Integration, Regional Specialization and Growth Differentials in EU Acceding Countries
11
5 Empirical results
5.1 Knowledge spillovers and manufacturing specialization effects on
regional growth
In order to capture the impact of knowledge spillover effects and of regional
manufacturing specialization on regional growth we estimate the following panel
model:
(2)ln(
GDPCAPj,t+1
GDPCAP
) = a+blnFDIj,t +clnSPECj,t + dAGRICj,t + eSERVj,t + fOPENj,t + gBORDEU+
+εj,t,t+1
j refers to regions and t to years.
εj,t,t+1 =ωj +νt +ηj,t,t+1
where ωj is a fixed effect for region j, νt is a fixed effect for year t and ηj,t,t+1 is an
independently and identically distributed random variable with mean zero and variance
σ2.
The dependent variable is the annual growth rate of regional real GDP per capita
in Hungary in the period 1994-2000. The independent variables are one year-lagged
values. The knowledge spillover effects are proxyed with a measure of regional FDI
intensity (the number of firms with foreign capital per 1000 inhabitant in the
region).The Herfindahl index (SPEC), captures the effect of regional manufacturing
specialization on regional growth. The share of employment in agriculture in total
regional employment (AGRIC), the share of employment in services in total regional
employment (SERV), and the share of export in regional industrial output (OPEN) are
control variables. In addition, we introduce a dummy variable for regions bordering
EU, (BORDEU) with the aim to control for time invariant factors specific to these
regions that matter for growth (such as possibilities for cross-border commuting).
Table A2.1 shows simple correlations between the variables. The correlation
coefficients indicate that multicollinearity is not a problem. We estimate the model
with pooled OLS, introducing gradually our control variables. We then control for
unobserved time specific region-invariant effects, and for time-invariant regional fixed
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