEAC) was signed in Brazzaville; it would go into effect in 1966. This
union developed as a force for market integration and in 1994 became the Economic and
Monetary Community of Central Africa (CEMAC). Its six member states are Cameroon
(Yaoundé), Central African Republic (Bangui), Chad (N’Djamena), Equatorial Guinea
(Malabo), Republic of Congo (Brazzaville), and Gabon (Libreville).
The goal of this union is to facilitate the economic integration begun with the UDEAC
and to increase regional and international trade. The group’s main goals are as follows:

Harmonizing member countries’ economic policy and legal environment;

Seeking ways to create an effective common market;

Creating mechanisms to prevent, manage, and resolve regional conflict.
Overall, the CEMAC’s primary goal is to develop harmonious relations among its
member states by strengthening economic and trade ties.
While these are praiseworthy goals, many studies of the effects of trade agreements
among developing countries have not been able to discern any positive effects (Cadot, De
Melo & Olarreaga, 2000; Longo & Sekkat, 2004; Mayda & Steinberg, 2006; Schiff, 1997;
Subramia & Tamirisa, 2001; World Bank, 2000; Yeats, 1998). These results have led
scholars to question whether developing countries are in fact inclined to do business with
each other.
However, the countries of the CEMAC, like other developing countries, have recently
expanded their trade with emerging countries, especially China. Now that this rising
economic power has opened its markets, China has managed to strengthen its economics,
business, and diplomatic ties with the rest of the world. It is widely believed that China
has initiated trade relations with African countries primarily to satisfy its need for
natural resources (OECD, 2006; UNCTAD, 2005). Certainly China needs these trade
relations to maintain consistent access to natural resources, and the countries of the
CEMAC are particularly attractive partners because of the vast reserves of energy
resources that make up a major share of their exports. This new situation raises a major
question about trade relations among the CEMAC member countries and the economic
growth they may enjoy due to their specialization in natural resources: will trade with
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China create sustainable growth in these countries, or will it have a long-term negative
effect because it promotes and further preserves specialization in natural resources?
To answer this question, we study the growth dynamics of the CEMAC countries in
relation to their trade orientation, their specialization, and the inflow of capital in the
area. To this end, we use panel data estimation methods (both static and dynamic),
which allow us to better understand the relations among the studied variables and
which provide us with better results, particularly the dynamic one.
Thus, our work has proved the interest for CEMAC countries to better regulate their
specialization patterns in order to avoid the effects of a “Dutch disease”. This work has
also established interest for these countries to develop partnerships with other
developing countries, in particular with China. Indeed, South-South partnerships could
be interesting sources of capital and of market opportunities.
The rest of the paper is organized as follows: in section 2 we will describe the economic
situation and the makeup of the trade flow of the CEMAC countries. In section 3 we will
present our dynamic model and empirical results. Section 4 concludes with economic
policy recommendations for the CEMAC countries.
2. Growth and trade structure in the CEMAC area
a. Dynamics of economic growth
For decades the economy of the CEMAC area has been based primarily on natural
resources with very little diversification. In fact, fuel has been the fastest-growing sector
of the economy. Almost all of the region’s exports fall into three categories: petroleum,
timber, and agricultural products; the revenues of these countries are therefore based on
these sectors. Nevertheless, the economy of the region has been growing since the 1990s:
from 1995-2004, it grew by 4.3%; in 2004-2008, the region experienced 6% growth (IMF,
2010). This growth, remarkable because based on the notoriously instable price of
natural resources, can be explained by an increase in exports, especially petroleum
exportsi, and a stabilization of public finances. Nevertheless, although this growth
showed the CEMAC countries in a favorable light in terms of per capita GDP, it had no
effect in terms of social welfare, specifically poverty reduction through increased
employment. This lack of social welfare effect may be explained in that the rate of
growth is still too small to be able to reduce poverty, much less to achieve the goal of
cutting the rate of poverty in half by 2015 (CEMAC, 2009). Moreover, petroleum
revenues do not tend to be re-invested into other promising sectors of the economy.
