Neighbouring ‘industrial atmosphere’ and manufacturing
location in Spanish municipalities*
Ángel Alañón, Rafael Myro, Belén Rey
Abstract
A bayesian spatial autorregresive probit model is estimated
to test if spatial pattern of firms location reflects the
existence of interurban agglomeration forces. Results
confirm that it does. Manufacturing diversity,
manufacturing specialisation, human capital, municipality
product as well as interurban agglomeration forces explain
the creation of new manufacturing units in the Spanish
municipalities (NUTS V) over the period 1980-1998.
Key words: industrial location, interurban agglomeration
forces, bayesian spatial probit models
JEL classification: L6; R3; R4
*This research was supported by Ministerio de Fomento
project “Efectos de la red viaria de alta capacidad sobre la
movilidad empresarial y el desarrollo regional”.

Dpto. Economía Aplicada I, Fac. CC. Económicas, Universidad Complutense de
Madrid, E-28223 Pozuelo de Alarcón; Fax: +34913942499; Telephone: +34913942470;
e-mail: angel@ccee.ucm.es
Dpto. Economía Aplicada II, Fac. CC. Económicas, Universidad Complutense de
Madrid, E-28223 Pozuelo de Alarcón; Fax: +34913942457; Telephone: +34913942476
e-mail: r.myro@ccee.ucm.es
 Dpto. Economía Aplicada II, Fac. CC. Económicas, Universidad Complutense de
Madrid, E-28223 Pozuelo de Alarcón; Fax: +34913942457; Telephone: +34913942635;
e-mail breylegi@ccee.ucm.es
1 Introduction
In order to choose a good location for their firms decision-makers could be not only
interested in traditional variables such as human capital, manufacturing diversity or
specialisation of a certain area of a given city or town. They may also be interested in
the neighbouring area beyond the borders of that city. That is, decision-makers may
be taking into consideration the existence of interurban agglomeration forces or
economies which would reduce the costs of their firms.
In this paper we apply the agglomeration economies concept to a interurban
framework –the 8071 municipalities of Spain (NUT V), within a two digits
manufacturing context. The most remarkable points of this approach are the inclusion
of new explanatory variables, such as the product of Spanish municipalities and the
interurban agglomeration forces, and, above all, the use of a bayesian spatial
autoregressive probit model to analyse the location decision process. This model
allows us to tackle spatial autocorrelation problems and the inclusion of interurban
agglomeration forces as an explanatory variable.
This paper is organised as follows. In section 2 the role of interurban agglomeration
forces in location decision is analysed and exploratory spatial test are performed to
find out if the creation of new manufacturing units is clustered or randomly
distributed in space. In section 3 we present a bayesian spatial autoregressive probit
model which explains the location of new manufacturing units for Spanish
municipalities over the period 1980-1998 and accounts for interurban agglomeration
economies. The model is estimated in section 4. Finally, the main conclusions are
exposed in section 5.
2 Location decisions, interurban agglomeration forces and spatial techniques
Traditional sources of Marshall’s agglomeration economies are usually assumed to
perform in a very reduced area, i.e. an industrial district. However, information
spillovers may flow between neighbouring cities or towns; some non-trade local
inputs may also be shared between the firms of the cities of a regional area; and,
thanks to commuting, a local skilled-labour pool may be no restricted to a city.
Examples of interurban agglomeration forces and related concepts can be found in
Scott (1988); Saxenian (1994), Ellison and Glaeser (1997), or in Suárez-Villa and
Alrod (1998).
Thus, the location of a firm may be good not only for the local industrial environment
or atmosphere. Firms may also take advantage of the interurban agglomeration forces.
So, decision-makers will take into consideration social and economic conditions of
neighbouring cities and towns to choose the right location for their firms.
As shown in Alañón (2001), agglomeration economies which become interurban have
an inter-territorial continuos and distance-decay clustering effect. That is, interurban
agglomeration forces are generated due to the clustering of firms in a not very large
regional area and its effects are inversely proportional to the distance between these
firms. Therefore, interurban proximity plays a key role in the development of
interurban agglomeration forces.
The influence of interurban agglomeration forces in the creation of new
manufacturing units can be measured using spatial statistics. If interurban
agglomeration forces matter the creation of new manufacturing units should exhibit
positive spatial autocorrelation, that is, they should be clustered in space. BB Joint
Count test for spatial autocorrelation or spatial dependence reflects if binary variables
are clustered or randomly distributed in space. BB Joint Count test are defined as
follows1:
BB  (1 / 2)
i
w
ij
LOC i LOC j
(1)
j
where wij is i-jth element of a spatial weights matrix W, with LOCi is set to 1 for
municipality i if new units of a given manufacturing activity have been created over
the period 1980-1998, and LOCi is set to 0 otherwise. Spatial weights matrix W
reflects reflects the potential interaction between the observation pair i and j. A
positive and significant z-value for this statistic indicates positive autocorrelacion,
i.e., similar values, either high values or low values, are more spatially clustered than
could be caused purely by chance (Anselin, 1992).
