REGIONAL COOPERATION AND ECONOMIC INTEGRATION

PART V:
DIST shows that if the distance between Brussels and one of the capital cities of the EU
candidate countries increases for one kilometre, that will lead to decrease of the FDI inflow
to EU candidate countries for 0.0009 million Euros.
In model (2) which is presented in the third column of Table 2, we add two policy variables:
openness of economy, as a proxy for external liberalization and consumer price index,
as a proxy for macroeconomic stability. The econometric results suggest that neither of
these variables are determinants that significantly influence the investments decision of
EU investors to invest in the EU candidate countries. The positive, although not significant
impact of TROPEN on FDI indicates that the EU candidate countries which are more open
to international trade are valued more by EU investors. Consumer price index as a proxy
for inflation is negatively related to FDI stocks, but not statistically significant. This finding
suggests that macroeconomic stability, seems to be of a secondary concern to EU investors
investing in the EU candidate countries. However, the obtained econometric results should
not undermine the importance of macroeconomic stability for attracting FDI on a long
run.
In model (3) we add the variable Euromoney country ranking (RANKING) as a proxy
for governance. This variable is statistically significant and shows that if the country
ranking improves for one place, that will contribute to increase of the FDI inflows to
the EU candidate countries for 0,04 million Euros. The results in this model differ from
those previous obtained in sense that GDPPC now turns out to have negative coefficient,
opposite to models (1) and (2). This happens again in model (5) and the fact that it is not
statistically significant (from model (3) to model (5)) indicates that FDI in these countries
are not market-seeking, but export oriented.
The models (1)-(3) (without the agglomeration effect) show that the FDI in candidate
countries are mostly driven by cheap labour force, access to local market (infrastructure)
and non-economic factors, such as governance. This conclusion is more valid for the earlier
investors when there is no prior experience. Once we introduce the agglomeration effect
in model (4), proxied by the stock of FDI with a 1-year lag, the R square increases. The
coefficient of this variable implies that once the FDI stock in the EU candidate countries
reaches a critical mass, it is an indicator of favourable investment climate and attracts more
FDI flows to those countries.
After including the EU accession effect in the last model, the econometric results have
been partly changed. Namely, in the model (5) only the variable FDI per capita from the
previous period ( FDIPC
t−1
) and the dummy variable negotiations (NEG) turned out to
be statistically significant. The positive and significant regression coefficient of the dummy
variable NEG suggests that progresses achieved in the EU integration process plays a crucial
role for the EU candidate countries in attracting more FDI. The EU candidate countries that
have already started with the formal EU association negotiations are more preferred by EU
investors than those countries that have only a candidate status.
CONCLUSION
In this paper we have analyzed the determinants of EU FDI outward stocks per capita in the
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