Taken individually, CEMAC countries have experienced significant, though not uniform
growth ratesii (Table A.1). Like others, CEMAC member states were greatly affected by
the financial crisis of 2008iii. This crisis led to a decrease in the demand for CEMAC
petroleum, leading to a sharp decline in growth in 2009: growth rates fell from 3.9% in
2008 to 2.1% in 2009 (Banque de France, 2009). This decrease in economic activity
occurred in all CEMAC countries but was unevenly distributed. In 2009, the economic
growth rate was still 2% in Cameroon and in the Central African Republic, but ranged
from 8% in the Republic of Congo to -5% in Equatorial Guinea, with -1% in Gabon and -
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2% in Chad. According to forecasts by the monetary policy committee of the Bank of
Central African States (BEAC), economic growth should stabilize to about 5% once the
crisis is over (BEAC, 2011). To manage the negative effects of the crisis, a regional
economic program has been set up with the aim of leading the regional economy towards
a more sustainable development. This program is expected to triple per capita GDP and
reduce poverty within the CEMAC area by 2025 (CEMAC, 2009).
b. Trade structure: the dominance of the primary sector
As is true for most African countries, CEMAC exports are predominantly made up of
raw, unprocessed goods (see Table A.2)iv. These countries have a strong competitive
advantage in raw materials, which make up about 90% of the region’s exports. Exports
from other sectors (stages 2-6) have only a very marginal place in CEMAC trade
relations with the rest of the world: only about 10% of exports. In the Republic of Congo,
this heavy concentration has in fact led to a decrease in diversification of exports in
recent years (Lessoua & Diaw, 2011). The demand for petroleum facing these countries
actually leads to a reduction in supply from the other sectors. It is therefore crucial that
CEMAC countries diversify their economies beyond petroleum so as to promote
sustainable growth in the region.
This common specialization of the CEMAC countries raises further questions about what
other markets might be developed in the region and how this specialization affects
interregional trade. In other words, this specialization in raw materials may lead
CEMAC countries away from interregional trade and towards foreign trade partners,
especially emerging countries. It remains unclear whether this new arrangement could
lead to further economic development in the region.
c. CEMAC: a poorly integrated union
A look at the empirical literature reveals an ambiguous relationship between economic
growth and regional integration in Africa. Evans (1998), Lewis, Robinson & Thierfelder
(1999), and Coe & Hoffmaister (1999) have shown that certain trade agreements in
Africa have brought about minor increases in trade. On the other hand, Subramania &
Tamirisa (2001), Longo & Sekkat (2004), and Mayda & Steinberg (2006) have not seen
any significant trade effects of these policies. These studies often suggest that the
African countries that enter into these trade agreements are not necessarily ideal
trading partners for each other since their trade flows are so similar. Along the same
lines, Schiff (1997), Yeats (1998), Cadot, De Melo & Olarreaga (2000) and the World
Bank have come to the same conclusion: trade agreements among African countries, or
indeed among less-developed countries, do not lead to the creation of additional trade
and thus do not contribute to the development of these countries. These studies even
show that any increase in trade among these countries due to trade agreements is
simply a diversion of trade and has negative effects on long-term economic development
in the trade area.
At the same time, many studies have shown that trade agreements among developing
countries can lead to the creation of new trade. For example, in the case of the CEMAC
countries, Carrère (2004) and Gbetnkom (2008) used a gravity model to show that the
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propensity to trade with another country doubles if that other country is also a CEMAC
member. But as Table A.3 shows, the trade in raw materials, which makes up close to
90% of the area’s exports, is very limited within the area. This confirms that in fact trade
among these countries is practically nonexistent and is more oriented toward outside
partners. Stage 6 consumer goods do make up half of the trade flow among CEMAC
countries, but since very few final goods are actually produced in CEMAC countries, the
flow of final goods remains very small. This confirms the findings of Masson & Patillon
(2004) on the absence of any economic effect of the union.
Table A.3 shows the mismatch between trade among CEMAC countries and each
country’s specialization. There are many possible reasons for this, including the
significant geographic barriers separating some of the countries (Limao & Venables,
2001) as well as the absence of developed transportation and communication
infrastructure (Longo & Sekkat, 2004), not to mention the significant bureaucratic
hurdles. Moreover, most of these countries are specialized in the same economic sector
(petroleum) and thus prefer to trade with the rest of the world, where the demand for
their products is higher. This explains why integration among CEMAC countries has
remained weak. Madariaga (2010) also shows that the intra-CEMAC openness rate (a
meager 1% of GDP) has been decreasing over the past decade. In fact, trade among
member states made up less than 2% of all CEMAC trade. This highlights the failure of
the CEMAC preferential trade policy, which had been meant to develop trade among
member states and thus lead to economic growth. It must be noted that CEMAC member
states that trade most within the area are those who lack access to the coastline; the
flows are subsequently exported out of the area (Madariaga, 2010).