Table I shows BB Joint Count test for the creation of new manufacturing units in
8071 Spanish municipalities (NUTS V)2 over the period 1980-1998. Test have been
calculated for 11 manufacturing activities and 10 spatial weight matrices. These
spatial weight matrices represent kilometric distance thresholds in which interurban
1
See Anselin (1992) for technical details.
agglomeration forces are supposed to be active. Each spatial matrix has a suffix
which indicates the largest distance between municipalities to allow for interaction:
M50 means until 50 kilometres, M100 means 100 kilometres etc. Test are carried out
using SpaceStat v.1.90. Only results for M50, M100, M150 and M200 are reported
because the statistics for the rest of matrices are not significant. As expected as
distance increases spatial autocorrelation decreases, so spatial clustering or interurban
agglomeration forces are weaker. For 50 kilometres, all sectors show clustering
evidence, but food and tobacco – which cover basic human necessities, so they may
not depend heavily on such interurban agglomeration forces 3 - and first
transformation of metals – which involves high transport costs and are usually linked
to the mineral source. For M100 wood and furniture and other non metallic minerals
add to the manufacturing activities which does not show interurban agglomeration
forces. And for M150, only manufacturing of computers, office equipment and some
related activities shows a weak evidence of spatial autocorrelation. Summing up,
interurban agglomeration forces seem to vanish beyond 150 kilometres.
These results could biased due to the fact that LOC reflects the creation of new
manufacturing units over a large period of time (1980-1998). However, as shown in
appendix 2, if we apply BB Joint Count test to a sample of years, i.e. 1980, 1985,
1990, 1995 and 1995, results are very similar.
2
These municipalities cover the whole Spanish territory, excluding Ceuta and Melilla (two little cities
in the north of Africa). The number of municipalities is adjusted to account for the changes 1989-1991
inter census periods.
3
For example, most of towns, no matter the size, have a small bakery.
TABLE 1 JOIN COUNT BB TEST FOR SPATIAL AUTOCORRELATION
(1980-98)
(normal approximation -- non-free sampling)
VARIABLE
WEIGHT M50
Food and tobacco
Clothes and leather
Wood and furniture
Printing and paper
Chemistry
Other non metallic minerals
First transf. of metals
Machinery*
Computers, office equipment etc
Electric and electronic equipment
Transport equipment
BB
VARIABLE
WEIGHT M100
Food and tobacco
Clothes and leather
Wood and furniture
Printing and paper
Chemistry
Other non metallic minerals
First transf. of metals
Machinery*
Computers, office equipment etc
Electric and electronic equipment
Transport equipment
BB
VARIABLE
WEIGHT M150
Food and tobacco
Clothes and leather
Wood and furniture
Printing and paper
Chemistry
Other non metallic minerals
First transf. of metals
Machinery*
Computers, office equipment etc
Electric and electronic equipment
Transport equipment
BB
VARIABLE
WEIGHT M200
Food and tobacco
Clothes and leather
Wood and furniture
Printing and paper
Chemistry
Other non metallic minerals
First transf. of metals
Machinery*
Computers, office equipment etc
Electric and electronic equipment
Transport equipment
BB
* transport equipment not included
Z-VALUE
PROB
135956
-3.72
65548
18.29
114582
5.68
25081
28.69
41797
22.85
48814
10.16
117519
-0.58
31532
22.65
4449
37.26
16513
23.99
14411
24.38
0.999901
0.000000
0.000000
0.000000
0.000000
0.000000
0.721207
0.000000
0.000000
0.000000
0.000000
Z-VALUE
PROB
416156
-13.88
181911
3.43
329382
-8.78
58391
7.14
105742
4.07
131633
-4.23
357690
-10.58
80215
5.56
8569
13.87
38922
5.90
3201
4.36
1.000000
0.000297
1.000000
0.000000
0.000023
0.999988
1.000000
0.000000
0.000000
0.000000
0.000006
Z-VALUE
PROB
771904
-19.99
308874
-6.71
583478
-17.55
87273
-5.39
167150
-7.78
224876
-12.30
655470
-17.23
129716
-4.62
11612
2.29
59347
-4.48
48333
-5.55
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
0.999998
0.010986
0.999996
1.000000
Z-VALUE
PROB
1183494
-22.92
450025
-11.59
860950
-22.53
116005
-11.54
228139
-14.16
325746
-16.56
998790
-20.39
179846
-10.19
13869
-3.95
80957
-9.29
63822
-10.78
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
1.000000
0.999962
1.000000
1.000000
3 A bayesian spatial autoregressive probit model for the location of
manufacturing units
The existence of spatial autocorrelation invalidate the use of most of usual Statistics
and Econometrics techniques, such us ordinary least squares4. So, to obtain reliable
estimates spatial autocorrelation needs to be treated properly, for example including
that spatial autocorrelation in model specification. One of the most common
alternatives is including a spatially lagged dependent variable, Wy, as one of the
explanatory variables, that is, a spatial lag model or spatial autoregressive model
(SAR):
y  Wy  X  
(2)
where y is a nx1 vector of observations on the dependent variable, Wy is a nx1 vector
of spatial lags for the dependent variable,  is the spatial autoregressive coefficient, X
is a nxk matrix of observations on the (exogenous) explanatory variables with
associated a kx1 vector of regression coefficients  , and  is a nx1 vector of normally
distributed random error terms, with means 0 and constant (homoskedastic) variances
2 (Anselin, 1988).
