Klyngepolitik på et faktabaseret grundlag - Dansk Biotek

FORA RAPPORT KLYNGEPOLITIK PÅ ET FAKTABASERET GRUNDLAG
Table 2.2 Estimation results including concentration measure
Source SS df MS
Number of obs = 629
Model 2838.770 3 946.256 F( 3, 625) = 1266.36
Residual 467.0167 625 946.257 Prob > F = 0.000
R-squared = 0.859
Total 3305.787 628 5.264 Adj. R-squared = 0.858
Root MSE = 0.864
lnY Coef. Std. Err. t P>t [95% Conf. Interval]
lnL .899 0.0260 34.53 0.000 0.848 0.950
lnK .0497 0.021 2.40 0.017 0.009 0.090
LQ_rc .0339 0.067 0.51 0.611 -0.097 0.165
_cons 1.397 0.160 8.71 0.000 1.082 1.712
Source: Copenhagen Economics and Bureau van Dijk (2009)
Martin, Mayer and Mayneris (2008) argued, based on similar estimations, that the benefits
in terms of productivity gain resulting from proximity to other firms are already internalised
in the firms’ location choice. We argue, based on similar estimates, that concentration in a
cluster is important to the performance of firms, although only indicative, and the reason is
that the concentration of firms allow for provision of cluster based policies that add to the
positive externalities from co-location, and that for a given concentration, policies can make
a difference.
Our statistical analysis on BSR life-science data show a positive effect of increased cluster
concentration: increasing the life-science employment with the six most clustered life science
regions by 10 percent will increase firm productivity by 0.5 percent. Recall that this effect is
related to an increased concentration within the regions that are already concentrated. The
productivity impact of a hypothetical de-location of an average firm from a nonconcentrated
region to one of the six concentrated regions is like to be much higher.
Furthermore, comparisons with studies such as Henderson (2003) and Martin, Mayer and
Mayneris (2008) cannot be readily made, because these studies estimate the effect over time
while our data is only a cross-section dataset for one period. We would expect our LQ effect
to be much higher in a panel dataset which includes both data over time and both high LQ
and low LQ regions.
2.2. Investigating the impact of cluster framework conditions
Next, we have added a variable reflecting firm i’s perceived framework conditions as well as
the level of innovation they carry out through the explanatory variable FCi.
α β
Yi Ai
K L i i
= , where
A = f (...) + γFC
(3)
i
i
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