Although regional economic integration is considered an effective springboard for
economic development, much remains to be done within CEMAC. This weak integration
brings about a lack of competitiveness, a serious handicap in an increasingly globalized
economy. This problem also reflects poorly upon the attractiveness of the business
environment in CEMAC member states (World Bank, 2011), an impediment to the
foreign direct investment (FDI) flow that is necessary for growth in this area.
However, parallel to this weak integration is the development of stronger trade ties with
emerging economies, particularly China. This new trade policy can be seen as a possible
factor in development. Still, given the CEMAC countries’ dependence on raw materials,
the question arises of how advantageous this trade partnership will be for them over the
long term.
d. The role of trade with emerging economies
The participation of Less Developed Countries (LDCs) in international trade has greatly
increased over the last two decades, which has led to more trade among them. From
1997 to 2005, the percentage of exports from LDCs going to other LDCs grew from 40 to
50 percent of total goods exported (UNCTAD, 2010). Even though this so-called “SouthSouth” trade is still a small segment of world trade (20% in 2007), it has continued to
increase steadily and in fact at a faster rate than that of worldwide trade since 1995:
13% a year as opposed to 9.8% growth in “North-South” trade and only 7% growth in
“North-North” trade.
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Many studies have pointed out the driving role in this trend of the emerging economies
of Asia, especially China and India. One study finds that these countries provide their
partners with the benefit of access to an exclusive market with better terms of trade
(Diaw, Rieber & Tran, 2011). Following the example of other LDCs, CEMAC members
have begun to increase their participation in “South-South” trade relations. Thus,
emerging economies like China play a major role in these countries’ export strategy
(Madariaga, 2010). Table A.4 demonstrates the sharp increase in exports of raw
materials to China, now 20% of the exports in that sector. The table also shows a fairly
sharp increase in Chinese production of basic manufactured goods since 2006.
In terms of the primary sector, the countries that have turned most to China are the
Republic of Congo (with an average of 30% of the country’s total export of primary
products in 2000-2009), Equatorial Guinea (25%), and Gabon (12%); these countries have
the most petroleum of the trade area (Table A.5). The percentage of exports from the
CEMAC going to China went from 3.2% from 1990-1999 to 16.3% from 2000 to 2008,
while the percentage going to developed countries dropped by almost a half (Madariaga,
2010). Nevertheless, this active export to China perpetuates the mono-specialization of
these countries and makes them even more vulnerable to price fluctuations.
But aside from the instabilities inherent in the raw materials economy, it remains
interesting to explore whether increased trade with China will allow the CEMAC
countries to maintain their economic growth.
3. The economic effect of trading with emerging economies
In this empirical section, we attempt to explain the dynamics of growth in the CEMAC
area. The main objectives are to identify the impact on growth of specialization in these
countries and trade policy (inter-regional or with China). At the same time we will
evaluate the amount of convergence among these countries and the effect of FDI on GDP
growth in the CEMAC.
Our first objective is to evaluate the direct effect of specialization on growth, sector by
sector, with a particular focus on the primary sector. The literature frequently mentions
the idea that specialization in the primary sector results in overall poor economic
performance (Humphreys, Sachs & Stiglitz, 2007; Lessoua & Diaw, 2011). If this is true
for the CEMAC countries, any comparative advantage in the primary sector should
affect negatively economic growth of CEMAC countries. The specialization of CEMAC
countries is computed using the contribution to balance of trade indicator (ICS), a
refinement of Balassa’s indicator (1965). In addition to export data, the ICS indicator
takes into account the import structure of the country to evaluate its comparative
advantage (see Diaw & Tran (2009) for more details). According to this indicator, a
country has a comparative advantage in a given product if the trade balance for the
product is more than its theoretical balance (Lafay, 2004). The indicator is written:
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



1000
X  M iw
( X ij  M ij ) iw
ICSij 
( X j  M j ) , where X ij and M ij are the exports
PIB j 
X w  M w

Actual
Theoretical
 balance

balance
and imports of country j in good i and w subscript denoting world data.
