The aim of this paper is to present a further contribution to the analysis of absolute convergence, associated with the neoclassical theory, of the manufactured industry productivity at regional level and for the period from 1995 to 1999 (1)(Martinho, 2011a). This paper pretends, also, to analyze the importance which the natural advantages and local resources are in the manufacturing industry location, in relation with the "spillovers" effects and industrial policies. To this, we estimate the Rybczynski equation matrix for the various manufacturing industries in Portugal, at regional level (NUTS II) and for the period 1995 to 1999 (2)(Martinho, 2011b).convergence; geographic concentration; panel data; manufactured industries; Portuguese regions

Martinho, Vítor João Pereira Domingues

English

Research Papers in Economics

1 
 
THE CONVERGENCE THEORIES AND THE GEOGRAPHIC 
CONCENTRATION IN THE PORTUGUESE MANUFACTURED 
INDUSTRY. ANOTHER APPROACH 
 
Vitor João Pereira Domingues Martinho 
 
Unidade de I&D do Instituto Politécnico de Viseu 
Av. Cor. José Maria Vale de Andrade 
Campus Politécnico 
3504 - 510 Viseu 
(PORTUGAL) 
e-mail: vdmartinho@esav.ipv.pt  
 
 
ABSTRACT 
 
The aim of this paper is to present a further contribution to the analysis of absolute convergence, 
associated with the neoclassical theory, of the manufactured industry productivity at regional level and for 
the period from 1995 to 1999 (1)(Martinho, 2011a). This paper pretends, also, to analyze the importance 
which the natural advantages and local resources are in the manufacturing industry location, in relation 
with the "spillovers" effects and industrial policies. To this, we estimate the Rybczynski equation matrix for 
the various manufacturing industries in Portugal, at regional level (NUTS II) and for the period 1995 to 
1999 (2)(Martinho, 2011b). 
 
Keywords:  convergence;  geographic  concentration;  panel  data;  manufactured  industries; 
Portuguese regions. 
  
1. INTRODUCTION 
 
(3)Islam (1995) developed a model about the convergence issues, for panel data, based on the 
(4)Solow model, (1956). 
Taking into account the work of (5)Kim (1999), we seek, aldo, to analyze the importance of the 
natural advantages and local resources (specific factors of locations) have in explaining the geographic 
concentration over time in the Portuguese regions, relatively effects "spillovers" and industrial policies (in 
particular, the modernization and innovation that have allowed manufacturing in other countries take better 
advantage of positive externalities). For this, we estimated the Rybczynski equation matrix for the different 
manufacturing industries in the regions of Portugal, for the period 1995 to 1999. It should be noted that 
while the model of inter-regional trade, the Heckscher-Ohlin-Vanek, presents a linear relationship between 
net  exports  and  inter-regional  specific  factors  of  locations,  the  Rybczynski  theorem  provides  a  linear 
relationship between regional production and specific factors of locations. In principle, the residual part of 
the estimation of Rybczynski, measured by the difference between the adjusted degree of explanation 
(R2) and the unit presents a approximated estimate of the importance not only of the "spillovers” effects, 
as  considered  by  Kim  (1999),  but  also  of  the  industrial  policies,  because,  industrial  policies  of 
modernization and innovation are interconnected with the "spillover" effects. However, it must be some 
caution with this interpretation, because, for example, although the growth of unexplained variation can be 
attributed to the growing importance of externalities "Marshallians" or "spillovers" effects and industrial 
policies,  this  conclusion  may  not  be  correct.  Since  the  "spillovers"  effects  and  industrial  policies  are 
measured as a residual part, the growth in the residual can be caused, also, for example, by growth in the 
randomness of the location of the products manufactured and the growing importance of external trade in 
goods and factors. 
 