As our aim is to estimate a model which explains location decisions, that is, our
dependent variable is the creation of new manufacturing units in a given municipality,
the spatially lagged dependent variable, Wy, of SAR models has an important
economic and spatial meaning. As expression (2) and its reduced form, expression
(3), clearly express, it accounts for interurban agglomeration forces, since it makes
depend what happens in a given municipality on what happen in the neighbouring
municipalities:
y  ( I  W ) 1 X  ( I  W ) 1 
(3)
Location decision models are usually estimated using limited dependent variable
models, i.e., logit, probit or poisson specifications5. However, as far as we know,
there is no mention about spatial autocorrelation in
4
industrial location models
See Anselin (1988) for more information about spatial autocorrelation and Spatial Econometrics
techniques.
5
See Arauzo (2002) or Holl (2003).
literature6. A possible alternative is the specification of a bayesian spatial
autoregressive probit7. In this case the usual specification of SAR models differs,
since
spatial
autorregressive
probit
models
are
heteroskedastic:
  N (0,  2V ), V  diag (v1 ,v 2 ,...., vn ) . It includes spatial heterogeneity in the model,
which makes standard probit estimation inconsistent8.
Therefore, changing the notation of expression (2) new manufacturing units location
decision could be explained using a single equation model (4):
LOC ij  f ( HC i , LQij , DI i , MPi , IAFi )
(4)
LOCij is a binary variable which is set to 1 if a unit of manufacturing activity j has
been located in municipality i over the period 1980-1998 and to 0 otherwise.
HCi is a human capital index which measures the percentage of population over ten
years old with at least a secondary school degree in municipality i in 1991. The
expected sign is positive since it reflects labour market’s qualification.
LQij is the classic location quotient of manufacturing activity j for municipality i
which measures specialisation in a manufacturing activity j in 1990. It represents the
advantages of geographical specialisation: the traditional Marshallian externalities or
MAR’s type (Marshall, Arrow and Romer) (Glaeser et al., 1992). LQij, is defined as
follows:
LQi , j  ( Eij / E I ) /( E J / ET )
(5)
where Eij accounts for total employment in manufacturing activity j in municipality i,
Ei for total employment in municipality i, EJ for national employment in
manufacturing activity j, and ET total national employment in all manufacturing
activities. Its expected sign is positive.
6
Spatial Econometrics techniques are not commonly used by economists yet (Anselin and Florax,
1994).
7
“Note that when  does not follow a normal distribution, transformed multivariate random variable
( I  W )1 is not necessarily well defined. For example, this in the case for a logit specification
and for models of counts (Poisson models), where the resulting multivariate specification is
intractable” (Anselin, 2002, p.8).
8
“In the case of a standard normal  i , i.,e., for a spatial probit model, the random vector u will be
multivariate normal with a covariance matrix . An important consequence of this complex covariance
structure is that the margninal ui will be heteroskedastic, This makes standard probit estimation
inconsistent” (Anselin, 2002, p. 8).