Next, for trade policy, our objective is to evaluate the effect China’s demand has on
economic growth in the CEMAC countries. In each stage, we use as an explanatory
variable the percentage of China in exports from the CEMAC countries, by stage
(variable Share_Chine_s, where subscript s denotes stage number). Moreover, in order to
evaluate the trade integration in the area, we also look at the effect of interregional
trade on that economic growth variable (Share_CEMAC_s).
The literature often views the flow of FDI as a factor in the growth of recipient countries
(Findlay, 1978; Görg & Greenaway, 2004; Romer, 1993). It is worth repeating that there
is no consensus in the empirical literature that there is in fact a positive relation
between the influx of FDI and economic growth. Indeed, while many studies have found
a positive relation (Alfaro, Chanda, Kalemli-Ozcan & Sayek, 2003; Haddad & Harrison,
1993; Li & Liu, 2005; Kokko, Tansini & Zejan, 1996; Ram & Zang, 2002), other studies
have found mitigating factors that limit or eliminate that relation (Aitken & Harrison,
1999; Borensztein, DeGregorio & Lee, 1998; Carkovic & Levine, 2005). According to
these authors, the ability of a country to profit from FDI depends on several factors,
including its potential for successful technology transfer (Blomströn, 1996; Borensztein
et al., 1998; Görg & Greenaway, 2004). This potential clearly varies with the gaps among
countries in terms of development (Findlay, 1978), technology (Glass & Saggi, 1998,
2004; Kokko, 1994), and the quality of the financial sector (Alfaro, Areendam, Sebnem &
Sayek, 2004; Hermes & Lensink, 2003). Weakness in any of these areas is generally
associated with low human capital endowments, poor infrastructure, or a difficult
institutional environment in the recipient country. All these weaknesses restrict the
mechanisms by which FDIs can have a lasting productivity effect on the recipient
country.
Finally, the overall growth of the area along with its level of stability will be assessed by
looking at economic growth as a dynamic process, with the possibility of convergence
among the member states. Their GDP levels will be integrated into our dynamic model
to estimate this convergence.
a. The model: dynamic panels
In order to study dynamic economic growth in the CEMAC countries, we use dynamic
panel data estimation techniques. This allows us to relate economic growth at a given
time to that observed at an earlier time (AR(1) model). The dynamic model that we
estimated is specified as follows:
GD Pi ,t  GD Pi ,t 1  X i ,t  X i ,t 1  i   i ,t
(1)
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 P is the rate of growth in country (i) at time (t), and X is the matrix of the
where GD
i ,t
i ,t
explanatory variables at time (t). It includes measures of comparative advantage for
each stage, GDP level, the inflow of FDI, and the shares of China and the CEMAC
countries in the six stages. But given the small changes in the structure of comparative
advantage, their lagged values are not taken into account in the regressions. The
 i stand for the country-specific effects that might explain the differences in growth.
These effects are assumed to be fixed and independent of estimation errors (  i,t ).
For dynamic models, OLS is quite inefficient particularly because of the endogeneity of
the lagged variable relative to the fixed effects. It creates an upward bias in the
estimation of the coefficient associated with the lagged endogenous variable. One way
that has been suggested to correct this bias is to transform the estimation model so as to
eliminate the fixed effects. The first change involves using the Within-Estimator, which
subtracts the individual mean at every observation. Since the specific effects are
constant over time, each observation equals the mean.
Nevertheless, Nickell (1981), Judson & Owen (1999), and Bond (2002) have shown that
the Within-Estimator is itself not efficient, especially for panels with few time periods. In
fact, they showed that in these short-t panels, the transformation results in a substantial
negative correlation between the transformed lagged dependent variable and the
transformed error term. In this way, according to Bond (2002), any significantly better
estimator should find a coefficient for (  ) somewhere between that of the WithinEstimator and that of the non-transformed OLS estimator.
Anderson & Hsaio (1981) have suggested a different transformation to correct the
endogeneity bias between the lagged variable and the fixed effects. This involves
estimating a first-difference model, which by design also eliminates individual effects.
GD Pi ,t  GD Pi ,t 1  X i ,t  X i ,t 1   i ,t
(2)
However, this transformation does not make it possible to remove the endogeneity of the
 P ) in relation to the transformed error
transformed lagged dependent variable ( GD
i , t 1
 P in GD P is correlated with 
term (  i,t ), since GD
i , t 1
i , t 1
i ,t 1 in  i,t .
