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

dummy variable. Results are inconclusive since for the first period
considered in the analysis (1985-1990) the dummy coefficient was positive
and for the second period (1991-1995) was negative.

Muscio (2006) centres on the industrial districts identified by
Garofoli (1989) in the Italian region of Lombardy. He uses firm data taken
from the author’s survey of eight manufacturing sectors and a probit
estimation. Results suggest that location in industrial districts increases the
probability of being innovative by 14%.

Boix and Galletto (2008a) use as unit of analysis 806 LPS divided
into seven typologies identified by applying to Spain the Sforzi-ISTAT
(2006) methodology. The I-district effect is contrasted using national and
international patents per employee and LPS and a fixed effect model by
typology of LPS. The results prove that industrial districts are the most
innovative LPS with an innovative intensity that is 47% above the mean and
the results are robust to other periods and indicators.

Although no research to date has simultaneously relied on the three
indicators (productivity, competitiveness and innovation), the separate
finding of large positive district effects on the three magnitudes suggests the
existence of a “magic triangle” where high innovative capacity (I-district
effect) generates higher levels of productivity, pushing competitiveness.
Changes in markets and the search for new market niches stimulate new
incremental and radical innovations in such a way that the triangle performs a
loop³.

Table 1. The measurement of the district effect in quantitative research

³ Innovations affect static efficiency reducing costs but also dynamic efficiency since
they allow for changes and improvements in products and their introduction into
markets.

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