DIi is a manufacturing diversification index for municipality i in 1990. The expected
sign of this variable is positive since manufacturing diversity may reflect the
existence of interindustrial external economies, such as Jacobs type (Glaeser et al.,
op. cit.), and, also, the creation of new plants is biased towards diverse cities
(Duranton and Puga, op. cit.). This index is based on the correction for differences in
sectoral employment shares at the national level of the inverse of a HirschmanHerfindahl index proposed in Duranton and Puga (2000):
DI i  1 /  / s ij  s j /
(6)
j
where, sij is the share of manufacturing activity j in manufacturing employment in
municipality i, and sj is the share of manufacturing activity j in total national
manufacturing employment.
MPi is the gross product of municipality i in 1991. This variable reflects the volume
of economic activity of the municipality, so its expected sign is positive. It has been
calculated regressing Spanish provinces (NUTs III) gross value added on some
agglomeration and production variables, (x1, x2, x3), using Spatial Econometrics
techniques to overcome spatial autocorrelation problems. MPi was obtained
multiplying the estimated coefficients (1, 2, and 3), by the municipal values of (x1i,
x2i, x3i)9.
Finally, IAFi are the interurban agglomeration forces which affect to municipality i,
measured as the spatially lagged dependent variable.
The source for manufacturing units creation is REI, Registro de Establecimientos
Industriales (Spanish Industrial Establishments Register)10. Employment and capital
human data sources are Censo de Locales 1990 and Censo de Personas 1991 (Spanish
Census 1990-1991)11.
4 Results
In this section we estimate the bayesian spatial autoregressive probit model presented
above. To stress the importance of accounting for spatial autocorrelation, we compare
9
See Alañón (2001, 2003) for more details.
The authors are greatly indebted to the previous work of Josep María Arauzo, who collected this
dataset.
11
Although there are new census data for population (Censos de Población y Viviendas 2001), we can
not obtain new data for employment at a municipality level since Censo de Locales 1990 is the last one
available.
10
its estimates to the ones obtained by a non spatial probit model which does not
include interurban agglomeration forces as an explanatory variable. Spatial
estimations are carried out using MATLAB R.12, via Markov Chain Monte Carlo
methods (MCMC) as proposed in Lesage (1997). The spatial weights matrix, W, is a
binary contiguity matrix which elements, wim, reflects if the municipalities i and m
have a common border12. Eviews 4 has been used to estimate non spatial models.
In table 2 we can see how coefficient estimates for spatial models do not apparently
differ too much from non spatial ones. However it is usually expected that non spatial
models produce larger coefficient estimates since they ignore spatial dependence and
spatial effects are attributed to the rest of explanatory variables in these non spatial
models (Smith and LeSage, 2002). It seems that most of spatial effects in the non
spatial model are included in the municipality product coefficient estimates, which
are larger than in the spatial case. Coefficient estimates for municipality product are
insignificant in the spatial model for food and tobacco, wood and furniture and first
transformation of metals manufacturing activities and weakly significant in clothes
and leather and other non metallic minerals manufacturing activities, whereas in the
non spatial models that coefficient is always significant. As suggested in section 2, it
could mean that food activities are basic human necessities and they do not depend
on municipality product and first transformation of metals manufacturing could be
deeply rooted in the mineral sources due to transport cost.
The rest of coefficients in the spatial model are highly significant, including , which
represent interurban agglomeration forces. Therefore, what happen in the
neighbouring municipalities, the interurban agglomeration forces, matter in location
decision processes.
In the non-spatial model only human capital coefficient is not significant for food and
tobacco manufacturing activities, it might suggest that these activities do not require
skilled labour. However, as stated in section 3, non-spatial estimations are
inconsistent.
12
As we are using a different program we can not use the same spatial weights matrices that in section
2. Anyway, “There is very little formal guidance in the choice of the ‘correct’ spatial weights in any
given application…. In practice, model validation techniques, such as comparison of goodness-of-dit,
or cross-validation, may provide ways to eliminate bad choices.” (Anselin, 2002, p. 19).
Table 2. SPATIAL AND NON SPATIAL ESTIMATES OF THE MODEL (1/2)13
Food and tobacco
SPATIAL MODEL
Coef.
P-Level
-1.989960
0.000000
CONST
1.378667
0.000000
HC
0.019160
0.000000
LQ
2.323121
0.000000
DIV
0.000416
0.302000
MP
0.313976
0.000000
IAF
Clothes and
SPATIAL MODEL
leather
Coef.
P-Level
-2.594266
0.000000
CONST
1.552214
0.000000
HC
0.373039
0.000000
LQ
1.857059
0.000000
DIV
0.001778
0.015000
MP
0.274776
0.000000
IAF
Wood and
SPATIAL MODEL
furniture
Coef.