Anderson & Hsiao (1981) therefore suggest using the instrumental variables method to
overcome this hurdle. According to them, for every first-difference observation
(beginning in the 2nd period) there are two potential instrumental variables, both already
present in the model, namely the level and the first-difference variables of the previous
P


time period. For example, for GD
i , t 1 both GDPi , t  2 and GDPi , t  2 are appropriate
P
instruments since they are highly correlated with GD
i , t 1 but not correlated with
 i,t , assuming that the errors are time independent and that the initial conditions are
predetermined (Bond, 2002). Anderson & Hsiao, on the other hand, prefer levels as
instruments for differences, since especially in the case of short-t panels, level
instruments offer a better way to use more observations, which is a welcome efficiency
gain.
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This being said, their method does not allow for the possibility of using potential lags as
instruments. This possibility was later introduced by Holtz-Eaken, Newey & Rosen
(1988) and Arellano & Bond (1991). Their methodology is based on the Generalized
Method of Moments (GMM) with additional orthogonality assumptions to ensure the
non-endogeneity of the instruments.
Arellano & Bond (1991) propose a GMM estimator that is based on the orthogonality of
the level variables instruments to the differences of residuals: the condition on the
moments is as follows:





 E GDPi ,t  j   i ,t  0
for j  2 and t  3,4,............, T


 E X i ,t  j   i ,t  0

(3)
P
where GD
i , t  j and X i ,t  j stand for the collection of instruments for the first-difference
variables.
Blundell & Bond (1998), however, show that for very long time series, level variables are
very weak instruments for first-difference variables. For efficiency gains, they suggest
additional moment conditions that can take into account a wider range of instruments
(system GMM). Their suggested transformation is an extension of Arellano & Boyer’s
(1995) forward orthogonal deviations to make the instruments exogeneous relative to the
fixed effects.
The conditions on the additional moments are as follows:



E GD Pi ,t 1  (i   i ,t )  0
, t  3,4,............, T


E X i ,t 1  (i   i ,t )  0
(9)
P
where GD
i , t  j and X i ,t  j stand for the collection of instruments for the level
variables, with j  2 .
To estimate our dynamic model, we have thus chosen to use the GMM (Blundell & Bond,
1988) approach. The efficiency of the GMM method in a dynamic panel, however, must
be tested. The two prerequisites are a good identification of instruments (Sargan test)
and the absence of autocorrelation among the residuals (Arellano & Bond test). The
Sargan test states as a null hypothesis the absence of correlation between instruments
and residuals. If this hypothesis is rejected, then the estimations are not efficient. The
Arellano & Bond test, on the other hand, states as a null hypothesis the absence of
autocorrelation among residuals. Since the test involves a first-difference
transformation, there will necessarily be a first-order autocorrelation. On the other
hand, the absence of autocorrelation among (level) residuals is guaranteed if there is no
second-order autocorrelation among the first-difference residuals. For an efficiency gain,
we corrected the standard deviations of the heteroscedacity bias, following Windmeijer’s
(2000) guidelines.
For comparison’s sake, we also present estimations based on a static panel with fixed
effects, the Hausman test suggesting fixed individual effects. The estimation results are
in the appendix (Table A.6).
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b. Interpretation of estimation results
The Sargan tests for instrument validity, as well as the first- and second-order
autocorrelation tests, do not reject the chosen level and first-difference instrumental
variablesv and thus validate the GMM-based estimates.
Our results find an autoregressive process for economic growth in the sense that growth
at time t has a positive and significant relation to growth at time t-1. This result
supports the relevance of dynamic panel data estimators to study economic growth in the
CEMAC area. Furthermore, we can see a tendency towards convergence among the
CEMAC member states. Indeed, although GDP at time t has a positive and significant
effect on the growth rate at time t, the lagged values have a negative effect on growth.
This result is also supported by our static model.
In terms of capital flowvi, our results for both the dynamic and the static model show a
positive impact from the flow of FDI on the growth of CEMAC member states.
