REGIONAL COOPERATION AND ECONOMIC INTEGRATION

SOME ASPECTS OF TRADE STATISTICS AND REPORTING
CONCLUSION
The central aim of this analysis was to confirm the assertion that the gravity model, almost
fifty years after its first introduction, is still a useful workhorse for researchers. To confirm
this affirmation, the analysis formed two different datasets on migration stocks and flows for
the fifteen European Union member states as the destination countries and 73 other states
that represent sending countries. At the same time this paper introduced an equation that is
reminiscent of the generalized gravity equation. Testing showed that the gravity model is,
almost fifty years after the first successful implementation, still a useful instrument.
The results of the estimation by using the fixed effects estimator for either the entire
sample of data or for sub-samples of developing countries, Central and Eastern European
countries and EU-15 states clearly showed that GDP per capita of the destination country
is a significant proxy variable for wage differentials between two countries and that the
population of the destination country is also characteristic as a proxy variable for population
differentials between the sending and receiving country.
But the results of the estimation less characteristically rejected the supposition that the Gini
coefficient of the destination country is a significant proxy variable for income inequality
between the sending and destination country. While our dataset (on stocks) covered
different stratums of the immigrant population, which involved either highly educated
individuals or unskilled immigrant individuals from the sending countries, the introduced
proxy variable for income inequality showed either a positive or negative expected sign
with mainly insignificant values of the coefficients.
We supposed that the Gini coefficient is sensitive measure for income inequality.
Nevertheless, the further research, which data will enables differentiation of immigrant’s
population by the level of education or by occupation, will either confirms or rejects given
hypothesis.
Appendix 1: List of the countries of origin of migrants for the bilateral migration data
EU: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy,
Luxemburg, Netherlands, Portugal, Spain, Sweden, United Kingdom,
CEE: Bulgaria, Czech Republic, Estonia, Hungary, Latvia, Lithuania Poland, Romania,
Slovenia, Slovakia,
Developing world: Afghanistan, Albania, Algeria, Argentina, Armenia, Azerbaijan,
Bangladesh, Byelorussia, Bosnia and Herzegovina, Brazil, Cameroon, Cape Verde, China,
Colombia, Congo Republic, Congo the Democratic Republic, Croatia, Cuba, Cyprus,
Ecuador, Egypt, Ethiopia, Georgia, Ghana, India, Indonesia, Iran, Iraq, Kenya, Korea
Republic, Kyrgyzstan, Lebanon, Libya, Macedonia, Malta, Malaysia, Mexico, Moldova,
Morocco, Nigeria, Pakistan, Philippines, Russian Federation, Saudi Arabia, Serbia and
Montenegro, South Africa, Sri Lanka, Sudan, Syria, Thailand, Tanzania, Tunisia, Turkey,
Ukraine, Uzbekistan, Vietnam,
Other countries: Australia, Canada, Japan, Norway, Switzerland, United States
211