P-Level
-2.670987
0.000000
CONST
2.628702
0.000000
HC
0.109634
0.000000
LQ
2.340870
0.000000
DIV
0.000307
0.401000
MP
0.304295
0.000000
IAF
Printing and
SPATIAL MODEL
paper
Coef.
P-Level
-3.236523
0.000000
CONST
3.065153
0.000000
HC
0.237514
0.000000
LQ
1.610273
0.000000
DIV
0.006610
0.000000
MP
0.189534
0.000000
IAF
Chemistry
SPATIAL MODEL
Coef.
P-Level
-3.003741
0.000000
CONST
2.711943
0.000000
HC
0.237320
0.000000
LQ
1.893904
0.000000
DIV
0.002300
0.009000
MP
0.244725
0.000000
IAF
Other non
SPATIAL MODEL
metallic minerals Coef.
P-Level
-2.709488
0.000000
CONST
1.964786
0.000000
HC
0.152384
0.000000
LQ
1.922727
0.000000
DIV
0.002213
0.015000
MP
0.252655
0.000000
IAF
13
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-1.645902
-26.04835
0.0000
0.237805
1.214430
0.2246
0.045595
6.310680
0.0000
1.630136
25.55807
0.0000
0.283780
19.46022
0.0000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-2.520284
-31.85907
0.0000
0.805106
3.727101
0.0002
0.341255
29.44734
0.0000
1.696612
22.96632
0.0000
0.060587
13.92180
0.0000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-2.387535
-35.56686
0.0000
1.552676
7.940431
0.0000
0.089711
12.79254
0.0000
1.934568
31.98060
0.0000
0.017400
36.44666
0.0000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-3.547762
-33.00544
0.0000
2.823758
10.97818
0.0000
0.026213
4.307667
0.0000
1.500295
17.49725
0.0000
0.114461
22.52102
0.0000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-3.022071
-33.47779
0.0000
1.945728
8.514447
0.0000
0.232200
13.70776
0.0000
1.729642
21.93607
0.0000
0.081341
17.58169
0.0000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-2.532196
-33.38047
0.0000
1.064641
5.031806
0.0000
0.129513
19.75910
0.0000
1.711081
25.18040
0.0000
0.056265
15.87300
0.0000
Complete estimations results for the spatial model are shown in appendix I.
Table 2. SPATIAL AND NON SPATIAL ESTIMATES OF THE MODEL (2/2)
First transf. of
metals
CONST
HC
LQ
DIV
MP
IAF
Machinery (excl.
transport
equipment)
CONST
HC
LQ
DIV
MP
IAF
Computers, office
equipment etc
CONST
HC
LQ
DIV
MP
IAF
Electric and
electronic
equipment
CONST
HC
LQ
DIV
MP
IAF
Transport
equipment
CONST
HC
LQ
DIV
MP
IAF
SPATIAL MODEL
Coef.
P-Level
-2.391933
0.000000
1.984242
0.000000
0.063970
0.000000
2.418330
0.000000
0.000358
0.294000
0.253700
0.000000
SPATIAL MODEL
Coef.
P-Level
-2.944836
0.000000
2.818123
0.000000
0.058006
0.000000
1.577736
0.000000
0.007088
0.000000
0.209812
0.000000
SPATIAL MODEL
Coef.
P-Level
-3.009778
0.000000
2.238432
0.000000
0.044042
0.001000
0.648717
0.000000
0.011996
0.000000
0.067316
0.005000
SPATIAL MODEL
Coef.
P-Level
-3.099496
0.000000
2.845495
0.000000
0.349033
0.000000
1.237076
0.000000
0.010146
0.000000
0.170379
0.000000
SPATIAL MODEL
Coef.
P-Level
-3.098176
0.000000
2.864999
0.000000
0.349346
0.000000
1.233071
0.000000
0.009803
0.000000
0.167611
0.000000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-2.080747
-29.17844
0.0000
1.117730
5.143238
0.0000
0.065194
8.933261
0.0000
1.952301
22.70733
0.0000
0.013600
3.925442
0.0001
NON SPATIAL MODEL
Coef.
Z-Statistic
-2.954426
2.230605
0.067639
1.447518
0.060668
-34.78775
9.925412
8.736909
20.37964
16.34985
P-Level
0.0000
0.0000
0.0000
0.0000
0.0000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-4.099518
-29.43870
0.0000
3.696380
11.29634
0.0000
0.037899
4.204865
0.0000
1.186799
13.51803
0.0000
0.020673
12.87121
0.0000
Coef.
