Regional integration and the other determinants of North-South ...

produces the most efficient and the least biased estimators if compared with the random
effects model (Plümper and Troeger, 2007). The estimator decomposes the unit fixed effects
into an unexplained part and a part explained by the time-invariant variables. In a first step, a
fixed effects model is obtained to estimate the unit effects. The second step involves an
ordinary least squared (OLS) regression of the fixed effects vector on the time invariant
variables. This permits us to decompose the fixed effects vector into a part explained by the
time-invariant variables and an unexplainable part, the error term. In the last stage, the model
is re-estimated by pooled OLS, including all explanatory variables, the time-invariant
variables and the error term. This model is presented in column (3) 44 . Finally, in order to
tackle the problem of the correlation between the residuals and some independent
variables (GDPs), both two stage least squares instrumental variable model (2SLS IV) and
Arrelano, Bond and Bover’s GMM estimator have been implemented 45 .
Estimation results
[Table 4.1]
The models fit of the regression is in line with the usual finding in gravity literature.
All the parameters are significant at the 5% level and present the expected sign: an increase of
importer and exporter’s GDP increases trade flows. The distance between countries and the
presence of borders strongly affect trade. The parameter distance is more than the unity and
the intra-trade is, on average, 11 times (=exp(2,42)) larger than the cross-border trade in the
euro-Mediterranean area.
Since bilateral trade barriers vary across countries, the average border effect can mask
substantial differences across country groups. In other to check for these differences, in a
second stage, we break down the sample into three country groups: 12 Western European
Countries (WEC), 10 new member states (NMS) and 6 Mediterranean countries (MC).
Consequently, border effects have been re-evaluated for each group separately (Table 4.2) 46 .
We can notice that the WEC border effect is relatively low in comparison to both NMS and
MC border effects. Crossing the national frontier inside the WEC reduces trade by 4.5 times
(exp(1.50)), whereas it generates a decrease of trade by a factor of 7 in the NE group and 244
in the MED group. This result clearly shows the lack integration between Mediterranean
countries and tends to confirm Almonte and Gualiano’s (2003) results: Eastern Europe is
more integrated than Mediterranean countries. This non-existence of an important
“Mediterranean” market could have an important impact on firm’s location choices. Eastern
countries can be more attractive than Mediterranean countries because of both their proximity
to the WEC markets and the relatively less segmented regional market 47 .
44 Even if we have a few zero values (0,38%) in our dataset, we have also tried to estimate the equation using
the Poisson Pseudo-Maximum Likelihood (PPML) estimator. However, Poisson iterations have not converged.
45 Note that another solution would have been to transfer the GDPs to the left of the equation.
46 For space raisons, we only present two models in tables 4.2.
47 Note that the market accessibilities measured here include the access for both industrial and agricultural goods
and are so a bit overestimated. That could partly explain the very high border effects concerning Mediterranean
countries.
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