Figure 1: Inflow of FDI into the CEMAC area (in millions of $, nominal)
Source: World Bank, WDI, 2011
As we can see in Figure 1, the inflow of FDI into the CEMAC area is generally
increasing, especially in the countries with the largest petroleum reserves. In fact, as is
the case for all countries rich in natural resources, in the last few years increasing prices
have bolstered the incentive to invest in these areas (UNCTAD, 2009a). But for most of
the CEMAC states, it is worth noting a growing shift towards investment in
infrastructure, real estate, and services such as banking and telecommunications
(Dzaka-Kikouta, 2008; Reisen, 2007). While developed countries are the major source of
these investments, there has been a significant increase in the flow from other countries,
predominately from Asian countries like China and India (World Bank, 2011). This
positive correlation between FDI inflow and growth in the CEMAC area suggests
positive spillovers from the capital flowing into the area, especially given its impact on
the expansion of the service sector and other growth-stimulating sectors. Capital coming
from emerging countries, especially China, takes on a special interest for the continent,
as it is seen as the best opportunity to overcome weaknesses in infrastructure and
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stimulate intra-regional commerce in some sectors such as agriculture (Sheridan, 2007;
UNCTAD, 2009b).
If we return to the question of the effect specialization has on growth, we can see that for
all the sectors besides the primary sector, comparative advantage has a positive effect on
economic growth. In these other sectors, the expected effect of proper specialization is
confirmed. On the other hand, specialization in the primary sector seems to have
weakened the growth of the CEMAC member countries. The countries that have a
relatively high comparative advantage in this sector (see Figure 2) have a tendency to
grow more slowly, especially because of poor management of these resources. This can be
seen in a lack of investment, which in turn prevents other sectors from developing and
can develop into the “Dutch disease” that is typical for countries with rich natural
resources (Corden, 1984; Gelb, 1988; Gylfason, 1999; Gylfason & Herbertsson, 1996;
Gylfason, Herbertsson & Zoega, 1999; Neary & van Wijnbergen, 1986; Sachs & Warner,
1995, 1999).
Figure 2: Comparative advantage in stage 1
Source: Authors’ own calculations
Nonetheless, unlike inter-CEMAC trade, trade with China seems to reduce the negative
effect associated with large endowments in natural resources. China is currently offering
market opportunities that are much more advantageous than those offered by CEMAC’s
traditional trading partners. Besides its demand for natural resources, China is making
an impact on the infrastructure needed to access these resources. This can be seen in the
road and rail improvements and the hydroelectric dams that support the governments of
partner countries in their global trade policies. Moreover, the opportunity for CEMAC
countries to find new buyers for their primary export products allows them to sidestep
preexisting constraints and benefit from demand-side competition. The trade partners
then invest more in the area in order to more easily access the natural resources and
thus facilitate the economic growth in those areas. The China-CEMAC relationship can
thus be seen as a win-win situation as it positively affects economic growth. But for this
growth to be sustainable, the countries need to ensure that it spills over into other
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economic sectors. For this to happen, the revenues from the sales of natural resources
need to be reinvested into other sectors rather than placed back into the primary sector.
Furthermore, the CEMAC area must insist upon a more dynamic interregional trade. In
theory, regional trade liberalization should stimulate economic growth by creating
businesses (Diaw & Tran, 2009). But we have found that for the CEMAC, interregional
trade in any sector has failed to create economic growth in the area. This is a result of
the aforementioned situation in which each country has a supply of and demand for
similar goods. Therefore, far from being trade partners, these countries seem more like
competitors on the global market, and must insist on coordinating their trade policies to
fully benefit from their comparative advantages.
Conclusion
In this study we have attempted to analyze the dynamics and the repercussions for
growth of specialization and trade orientation of the CEMAC member states. The main
objective of this study was to not only assess the weaknesses in the economic integration
among the CEMAC members but also to evaluate their new and increasing trade
opportunities with China.
Our results show that despite some criticism of the economic and trade relations
between China and Africa, this relationship has proved to be relatively effective in terms
of economic effects in many African countries including the members of CEMAC. This
work has shown the real and positive effect of trade orientation towards China, in spite
of the mono-specialization of the countries in the area. African countries and especially
those in the CEMAC need to optimize the positive side of this relationship, since it is a
win-win situation in certain cases. To make the best of this, the countries in the area
need to develop common development strategies based on good global economic
governance. This will allow the member states to free their economies from the vicious
cycle of investments too heavily focused on the primary sector and thus the “Dutch
disease” they are susceptible to.