NON SPATIAL MODEL
Z-Statistic
P-Level
-3.082232
1.754618
0.030280
1.429223
0.052217
-32.28912
6.939006
6.382476
18.50710
17.02519
0.0000
0.0000
0.0000
0.0000
0.0000
NON SPATIAL MODEL
Coef.
Z-Statistic
P-Level
-3.518302
-32.41856
0.0000
2.826310
10.52510
0.0000
0.269380
11.56270
0.0000
1.341146
16.27812
0.0000
0.056965
17.71614
0.0000
5 Conclusions
This research has focused on the role of interurban agglomeration forces in the
creation of new manufacturing units in the Spanish municipalities. Exploratory
analysis has shown that the creation of new manufacturing units at two digits level
exhibits an spatially autocorrelated pattern. This spatial behaviour has two important
implications. On the one hand, it stress the key role of interurban agglomeration
forces in the creation of new manufacturing units, which seems to vanish beyond 150
kilometres. And, in the other hand, as the existence of spatial autocorrelation
invalidates the use of traditional estimation models, to assess the role of interurban
agglomeration forces in location decision processes, spatial autocorrelation must be
treated properly. In order to do so, a bayesian spatial autoregressive model is
estimated for 11 manufacturing activities, where the explanatory variables are human
capital, manufacturing diversification and specialisation, municipality product and
interurban agglomeration forces, measured as the spatially lagged dependent variable.
Estimation results show that interurban agglomeration forces coefficients estimates,
as well as most of the other variables coefficients estimates, are significant. Spatial
heterogeneity is included in the model due to the heteroskedastic error term.
Comparison between spatial and non spatial estimates underlines again the
importance of taking into account spatial autocorrelation since non spatial coefficients
estimates signficance differs for some explanatory variables.
References
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determinantes, doctoral thesis, unpublished, Universidad Complutense de Madrid,
Madrid.
Alañón, A. (2002): “Estimación del valor añadido per cápita de los municipios
españoles en 1991 mediante técnicas de econometría espacial”, Ekonomiaz, nº 51, pp.
172-194.
Anselin, L. (1988): Spatial econometrics: Methods and models, Kluwer Academic,
Dordrecht.
Anselin, L. (1992): SpaceStat Tutorial: a book for using SpaceStat in the analysis of
spatial data, University of Illinois, Urbana-Champaign.
Anselin, L. and Florax, R. (1994): “New Directions in Spatial Econometrics:
Introduction”, in Aselin, L. and
Florax, R. (Eds): New Directions in Spatial
Econometrics, Springer, Berlin.
Anselin, L. (2002): “Under the Hood. Issues in the Specification and Interpretation of
Spatial Regression Models”, Mimeo.
Arauzo, J. M. (2002): “Determinants of Industrial Location. An application for
Catalan Municipalities”, Estudios sobre la Economía Española, nº 138, FEDEA,
Madrid.
Duranton, G. and Puga, D. (2000): “Diversity and specialisation in cities: why, where
and when does it matters?”, Urban Studies, vol. 37, nº 3, pp. 533-555.
Ellison, G. and Glaeser, E.L. (1997): “Geographic Concentration in US
Manufacturing Industries: A Dartboard Approach”, Journal of Political Economy,
105, 889-927.
Glaeser, E., Kallal, H.; Scheinkman, J.; y Shleifer, A (1992): “Growth in cities”,
Journal of Political Economy, nº 100, pp. 1126-1152.
Holl, A. (2003): “Manufacturing location and impacts of road transport infrastructure:
Empirical evidence from Spain”, Mimeo.
LeSage, J.P. (1997): “Bayesian Estimation of Spatial Autoregressive Models”,
International Regional Science Review, vol. 20, number 1&2, pp. 19-35.
Saxenian, A. (1994): Regional Advantage: Culture and Competition in Silicon Valley
and Route 128, Harvard University Press, Cambridge (Mass.).
Scott, A. J. (1988): New industrial spaces, Pion, London.
Smith, T. and LeSage, J.P. (2002): “A Bayesian Probit Model with Spatial
Dependencies”, unpublished document available in www.spatial-econometrics.com.
Suarez-Villa, L. and Walrod, W. (1998): “Operational Strategy, R&D and
Intrametropolitan Clustering in a Polycentric Structure: The advanced Electronics
Industries of the Los Angeles Basin”, Urban Studies, 34, 1343-80.
