Industrial districts, innovation and I-district effect: territory or industrial specialization?



although with opposite signs: Machinery et al. (0.69), Chemistry and plastics
(-0.68) and Leather and footwear (-1.30). The other manufacturing LPS also
show conflicting signs depending on the specialization and the aggregated
territorial effect is positive and statistically non-significant.

Regarding services, the evidence again suggests that the territorial
dimension is the one that explains the negative and statistically significant (or
non-significant) coefficients more than the type of services in which the LPS
are specialized. For the other categories (Construction and Primary
activities), no distinction is possible.

5.4. Robustness and other issues

The basic results by territory are robust to different time periods and
indicators. In the previous periods, 1991-1995 and 1996-2001, the innovative
intensity of industrial districts was 33% and 35% above the national average.
Regarding the sensitivity of the indicator of innovation (patents), the results
are maintained with another two indicators that are available on a microdata
level covering the same period: (1) industrial designs and models from the
databases of the Spanish Patent and Trademark Office (OEPM), which is an
indicator of output and non-technological innovation; (2) and number of
grants and loans provided by the Centre for the Development of Industrial
Technology (CDTI), which can be interpreted as an input indicator (demand
for public loans to innovate). Industrial districts show in the three cases the
most important differential effect in relation to the Spanish average, clearly
above that of large metropolitan areas and manufacturing LPS of large firms.
Furthermore, the choice of patent indicators seems to be the most
conservative option since the differentials are much larger regarding designs
and CDTI loans.

Following Boix and Galletto (2008a), the models were re-estimated
including as explanatory variables the external economies in the function of
production of innovations. In this case, and as was expected, the fixed effects
became statistically non-significant as external economies are in the basis of
the effect.

Additional controls of the functional form of the model and the
relationship between the dependent and explanatory variables were
introduced, although a log-linear specification without quadratic or
interactive terms proved to be the most suitable. Spatial correlation in the
form of lag and error models on the basis of a matrix of contiguity was
considered although no robust evidence of these effects was found.

17



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