Another significant challenge is that of attracting an inflow of FDI, since our results
have shown that it does in fact have a positive effect on growth. However, the CEMAC
countries must find more effective ways to channel the flow of FDI into non-primary
sectors of the economy. It is therefore necessary for these countries to improve not only
their physical infrastructure and business climate but also their ability to use FDI
effectively. These policies will be necessary for the CEMAC countries to develop their
competitiveness and facilitate further trade. The countries in this area are thus
encouraged to adopt such a policy, so as to create a productive economy with high value
added and extricate themselves from their current petroleum-based economy.
Finally, our results strongly confirm the weak trade complementarities among the
CEMAC countries. For a better integration in the global economy, these countries should
not count on forever relying on foreign demand but should also build sustainable
regional growth through local trade. The leaders of the CEMAC need to identify ways to
improve trade complementarities among the states and thus protect themselves from
various crises like those of recent years, such as food shortages and the financial debacle.
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Appendix:
Table A.1: Economic growth in CEMAC countries
Year
CMR
COG
GAB
GNQ
RCA
TCD
1995
3,3
4,0
5,0
14,3
7,2
1,2
1996
5,0
4,3
3,6
29,1
-4,0
2,2
1997
5,1
-0,6
5,7
71,2
5,3
5,7
1998
5,0
3,7
3,5
21,9
4,7
7,0
1999
4,4
-2,6
-8,9
41,4
3,6
-0,7
2000
4,2
7,6
-1,9
13,5
2,3
-0,9
2001
4,5
3,8
2,1
61,9
0,3
11,7
2002
2003
4,0
4,0
4,6
0,8
-0,3
2,5
19,5
14,0
-0,6
-7,6
8,5
14,7
2004
3,7
3,5
1,3
38,0
1,0
33,6
2005
2,3
7,8
3,0
9,7
2,4
17,3
2006
3,2
6,1
1,2
1,3
3,8
0,2
2007
3,5
-1,6
5,6
21,4
3,7
0,2
2008
2,9
5,6
2,3
11,3
2,2
-0,4
2009
2,0
7,6
-1,0
-5,4
2,4
-1,6
Source: World Development Indicators (World Bank, 2010)
Table A.2: Stages driving CEMAC exports (% CEMAC-World)
Stages
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
S_1
87,9
90,0
90,6
88,6
86,7
89,6
89,4
90,9
89,1
89,7
89,5
91,5
93,0
94,7
S_2
4,0
2,4
2,0
3,0
2,8
2,6
2,6
2,0
2,6
2,1
2,1
2,4
3,3
2,1
S_3
2,5
2,0
2,0
2,3
2,0
1,4
2,0
2,5
2,2
2,0
1,7
1,4
1,3
0,9
S_4
0,9
1,0
1,0
1,2
2,6
0,5
0,5
0,7
0,7
1,3
0,7
0,4
0,3
0,2
S_5
3,8
3,8
3,4
4,3
5,2
5,3
4,6
3,4
4,5
4,2
5,5
3,9
1,8
1,9
2009
93,9
1,7
1,3
0,6
2,4
Source: Calculations based on BACI data.
S_6
1,0
0,9
0,9
0,6
0,7
0,5
0,8
0,4
0,9
0,6
0,5
0,4
0,3
0,1
0,2
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Table A.3: Stages driving inter-CEMAC trade (% CEMAC-World)
CEMAC S_1
S_2
S_3
S_4
S_5
S_6
1995
0,1
15,8
9,0
24,4
8,2
48,5
1996
0,6
18,0
10,4
17,9
9,1
43,8
1997
0,5
12,5
11,4
24,9
8,7
49,8
1998
0,3
7,6
3,4
15,8
2,9
32,9
1999
0,1
5,3
3,6
6,8
2,1
46,4
2000
0,0
5,6
3,9
3,6
2,8
43,7
2001
0,1
9,2
6,2
12,2
7,1
39,4
2002
0,0
2,3
1,5
5,3
3,1
13,1
2003
0,1
8,8
8,3
14,9
13,1
36,3
2004
0,1
5,4
6,4
7,5
10,2
38,4
2005
0,4
5,1
7,9
12,3
8,4
57,3
2006
0,5
4,4
5,1
11,8
7,7
57,1
Source: Calculations based on BACI data.