2. CONVERGENCE MODEL 
 
The purpose of this part of the work is to analyze the absolute convergence of output per worker 
(as a "proxy" of labor productivity), with the following equation Islam (1995), based on the Solow model, 
1956): 
 
it t i it P b c P      1 , ln ln                                                                                  
 
3. THE MODEL THAT  ANALYZES THE IMPORTANCE  OF NATURAL  ADVANTAGES AND 
LOCAL RESOURCES IN AGGLOMERATION 
 
According to Kim (1999), the Rybczynski theorem states that an increase in the supply of one 
factor leads to an increased production of the good that uses this factor intensively and a reduction in the 
Alert! This author has published many duplicate versions of very similar papers with slightly 
different titles, but without an appropriate notice. This may apply to this contribution, too. 2 
 
production of other goods. 
Given these assumptions, the linear relationship between regional output and offers of regional 
factors, may be the following: 
V A Y
1   , 
where Y (nx1) is a vector of output, A (nxm) is a matrix of factor intensities or matrix input Rybczynski and 
V (mx1) is a vector of specific factors to locations. 
   
For the output we used the gross value added of different manufacturing industries, to the specific 
factors  of  the  locations  used  the  labor,  land  and  capital.  For  the  labor  we  used  the  employees  in 
manufacturing industries considered (symbolized in the following equation by "Labor") and the capital, 
because the lack of statistical data, it was considered, as a "proxy", the production in construction and 
public works (the choice of this variable is related to several reasons including the fact that it represents a 
part  of  the  investment  made  during  this  period  and  symbolize  the  part  of  existing  local  resources, 
particularly in terms of infrastructure) and the gross formation of fixed capital in manufacturing. With regard 
to land, although this factor is often used as specific of the locations, the amount of land is unlikely to serve 
as a significant specific factor of the locations. Alternatively, in this work is used the production of various 
extractive  sectors,  such  as  a  "proxy"  for  the  land.  These  sectors  include  agriculture  and  fisheries 
(represented  by  "Agriculture")  the  forest  ("Forest"),  extractive  industry  of  metallic  mineral  products 
("Extraction1"), extractive industry of several products ("Extraction2") and energy production ("Energy"). 
The overall regression is then used as follows: 
 
    
    
   
     
it it it it
it it it it it
Capital on Constructi Energy Extraction
Extraction Florestry e Agricultur Labor Y
ln ln ln 2 ln
1 ln ln ln ln ln
8 7 6 5
4 3 2 1
 
 
  In  this  context,  it  is  expected  that  there  is,  above  all,  a  positive  relationship  between  the 
production  of  each  of  the  manufacturing  industry  located  in  a  region  and  that  region-specific  factors 
required for this industry, in particular, to emphasize the more noticeable cases, between food industry and 
agriculture, among the textile industry and labor (given the characteristics of this industry), among the 
industry of metal products and metal and mineral extraction and from the paper industry and forest. 
 
4. DATA ANALYSIS 
 
Considering the variables on the models presented previously and the availability of statistical 
information, we used the following data disaggregated at regional level. Annual data for the period 1995 to 
1999, corresponding to the five regions of mainland Portugal (NUTS II), and for the several manufactured 
industries  in  those  regions.  The  data  are,  also,  relative  to  regional  gross  value  added  of  agriculture, 
fisheries  and  forestry,  extractive  industry  of  metallic  mineral  products,  extractive  industry  of  several 
products, the industry of fuel and energy products and construction and public works. We used yet data 
relating to gross formation of fixed capital. These data were obtained from INE (National Accounts 2003).  
 
5. EMPIRICAL EVIDENCE OF ABSOLUTE CONVERGENCE, PANEL DATA 
 
Table 1 shows results also for each of the manufacturing industries of the NUTS II of Portugal, but 
now for the period 1995 to 1999. 
 
