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



Table 4. Estimates of the input coefficients in the I-district fixed effects.

________________(1.1)

(1.2)

_____________(1.3)

(1.4)

(1.5)

(1.6)

Constant

5.1464 ***

5.5055 ***

5.5861***

4.7451***

4.7635***

4.8347***

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

(0.000)

R&D firms (1)

0.2635 ***

0.2825 ***

0.2751***

(0.000)

(0.000)

(0.000)

R&D firms (2)

0.0905***

0.0970***

0.0943***

(0.000)

(0.000)

(0.000)

R&D public

0.1902 ***

0.2441 ***

0.2485***

0.2061***

0.2328***

0.2187***

___________(0.001)

(0.000)

________(0.000)

(0.001)

(0.000)

(0.000)

Territory and

Territory and

Fixed effects

Territory

Industry

Industry

Territory

Industry

Industry

Fixed effects F-test

0.000

0.000

0.000

0.000

0.000

0.000

LR selection (Iambda=O)_________

_____________0.000

0.0001

________0.0002

0.0003

0.0003

0.0008

R2-ajd / Pseudo R2

0.297

0.3225

0.3283

0.3111

0.3315

0.3371

Log-L

-681.46

-670.24

-658.20

-679.82

-666.19

-654.26

Akaike

1370.91

1378.473

1390.40

1379.64

1370.39

1382.53

BIC

1388.50

1462.015

1553.08

1423.61

1453.93

1545.22

Number of obs________________

_____________806

_______806

___________806

_______806

806

_________806

(1) R&D imputed from regional data

(2) R&D from firm microdata

Notes: (a) Dependent variable = Patents per employee in the 2001-2006 period; (b) All variables are natural logarithms; (c) P-values are in parentheses and
asterisks represent statistical significance at 1% (***), 5% (**) and 10% (*); (d) Within group effect model estimates; (e) Fixed effects provided under the
restriction that ∑ α
i = 0, so that the dummy coefficients mean deviations from the averaged group effect (intercept); (f) Heckman two stages coefficients
adjusted for sample selection; (g) Robust Huber-White estimators when slight problems of heteroskedasticiy, collinearity or outliers are detected.

18



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