REGIONAL COOPERATION AND ECONOMIC INTEGRATION
REGIONAL COOPERATION AND ECONOMIC INTEGRATION REGIONAL COOPERATION AND ECONOMIC INTEGRATION
FDI FLOWS IN SOUTH EASTERN EUROPE On the basis of the data for the above mentioned variables for the period 1999-2007 and by applying the econometric software package EViews 6, we have obtained the following results: Table 2: Determinants of FDI: Dependent variable= FDI stock per capita in year t variable Model 1 Model 2 Model 3 Model 4 Model 5 LOG(GDPPC) WAGEDIF LOG(DIST) LOG(MOBILE) 0.544284 (0.158372) *** 0.000577 (0.000141) *** -1.784674 (0.314970) *** 0.406643 (0.096818)*** 0.534746 0.164444*** 0.000569 0.000188*** -1.217531 0.783777 0.380709 0.106024*** -0.005045 0.304262 0.000567 0.000174*** -0.790082 0.754567 0.245301 0.118284* 0.152342 0.295630 0.000222 0.000322 * 0.601180 0.934152 0.258726 0.295341 -0.162915 0.322514 0.000211 0.000298 0.128269 0.900880 0.303610 0.274075 SECONDARY CPI LOG(TROPEN) RANKING LOG(FDIPC(-1)) NEG 0.007332 (0.024770) 0.002866 0.027337 -0.001726 0.004300 0.723016 0.905690 0.010321 0.025552 0.002953 0.004587 0.007846 0.019725 0.042107 0.020554* 0.004429 0.022699 -0.000567 0.004731 1.792446 0.998653* 0.003639 0.023487 0.382394 0.193472* 0.004923 0.020983 -0.001824 0.004426 1.421671 0.944801 0.012692 0.022260 0.322671 0.181750* 0.327494 0.177960* Adjusted R 2 0.940018 0.935857 0.945095 0.952488 0.959408 Standard errors are presented in brackets below coefficients. ***,**,*, indicate statistical significance at level of 1%, 5% and 10% respectively. How are the above obtained econometric results interpreted The model (1) in the second column of Table 2e explains the variations of FDI stock per capita as a result of the classical FDI determinants. This model shows that market size expressed as GDP per capita, labour costs, transportation costs and infrastructure play important role in attracting FDI from EU to the EU candidate countries. When it comes to labour costs, a negative correlation is found between wage difference as a proxy for labour costs and FDI in our model which is consistent with the theoretical expectations that FDI is driven by lower labour costs. This is in case when vertical FDI is dominant. The coefficient of the variable secondary school enrolment rate is not statistically significant, so that we can conclude that FDI in these countries are not looking for skilled workers, but are mainly driven by cheap labour force. The distance (DIST) confirms the former expectations i.e. it affects inversely the level of FDI. In the estimated model, the coefficient of the variable 283
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 284
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FDI FLOWS IN SOUTH EASTERN EUROPE<br />
On the basis of the data for the above mentioned variables for the period 1999-2007 and<br />
by applying the econometric software package EViews 6, we have obtained the following<br />
results:<br />
Table 2: Determinants of FDI: Dependent variable= FDI stock per capita in year t<br />
variable Model 1 Model 2 Model 3 Model 4 Model 5<br />
LOG(GDPPC)<br />
WAGEDIF<br />
LOG(DIST)<br />
LOG(MOBILE)<br />
0.544284<br />
(0.158372) ***<br />
0.000577<br />
(0.000141) ***<br />
-1.784674<br />
(0.314970) ***<br />
0.406643<br />
(0.096818)***<br />
0.534746<br />
0.164444***<br />
0.000569<br />
0.000188***<br />
-1.217531<br />
0.783777<br />
0.380709<br />
0.106024***<br />
-0.005045<br />
0.304262<br />
0.000567<br />
0.000174***<br />
-0.790082<br />
0.754567<br />
0.245301<br />
0.118284*<br />
0.152342<br />
0.295630<br />
0.000222<br />
0.000322 *<br />
0.601180<br />
0.934152<br />
0.258726<br />
0.295341<br />
-0.162915<br />
0.322514<br />
0.000211<br />
0.000298<br />
0.128269<br />
0.900880<br />
0.303610<br />
0.274075<br />
SECONDARY<br />
CPI<br />
LOG(TROPEN)<br />
RANKING<br />
LOG(FDIPC(-1))<br />
NEG<br />
0.007332<br />
(0.024770)<br />
0.002866<br />
0.027337<br />
-0.001726<br />
0.004300<br />
0.723016<br />
0.905690<br />
0.010321<br />
0.025552<br />
0.002953<br />
0.004587<br />
0.007846<br />
0.019725<br />
0.042107<br />
0.020554*<br />
0.004429<br />
0.022699<br />
-0.000567<br />
0.004731<br />
1.792446<br />
0.998653*<br />
0.003639<br />
0.023487<br />
0.382394<br />
0.193472*<br />
0.004923<br />
0.020983<br />
-0.001824<br />
0.004426<br />
1.421671<br />
0.944801<br />
0.012692<br />
0.022260<br />
0.322671<br />
0.181750*<br />
0.327494<br />
0.177960*<br />
Adjusted R 2 0.940018 0.935857 0.945095 0.952488 0.959408<br />
Standard errors are presented in brackets below coefficients. ***,**,*, indicate statistical<br />
significance at level of 1%, 5% and 10% respectively.<br />
How are the above obtained econometric results interpreted<br />
The model (1) in the second column of Table 2e explains the variations of FDI stock<br />
per capita as a result of the classical FDI determinants. This model shows that market<br />
size expressed as GDP per capita, labour costs, transportation costs and infrastructure play<br />
important role in attracting FDI from EU to the EU candidate countries. When it comes to<br />
labour costs, a negative correlation is found between wage difference as a proxy for labour<br />
costs and FDI in our model which is consistent with the theoretical expectations that FDI is<br />
driven by lower labour costs. This is in case when vertical FDI is dominant. The coefficient<br />
of the variable secondary school enrolment rate is not statistically significant, so that we<br />
can conclude that FDI in these countries are not looking for skilled workers, but are mainly<br />
driven by cheap labour force. The distance (DIST) confirms the former expectations i.e.<br />
it affects inversely the level of FDI. In the estimated model, the coefficient of the variable<br />
283
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