Table A.4: Stages driving exports to China (% CEMAC-World)
China
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
S_1
3,0
4,9
8,0
5,5
9,7
12,4
12,4
11,2
15,6
22,0
20,0
23,9
20,5
19,6
S_2
0,0
0,0
1,3
0,3
2,4
7,8
5,0
4,1
1,7
1,2
5,1
10,1
28,8
23,9
S_3
1,0
0,1
0,2
0,4
0,3
0,1
0,3
1,0
0,8
1,1
1,1
0,6
0,8
0,8
S_4
0,0
0,0
0,0
0,0
0,1
0,0
0,0
0,0
0,0
0,1
0,2
0,0
0,1
0,1
S_5
0,0
0,1
0,0
0,0
0,1
0,3
0,1
0,5
0,3
0,3
0,2
0,2
0,6
5,6
2009
17,4
14,6
0,6
0,0
0,3
Source: Calculations based on BACI data.
S_6
0,0
0,2
0,0
0,1
0,0
0,1
0,0
0,0
0,1
0,9
4,4
3,2
1,0
0,4
0,1
Table A.5: China’s share in primary sector exports (S_1), by country
Year
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
CMR
2
2
4
3
5
7
7
7
6
5
2
4
4
10
15
COG
0
2
10
4
5
19
12
14
42
50
44
34
36
32
26
GAB
5
7
9
6
11
8
7
7
8
8
6
15
19
23
18
GNQ
18
11
22
17
28
28
31
21
16
29
24
35
22
16
14
RCA
0
6
2
0
0
0
2
2
2
9
8
12
7
19
26
2000/2009
7
31
12
24
9
Source: Calculations based on BACI data.
TCD
0
0
1
0
0
0
0
3
3
18
12
11
3
1
3
5
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Table A.6: Estimation results
N
chi2 / Fischer
R2
Panel
Dep Var: GDP_g
GDP_g
ICS_1
ICS_2
ICS_3
ICS_4
ICS_5
ICS_6
LnGDP
LnFDI
Share_Chine_1
Share_Chine_2
Share_Chine_3
Share_Chine_4
Share_Chine_5
Share_Chine_6
Share_CEMAC_1
Share_CEMAC_2
Share_CEMAC_3
Share_CEMAC_4
Share_CEMAC_5
Share_CEMAC_6
t
-0.04***
0.57***
0.09***
0.05***
0.16***
0.11***
27.65***
0.60***
-0.03
-0.21***
-0.59
-8.28***
-0.40***
0.02
-0.66
-0.13
-0.09**
0.03
-0.03
-0.02
Arellano-Bond test
AR(1)
AR(2)
504
2.5e+05
Dynamic
lagged (t-1)
0.14***
-27.60***
0.48*
0.22**
0.23***
-1.34***
-3.96*
0.17**
-0.05
0.11
0.08*
0.10
-0.09
-0.21**
-0.02
0.0007
0.6764
540
9.37
0.2192
Static
Fixed effects
0.01
-0.19
-0.07
-0.06
-0.10
-0.11
-7.86***(a)
0.49**
0.32**
0.13
-1.16
-3.01
-0.09
-0.08
-0.06
0.13*
-0.11
0.01
-0.30*
-0.09**
Hausman test
P-value = 0.00
Sargan test
P-value
0.9375
Note (a): For the fixed effect estimation, the lagged value of the GDP is considered.
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Endnotes
i
Petroleum makes up a significant portion of the exports of CEMAC countries: in 2009,
it made up 94.4% of exports in Equatorial Guinea, 86.2% in Chad, 90.4% in Gabon,
80.5% in Congo, and 40.7% in Cameroon for a CEMAC average of 83.2% (BEAC,
2010).
ii
Data on economic growth for each CEMAC country come from the World Bank (World
Development Indicators, 2011).
iii
Since the financial sector of the CEMAC is underdeveloped, the economy was only
affected in terms of the traditional exchange of goods and services, especially
petroleum, timber, and other raw materials.
iv
The trade data for the CEMAC countries come from the CEPII’s database for
international trade analysis or BACI. BACI provides data on the flow of products with
the HS-6 digit classification. The raw data for the BACI come from UN trade data
(UN COMTRADE): http://www.cepii.fr/anglaisgraph/bdd/baci.htm
v
Our various tests lead us to keep the second-order lagged variables as instruments.
Using the full set of instruments would lead us to reject their validity.
vi
The data on the inflow of FDI come from the World Bank (World Development
Indicators, 2011).
20