Appendix I Complete Spatial Estimations Results
Food and tobacco
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.7144
sige
=
1.2297
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
4205, 3866
ndraws,nomit
=
1100,
100
time in secs
= 1307.6600
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
hc
lq
di
mp
rho
-1.989960
1.378667
0.019160
2.323121
0.000416
0.313976
0.081838
0.239491
0.007305
0.074524
0.000838
0.019679
0.000000
0.000000
0.000000
0.000000
0.302000
0.000000
Clothes and leather
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.7818
sige
=
1.2420
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
5770, 2301
ndraws,nomit
=
1100,
100
time in secs
= 1262.8500
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-2.594266
0.093524
0.000000
hc
1.552214
0.266734
0.000000
lq
0.373039
0.016684
0.000000
di
1.857059
0.083154
0.000000
mp
0.001778
0.001535
0.015000
rho
0.274776
0.018902
0.000000
Wood and furniture
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.7867
sige
=
1.2420
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
4711, 3360
ndraws,nomit
=
1100,
100
time in secs
= 1265.9800
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-2.670987
0.093758
0.000000
hc
2.628702
0.249317
0.000000
lq
0.109634
0.008686
0.000000
di
2.340870
0.078260
0.000000
mp
0.000307
0.000830
0.401000
rho
0.304295
0.017776
0.000000
Printing and paper
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.6610
sige
=
1.2429
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
6833, 1238
ndraws,nomit
=
1100,
100
time in secs
= 1402.7400
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-3.236523
0.102350
0.000000
hc
lq
di
mp
rho
3.065153
0.237514
1.610273
0.006610
0.189534
0.268698
0.024299
0.078990
0.001883
0.020382
0.000000
0.000000
0.000000
0.000000
0.000000
Chemistry
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.7661
sige
=
1.2391
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
6336, 1735
ndraws,nomit
=
1100,
100
time in secs
= 1274.3300
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-3.003741
0.110826
0.000000
hc
2.711943
0.263692
0.000000
lq
0.237320
0.026539
0.000000
di
1.893904
0.084804
0.000000
mp
0.002300
0.001658
0.009000
rho
0.244725
0.021712
0.000000
Other non metallic minerals
>> Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.7301
sige
=
1.2295
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
5984, 2087
ndraws,nomit
=
1100,
100
time in secs
= 1340.1300
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-2.709488
0.098581
0.000000
hc
1.964786
0.236774
0.000000
lq
0.152384
0.010032
0.000000
di
1.922727
0.081521
0.000000
mp
0.002213
0.001567
0.015000
rho
0.252655
0.019948
0.000000
First transf. of metals
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.7471
sige
=
1.2230
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
4538, 3533
ndraws,nomit
=
1100,
100
time in secs
= 1238.3500
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-2.391933
0.077107
0.000000
hc
1.984242
0.223500
0.000000
lq
di
mp
rho
0.063970
2.418330
0.000358
0.253700
0.010493
0.071780
0.000609
0.018868
0.000000
0.000000
0.294000
0.000000
Machinery (excluding transport equipment)
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.4754
sige
=
1.2195
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
6587, 1484
ndraws,nomit
=
1100,
100
time in secs
= 1564.7200
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-2.944836
0.094791
0.000000
hc
2.818123
0.256793
0.000000
lq
0.058006
0.009935
0.000000
di
1.577736
0.072484
0.000000
mp
0.007088
0.001624
0.000000
rho
0.209812
0.022133
0.000000
Computers, office equipment etc.
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
-0.3923
sige
=
1.2757
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
7669,
402
ndraws,nomit
=
1100,
100
time in secs
= 2563.8200
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-3.009778
0.124872
0.000000
hc
2.238432
0.326019
0.000000
lq
