| Abstract: |
This paper deals with the problem of vulnerability of developing countries and
their resilience capacity with respect to external shocks. The analysis
particularly considers the countries of Latin America and Sub-Saharan Africa.
Although the transmission risks of the 2007 financial crisis were initially
underestimated for Southern countries, it ended up reaching all continents.
Aiming at understanding the crisis propagation the paper carries out a
comparative analysis between these two groups of countries. The objective is
to test the resilience of Latin America and Sub-Saharan Africa countries with
respect to the effects of the economic crisis. It accounts for the differences
between countries’ behavior with respect to external shocks. Using dynamic
panel techniques the paper estimates the growth dynamics for these countries.
The estimates results are shown to be relevant and indicate that some groups
of countries are more resistant to crisis effects than others. In order to
study dynamic economic growth in our sample countries, we use dynamic panel
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 estimate is as follows: . (1) where is the rate of growth in country
(i) at time (t), and is the matrix of the explanatory variables at time (t).
It includes according to data availability: public aids in percentage of GDP,
external debt, external reserves, domestic savings, net inflows of foreign
direct investments, remittances and the current account balance (CAB), two
dummy variables (to distinguish Fuel exporting and Metal exporting countries)
and the number of countries in recession (to appreciate contagion risks). The
stands for the country-specific effects that might explain the differences in
growth between countries. These effects are assumed to be fixed and
independent of errors ( ). 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 and 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
Within-Estimator and that of the non-transformed OLS estimator. Anderson and
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: . (2) However, this transformation does not make it
possible to remove the endogeneity of the transformed lagged dependent
variable ( ) in relation to the transformed error term ( ), since in is
correlated with in . 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 time period. For
example, for both and are appropriate instruments since they are highly
correlated with but not correlated with , assuming that the errors are time
independent and that the initial conditions are predetermined (Bond, 2002).
Anderson and 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. However, their method does not allow for the possibility of using
potential lags as instruments. This possibility was introduced later by
Holtz-Eakin et al (1988) and Arellano and 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 and
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: for and (3) where and stand for the collection of
instruments for the first-difference variables. Blundell and 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 and 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: , (4) where and stand for the collection of
instruments for the level variables, with . For the purpose of this paper in
order to estimate our dynamic model, we have chosen to use the GMM (Blundell &
Bond, 1998) 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. The transmission of the crisis from developed to developing
countries operated through two main channels: the traditional channel of
international trade and the international finance (Hugon and Salama, 2010).
Theoretically, many factors may justify the vulnerability of economies of
Sub-Saharan Africa and Latin America. All these economies did not experience
the effects of external shocks in the same way. Aiming at assessing the
resilience capacities of these Southern countries with regard to the crisis
this paper has performed an econometric investigation using a dynamic panel
model methodology. Furthermore, three sample countries have been considered to
carry out an empirical comparative analysis. Two samples of Sub-Saharan
African countries have been differentiated by the membership to the CFA zone,
and one sample of South American countries. The three groups of studied
countries share common features of economic structure and terms of trade.
Concerning the Sub-Saharan African countries, it has been shown that for the
CFA countries the transmission factors listed in the theoretical part are not
linked significantly to GDP growth. The only factor of vulnerability for these
countries has been shown to be the FDI inflows. In recession periods, we
showed that FDI decrease aggravate the crisis. Resilience capacities have not
been detected for this group. The Sub-Saharan African countries out of the CFA
zone present different results. For them, the results show a significant link
between public development aid and GDP growth. This link is significant and
positive in recession and expansion periods. This indicates that public
development aid constitutes a significant pro-cyclical transmission vector.
Domestic savings have been shown to be a pro-cyclical variable too. Indeed,
its decline in recession period may worsen the deterioration of the economic
situation. The external debt has been shown to be a counter-cyclical variable
in recession period. This may contrast with the counter-cyclical property of
external reserves shown in expansion periods. These countries have the
capacity to use their external assets and debts to adjust their macroeconomic
situation. The clear difference of results between CFA zone and non-CFA
Sub-Saharan African countries could motivate further research about the role
of the strict peg to the euro. In the case of Latin American countries three
variables have been shown to be significantly related to the fluctuations of
GDP growth. These factors are foreign direct investments (FDI), domestic
savings and the current account balance (CAB). FDI and CAB present opposite
behaviours relative to GDP. During expansion periods, FDI and CAB fall and
increase during recession periods. Indeed, when the growth of the GDP
decreases, authorities try to relax their FDI legislation in order to
rebalance the economic situation. In addition, they implement competitive
devaluation policies affecting the CAB. Countries of Latin America present
also the highest risk of propagation in our analysis. Taking into account that
the three groups of studied countries share common features of economic
structure and terms of trade, we could expect that the resilience capacities
would be similar. However, this paper has shown that resilience capacities of
the three investigated country groups (African CFA zone, Sub-Saharan African
non-CFA zone and Latin America) are not the same. Considering the Sub-Saharan
African countries the econometric results show that countries of the non-CFA
group better perform in terms of resilience to external shocks. This area has
shown to be less vulnerable to the transmission of the crisis effects compared
to the two other groups. The econometric regression results reveal also a
determining factor of vulnerability common to the Non-CFA zone and Latin
America which is domestic savings. This paper highlights interesting factors
and mechanisms relative to the capacity of resilience of certain developing
countries with respect to external shocks, in particular those of the Latin
America and Sub-Saharan Africa. Further research may be carried out to
investigate other variables likely to explain the resistance of Southern
countries to the crisis effects and their causalities. Data availability will
remain the principal limitation. |
| Keywords: |
Latine American countries: Chile, Colombia, Costa Rica, Ecuador, Nicaragua, Paraguay, Peru, Uruguay, Venezuela RB. CFA zonz countries: Congo, Cameroon, Equatorial Guinea, Central African Rep, Gabon, Chad, Senegal, Benin, Togo, Burkina Faso, Niger, Mali, Côte d'Ivoire,. Non CFA zone: Angola, Ethiopia, Ghana, Guinea, Kenya, Madagascare, Mozambique, Nigeria, Seychelles, Siera Leone, Sudan, Tanzania, Uganda, Zambia, Zimbabwé, Developing countries, Developing countries |