REGIONAL COOPERATION AND ECONOMIC INTEGRATION

PART III:
a positive sign can be expected for the variable POP it-1
( β > 0 2
) , and a negative one for
POP jt-1
( β 1
< 0)
, as a proxy variable for size differentials. As mentioned above, Ortega
and Peri also introduced the Gini coefficient as a measure of income distribution, where
the Gini coefficient of the destination country (GINI it-1
) is a proxy variable for income
inequality. It is supposed that in the periods when the income distribution is more equal,
the opposition to immigration in the host country may be milder. Thus a positive sign is to
be expected for GINI i,t-1
( β > 6
0 ), and by contrast a negative sign for GINI ( β < 0
i,t-1 5
), as
proxy variables for income inequality. The next section will present the data sources and
the regression results.
2. Empirical analysis
2.1. Data
We introduce a generalized gravity equation as a basic empirical specification that is
estimated using the fixed effects method. We initially tested the gravity model on migration
inflows data and migration stock data. The data on yearly flows into 15 European Union
member states are provided from OECD migration statistics. The EU member observed
states are: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy,
Luxemburg, the Netherlands, Portugal, Spain, Sweden and the United Kingdom. Most
of the data provided from the OECD database are taken from the individual contributors
of national correspondents appointed by the OECD Secretariat with the approval of the
authorities of the member countries. Consequently, these data have not necessarily been
harmonized at the international level. Thus the series presented in relatively standard
format does not imply that the data have been fully standardized and are comparable at the
international level. Since the database provides annual series for the ten most recent years,
we used migration inflow and stock data from 1996 to 2006.
This bilateral database, which has more than fourteen thousand items, is carefully examined
and organized separately for two datasets on migration flows and stocks. While all countrypairs
which show only zero items in the observed period are omitted, the final dataset
of approximately six thousand items is formed on migration stocks and approximately
five thousand cross-section items on migration flows. More precisely, the 5874 items that
represent the stock of the immigrant population by nationality and 5247 items that represent
inflow of immigrant population by nationality are extracted from the larger migration
database of 14,204 items. Preliminary tests 8 show that the first extracted dataset of the 5874
items, which represent the stock of foreign-born population in fifteen EU member states
from 1996 to 2006, is more reliable in comparison with the mentioned second extracted
dataset. This reliability of the first dataset is somehow linked with the zero value items.
While the first dataset has less than 9 per cent 0 values, the second dataset has more than
17 per cent zero values. Finally, we add one to each observation relative to stock and
flow of immigrants so that when taking logs we do not discard the 0 observations. The
testing repeatedly shows that the second dataset, which represent inflows of foreign-
8 Redundant (Likelihood Ratio) fixed effects test.
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