0.044042
0.011767
0.001000
di
0.648717
0.077676
0.000000
mp
0.011996
0.001507
0.000000
rho
0.067316
0.024917
0.005000
Electric and electronic equipment
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.3255
sige
=
1.2345
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
7055, 1016
ndraws,nomit
=
1100,
100
time in secs
= 1456.6800
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-2.827573
0.112460
0.000000
hc
2.095427
0.279293
0.000000
lq
0.120333
0.019785
0.000000
di
mp
rho
1.348573
0.009516
0.202373
0.076766
0.001991
0.022206
0.000000
0.000000
0.000000
Transport equipment
Bayesian spatial autoregressive Probit model
Dependent Variable =
loc
psuedo R-sqr
=
0.3471
sige
=
1.2447
Nobs, Nvars
=
8071,
5
# 0, 1 y-values =
7135,
936
ndraws,nomit
=
1100,
100
time in secs
= 1687.1500
min and max rho =
-1.0000,
1.0000
***************************************************************
Variable
Coefficient
Std Deviation
p-level
const
-3.099496
0.119824
0.000000
hc
2.845495
0.278970
0.000000
lq
0.349033
0.038929
0.000000
di
1.237076
0.075909
0.000000
mp
0.010146
0.001852
0.000000
rho
0.170379
0.022324
0.000000
APENDIX 2. JOIN COUNT BB TEST FOR SPATIAL AUTOCORRELATION (1980, 1985, 1990, 1995 and 1998)
(normal approximation -- non-free sampling)
SPATIAL WEIGHTS MATRIX = M50
1980
VAR BB Z-VALUE PROB
I
3713 6.33 0.00000
II
1502 7.33 0.00000
III
4900 9.60 0.00000
IV
256 5.84 0.00000
V
1088 9.97 0.00000
VI
1211 6.69 0.00000
VII
5412 1.80 0.035226
VIII
519 11.55 0.00000
IX
1 -0.93 0.825731
X
314 5.86 0.00000
XI
48 1.48 0.06843
1985
BB
7057
4749
9013
1206
2813
1306
7244
1638
110
290
185
SPATIAL WEIGHTS MATRIX = M100
1980
VAR BB Z-VALUE PROB
I
9801 -0.76 0.777448
II
4204 3.16 0.000778
III
12239 0.03 0.488020
IV
550 0.43 0.333490
V
2447 1.68 0.045584
VI
3064 0.76 0.221087
VII
15139 -4.14 0.999983
VIII
1126 4.22 0.000012
IX
5 -1.12 0.870145
X
861 3.42 0.000306
XI
104 -1.30 0.903425
I
II
III
*
Food and tobacco
Clothes and leather
Wood and furniture
excluding transport equipment
IV
V
VI
Z-VALUE PROB
8.35 0.00000
20.76 0.00000
13.45 0.00000
27.82 0.00000
20.50 0.00000
9.52 0.00000
27.70 0.00000
24.51 0.00000
13.95 0.00000
16.38 0.00000
17.33 0.00000
1995
BB Z-VALUE PROB
5804 8.64 0.00000
2676 21.68 0.00000
7394 19.89 0.00000
1798 28.64 0.00000
2558 34.43 0.00000
1366 7.87 0.00000
9651 27.70 0.00000
2096 26.96 0.00000
95 11.86 0.00000
548 32.08 0.00000
373 13.90 0.00000
1998
BB Z-VALUE PROB
9617 76.34 0.00000
2271 60.83 0.00000
3311 53.78 0.00000
944 49.61 0.00000
1598 63.70 0.00000
901 32.34 0.00000
4903 80.03 0.00000
1516 64.58 0.00000
63 15.11 0.00000
406 42.29 0.00000
282 27.67 0.00000
1985
BB Z-VALUE PROB
19767 -1.65 0.95152
10708 12.48 0.00000
21600 3.97 0.00003
2148 9.44 0.00000
5570 9.34 0.00000
2921 2.49 0.00627
17781 1.56 0.05901
3058 12.92 0.00000
163 4.42 0.00000
628 4.47 0.00000
385 3.50 0.00022
1990
BB Z-VALUE PROB
15651 -1.31 0.90523
11730 8.60 0.00000
23611 0.69 0.24376
4059 9.56 0.00000
5259 6.67 0.00000
5134 0.60 0.27309
24136 0.80 0.21056
4274 7.95 0.00000
124 3.68 0.00011
858 3.98 0.00003
1591 5.13 0.00000
1995
BB Z-VALUE PROB
14318 -1.60 0.94546
5947
8.65 0.00000
16817
4.44 0.00000
3193
9.24 0.00000
4895 14.07 0.00000
3319
0.81 0.20661
20393
6.78 0.00000
3757
7.67 0.00000
138
3.71 0.00010
783 11.60 0.00000
608
2.67 0.00375
1998
BB Z-VALUE PROB
22540 50.31 0.00000
4817 39.53 0.00000
7020 31.74 0.00000
1559 24.96 0.00000
2915 36.23 0.00000
1820 18.59 0.00000
9258 42.62 0.00000
2737 36.73 0.00000
100 8.47 0.00000
597 20.71 0.00000
512 16.66 0.00000
Printing and paper
Chemistry
Other non metallic minerals
VII
VIII
IX
Z-VALUE PROB
4.93 0.00000
28.27 0.00000
17.96 0.00000
26.55 0.00000
27.12 0.00000
11.58 0.00000
13.27 0.00000
31.30 0.00000
13.02 0.00000
10.35 0.00000
8.84 0.00000
1990
BB
6188
4857
9649
2150
2478
2175
9651
2190
99
510
841
First transf. of metals
X
Machinery*
XI
Computers, office equipment etc.
Electric and electronic equipment
Transport equipment