Industrial Cores and Peripheries in Brazil



Table 7: Exports (Model: IV 2SLSs)

Independent variables

OLS

SAR

W_EXP

0.04 *

Constant

-2.38 NS

-5.72 NS

QLA

1.27 NS

1.16 NS

QLB

0.98 NS

0.89 NS

QLC

-5.74 *

-5.61 *

E25

1.64 ***

1.55 **

POP

0.24 ***

0.24 ***

ESGT

-0.02 NS

-0.02 NS

NRM

-0.57 NS

2.41 NS

BCD

62.61 ***

59.79 ***

BI

6.41 NS

5.88 NS

BCND

2.27 NS

2.34 NS

CTSPM

0.00 NS

-0.67 NS

CTCAPM

0.00 NS

0.04 NS

R_POS1

793.21 ***

791.53 ***

R NEGl_____________

________-340.50 ***

-336.29 ***

R2aj. / R2buse

0.49

0.49

Jarque-Bera

2499383055 ***

Koenker-Basset____________

_________166.96 ***

Specification Tests

Moran

1.63 ***

LM (erro)

2.28 NS

0.11 NS

LM robusto (erro)

0.09 NS

LM (lag)

3.45 **

LM robusto (lag)___________

__________1.25 NS

*significant, 10%; **significant, 5%; ***significant, 1%

The specification tests have shown that the spatial lag model is the most appropriate13. As
anticipated, the location quotient variable (QL) indicates that industrial exports are negatively
correlated with type C agglomerations. By definition, type C are not exporting companies and therefore
their presence is small in environments where exports are higher. From the viewpoint of industrial and
regional policies, this is an important aspect, since large municipality-based concentrations of type C
companies do not share the same economic spaces with agglomerations of exporting companies into
which all type A and most type B companies fit. Such spatial “segregation” limits the spillover effects,
captured by the statistically significant spatial lag variable, which could help in the competitive
catching-up of type C companies.

13 Variables R_POS1 and R_NEG1 are dummies built from OLS residuals, intended to capture outliers that might affect
model estimations.

21



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