REGIONAL COOPERATION AND ECONOMIC INTEGRATION

orn population, is less reliable and consequently excluded from further research. It is
worth noting that we use data provided from OECD migration statistics as given. Thus
we introduce gross values of either stock or inflow of immigrant’s population in EU-15
member states, while Ortega and Peri introduced gross or net values of immigration.
Data for population (given in thousands) and gross domestic product on purchasing powerparity
(PPP) per capita was taken from the web version of the IMF’s International Financial
Statistics (www.imf.org), extracted April 2009). Gini coefficients for EU-15 member states
as destination countries and all other countries of origin were taken from UNU-WIDER
Database (WIID, World Income Inequality Database V2.0c May 2008). Data for distances,
contiguity and common language were taken from the CEPII website (www.cepii.fr).
Distance data is measured in km between the partner countries’ capital cities. Countries
are considered to share a common border when they share a land border. The list of the
countries of origin of the migrants for the bilateral migration data is in Appendix 1.
2.2. Results of the analysis
SOME ASPECTS OF TRADE STATISTICS AND REPORTING
The results of the estimation using the fixed effects (FE) model are robust to various
model specifications and regressions methods. We alternatively introduce either GDP or
population as a measure of the country size differences as predicted by a basic version
of the gravity model presented in equation (1). Marques (2005) points out that GDP as a
proxy variable shows a negative effect on either stock or inflows of immigrants, especially
when GDP per capita is introduced in the model. When the population is alternatively
introduced in the same model, this variable shows a positive expected sign. Our testing of
the regression model (3) completely confirmed these findings of the cited analysis.
It is also characteristic that the introduced proxy variable for wage differentials between
origin and destination country (GDPpc i,t-1
) shows highly significant values of the coefficients
and sign as expected. When we estimated the model firstly in the entire sample of countries
and then on three different sub-samples of countries, the results of the estimation by using
the fixed effect estimator repeatedly confirmed these finding. We also introduced Polled
Least Squares with cross section weights (EGLS) as an alternative estimation method.
Remember that results of the fixed effects estimator are more reliable in comparison with
the pooled EGLS method.
The proxy variable POP i-t-1
, which by supposition shows population differentials between
the sending and destination countries, reveals a positive expected sign with the significant
values of the coefficients by using the fixed effects estimator, and the proxy variable
GINI i,t-1
, shows relatively low values of coefficients and at the same time either a negative
or positive sign in each of the columns. As Svaton and Warin (2007) argue, the negative
statistically significant coefficients for this variable means that immigrants dislike more
unequal societies and allocate themselves to countries with a more even distribution of
income.
But Svaton and Warin, who used a similar source of data for fourteen European member
states from 1994 to 2004 (namely, OECD Migration Outlook, 2006), show a positive sign
for the Gini coefficient as the proxy variable for income inequality. How to explain these
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