Table 1: Analysis of convergence in productivity for each of the manufacturing industries at the five NUTS 
II of Portugal, for the period 1995 to 1999 
Metals industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  1.108* 
(3.591)    -0.111* 
(-3.353)  -0.118  2.457  0.384  18 
LSDV    1.476 
(1.143) 
1.496 
(1.183) 
1.503 
(1.129) 
1.451 
(1.186) 
1.459 
(1.233) 
-0.151 
(-1.115)  -0.164  2.424  0.416  14 
GLS  1.084* 
(7.366)    -0.108* 
(-6.866)  -0.114  2.176  0.724  18 
Minerals industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  -0.455 
(-1.236)    0.052 
(1.409)  0.051  1.601  0.099  18 
LSDV    2.158* 
(2.222) 
2.280* 
(2.265) 
2.287* 
(2.227) 
2.194* 
(2.248) 
2.417* 
(2.306) 
-0.221* 
(-2.192)  -0.250  1.359  0.567  14 
GLS  -0.356 
(-0.854)    0.042 
(1.007)  0.041  1.628  0.053  18 
Chemical industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  1.236 
(1.026)    -0.115 
(-0.966)  -0.122  1.049  0.049  18 
LSDV    5.320* 
(4.493) 
5.281* 
(4.452) 
5.447* 
(4.449) 
5.858* 
(4.711) 
5.072* 
(4.501) 
-0.525* 
(-4.470)  -0.744  2.432  0.702  14 
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GLS  3.136* 
(2.532)    -0.302* 
(-2.477)  -0.360  1.174  0.254  18 
Electric goods industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  1.936 
(1.289)    -0.196 
(-1.271)  -0.218  1.945  0.082  18 
LSDV    4.729 
(1.504) 
4.775 
(1.507) 
4.818 
(1.490) 
4.590 
(1.463) 
4.671 
(1.519) 
-0.482 
(-1.488)  -0.658  2.038  0.342  14 
GLS  2.075 
(1.299)    -0.211 
(-1.283)  -0.237  1.976  0.084  18 
Transport equipments industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  2.429* 
(2.264)    -0.237* 
(-2.179)  -0.270  1.837  0.209  18 
LSDV    8.626* 
(10.922) 
8.647* 
(10.973) 
9.051* 
(10.924) 
8.537* 
(10.917) 
8.356* 
(10.866) 
-0.867* 
(-10.811)  -2.017  2.000  0.896  14 
GLS  3.507* 
(3.025)    -0.346* 
(-2.947)  -0.425  1.649  0.326  18 
Food industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  0.873 
(1.619)    -0.082 
(-1.453)  -0.086  2.921  0.105  18 
LSDV    -0.516 
(-0.300) 
-0.521 
(-0.308) 
-0.532 
(-0.304) 
-0.425 
(-0.259) 
-0.435 
(-0.268) 
0.060 
(0.341)  0.058  2.230  0.208  14 
GLS  1.027* 
(4.163)    -0.098* 
(-3.800)  -0.103  2.251  0.445  18 
Textile industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  0.788** 
(2.048)    -0.080** 
(-1.882)  -0.083  1.902  0.165  18 
LSDV    0.514 
(0.261) 
0.525 
(0.270) 
0.515 
(0.262) 
0.522 
(0.272) 
0.541 
(0.301) 
-0.051 
(-0.239)  -0.052  1.919  0.167  14 
GLS  0.802* 
(20.052)    -0.081* 
(-18.461)  -0.085  1.719  0.950  18 
Paper industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  0.735 
(1.524)    -0.073 
(-1.471)  -0.076  2.341  0.107  18 
LSDV    5.201 
(1.479) 
5.454 
(1.462) 
5.410 
(1.467) 
5.053 
(1.470) 
4.970 
(1.486) 
-0.533 
(-1.465)  -0.761  1.939  0.227  14 
GLS  0.654* 
(3.329)    -0.064* 
(-3.198)  -0.066  2.185  0.362  18 
Several industry 
Method  Const.  D1  D2  D3  D4  D5  Coef.  T.C.  DW  R
2  G.L. 
Pooling  -0.338 
(-0.463)            0.042 
(0.531)  0.041  2.651  0.015  18 
LSDV    3.734** 
(1.949) 
3.883** 
(1.962) 
3.940** 
(1.966) 
3.817** 
(1.967) 
3.647** 
(1.934) 
-0.402** 
(-1.930)  -0.514  2.905  0.303  14 
GLS  -0.904* 
(-3.791)    0.102* 
(4.003)  0.097  1.922  0.471  18 
Note: Const. Constant; Coef., Coefficient, TC, annual rate of convergence; * Coefficient statistically 
significant  at  5%,  **  Coefficient  statistically  significant  at  10%,  GL,  Degrees  of  freedom;  LSDV, 
method of fixed effects with variables dummies; D1 ... D5, five variables dummies corresponding to 
five different regions, GLS, random effects method. 
 
