Industrial Cores and Peripheries in Brazil



Table 5: Variables

V ariable/Description

Source

IVA

Industrial Value-Added (R$ millions)

PIA 2000

EXP

Industrial Exports (R$ millions)

SECEX

IMP

Industrial Imports (R$ millions)

SECEX

BI

Intermediate share in total IVA

PIA 2000

BCD

Capital and durable goods share in total IVA

PIA 2000

BCND

Non-durable consumption share in total IVA

PIA 2000

EXTRA

Mining share in total industrial activity

PIA 2000

QLA

Location Quotient, type A manufacture a

PIA - PINTEC (2000)

QLB

Location Quotient, type B manufacture b

PIA - PINTEC (2000)

QLC

Location Quotient, type C manufacture c

PIA - PINTEC (2000)

ESGT

Sewage Connection to the sanitary system (% houses)

Atlas do Desenvolvimento
Humano

E25

Upper Schooling (share of population above 25 years-old
with 12 or more years of education)

Atlas do Desenvolvimento
Humano

POP

Population

SIM BRASIL

CTRPSP

Transport cost to the city of Sao Paulo

IPEADATA

CTRPCAP

Transport cost to state capital

IPEADATA

NRM

Non-Metropolitan Dummy

IBGE

a Type A: firms which innovate, differentiate products and export.

b Type B: firms that specialize in standardized products and that export.

c Type C: firms which do not differentiate products, have lower productivity and do not export.

The spatial econometric models allow distinguish two types of spatial correlation, which result
in multiplier effects both locally and globally. Global effects are recorded using SAR (spatial
autoregressive) models and local effects using SMA (spatial moving average) models.

The two SAR models most commonly used in spatial econometrics are the spatial
autoregressive error and the spatial lag models. Global spatial dependence in error terms is taken into
account using spatial autoregressive error terms, as follows:

Y = Xβ + ε                                                               (1)

ε = λWε + u                                                             (2)

Y = Xβ + (I-λW)-1 u                                                            (3)

Where ε is the autocorrelated error term and u é is an i.i.d. error term. The spatial error model is
suitable when the variables that are not included in the model but are present in the error terms are
spatially autocorrelated. The spatial lag model is specified as follows:

Y = ρWy + Xβ +ε                                                      (4)

15



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