Knowledge, Innovation and Agglomeration - regionalized multiple indicators and evidence from Brazil



and operational occupations (28.3 and 42.6 respectively), reinforcing the significance of regional
deconcentration of manufacturing plants and hence of technical and operational occupations.

The second type of indicator is designed to capture the tacit knowledge embodied in the
routines of innovative firms. The regional distribution of this type of knowledge, which lies at the
root of innovation activities, can be estimated using a breakdown of the number of innovative
firms by region based on a special tabulation of data from Pintec, although it is difficult to
regionalize this information.

The innovation rate for Sao Paulo State - defined as the number of innovative firms as a
proportion of the total - is close to the averages for other parts of Brazil, which are only slightly
higher. The differences between regions of the state are less significant in terms of these average
rates than in terms of the innovation patterns characteristic of each region. In other words, the
differences are more significant in terms of the sectors that innovate and the type of innovation
occurring in each region. Thus although the innovation rates for the various regions of Sao Paulo
State are relatively high by Brazilian and international standards, they are less significant in terms
of the importance and scope of their results. The highlights among regions with innovations in
both products and processes that are relevant for the market, and not just for the firms involved,
are the Para⅛a Valley region (with Sao José dos Campos as its hub), metropolitan Sao Paulo, the
so-called macro-metropolitan region of the state, and Campinas (see
Chart 1).

It should be noted, however, that one of the main limitations of the National and Regional
Systems of Science, Technology & Innovation is the absence of comprehensive information
regarding the numbers of skilled professionals occupying higher technical and technological
functions. The Pintec statistics only partially address this important gap in the national and
regional systems of statistics, since the Pintec database refers only to workers in firms that
declare themselves innovative, a universe that does not coincide with that of firms engaged in
technological activities. Hence the relevance of the data indicating a very high concentration of
workers in technological occupations in a few regions, especially metropolitan Sao Paulo,
Campinas, and the Para⅛a Valley - data which illustrate the dissociation between technological
activities and innovation. This observation confirms much of what is known about the
distribution of economic activities in Sao Paulo State, showing that it is very uneven in terms of
geographical location: production facilities and firms have advanced into the interior of the state
while corporate functions requiring higher levels of skill and qualification are still relatively
concentrated around one or two cities and along a few geographical axes.

The third type of indicator is based on information about codified knowledge, represented
by patents registered with INPI and USPTO. Using these two databases we produced
regionalized indicators for the number of patents per 100,000 inhabitants, technological
specialization, and patenting of strategic technologies.9

According to the results for the numbers of patents per 100,000 inhabitants based on
information from INPI for the period 1999-2001, as shown in
Map 2, seven of the 63 micro-
regions in the state stand out for having technological densities above 20 patents per 100,000
inhabitants. The Sao Paulo micro-region is the most important both in absolute numbers (5,105
patents, or 61% of the total) and in patents per capita (about 40 per 100,000 inhabitants). Next in
order of density come the Sao Carlos, Campinas, .Iundiiu, Limeira, Itapecerica da Serra, and
Ribeirao Preto micro-regions. All the rest except Marilia are located along the Anhanguera and

9 The authors acknowledge the research assistance by Rogério Vicentim, M. A. student at UNICAMP, for calculating
these indicators.



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