6. EMPIRICAL EVIDENCE OF GEOGRAPHIC CONCENTRATION 
 
In the results presented in the following table, there is a strong positive relationship between 
gross value added and labor in particular in the industries of metals, minerals, textile, paper and several 
products. On the other hand, there is an increased dependence on natural and local resources in the 
different industries. We found that the location of manufacturing industry is yet mostly explained by specific 
factors of locations and poorly explained by "spillovers" effects and industrial policies.  
 
Table 2: Results of estimations for the whole period 1995-1999 
    
    
   
     
it it it it
it it it it it
Capital on Constructi Energy Extraction
Extraction Florestry e Agricultur Labor Y
ln ln ln 2 ln
1 ln ln ln ln ln
8 7 6 5
4 3 2 1
 
  IMT  
(2) 
IMI  
(2) 
IPQ  
(2) 
IEE  
(2) 
IET  
(2) 
IAL  
(1) 
ITE  
(1) 
IPA  
(1) 
IPD  
(1) 
   3.476
    
(0.365)
   
3.151
   
(0.403)
   
-126.876
   
(-1.572)
   
64.626
(*) 
(4.362)
   
17.203
   
(0.395)
   
       
Dummy1   
 
   
     
     
Dummy2   
 
   
     
     
Dummy3   
 
   
     
     
Dummy4   
 
   
    -3.137
   
(-1.740)
    
-1.212
   
(-2.826)
   
0.687
   
(0.663)
   
-0.497
   
(-0.590)
   
Dummy5   
   
           
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different titles, but without an appropriate notice. This may apply to this contribution, too. 4 
 
 
1 
 
1.294
(*) 
(7.664)
   
1.251
(*) 
(13.829)
   
1.800
   
(1.339)
   
-0.073
   
(-0.321)
   
0.684
   
(0.640)
   
0.072
   
(0.332)
   
0.747
(*) 
(11.372)
   
1.320
(*) 
(2.887)
   
0.585
(**) 
(2.141)
   
2 
 
0.136
    
(0.778)
   
-0.078
   
(-0.452)
   
3.558
(*) 
(2.929)
   
-1.334
(*) 
(-4.651)
   
-0.482
   
(-0.703)
   
0.795
(**) 
(2.996)
   
0.408
(**) 
(3.914)
   
-0.638
   
(-1.666)
   
-0.114
   
(-0.411)
   
3 
 
-0.356
     
(-1.730)
   
-0.267
   
(-1.682)
   
2.306
   
(1.209)
   
-1.242
(*) 
(-3.769)
   
-0.639
   
(-0.521)
   
0.822
(**) 
(3.502)
   
0.498
(*) 
(6.317)
   
0.376
(*) 
(4.689)
   
0.258
(**) 
(2.227)
   
4 
 
-0.161
(**)  
(-2.024)
   
-0.064
   
(-1.073)
   
0.568
   
(0.911)
   
-0.175
   
(-1.475)
   
-0.147
   
(-0.423)
   
0.180
(**) 
(3.164)
   
0.107
(*) 
(5.271)
   
0.036
   
(0.532)
   
-0.084
   
(-1.025)
   
5 
 
0.606
(*) 
(4.819)
   
0.411
(*) 
(3.386)
   
2.198
(*) 
(2.755)
   
-1.039
(*) 
(-4.951)
   
0.120
   
(0.180)
   
0.011
   
(0.057)
   
-0.273
(**) 
(-3.729)
   
-0.384
   
(-1.462)
   
0.163
   
(0.509)
   
6 
 
-0.215
     
(-1.802)
   
-0.042
   
(-0.437)
   
-3.058
(*) 
(-3.196)
   
0.257
   
(1.338)
   
0.404
   
(0.540)
   
-0.352
   
(-1.599)
   
-0.562
(*) 
(-6.689)
   
-0.046
   
(-0.265)
   
-0.214
   
(-1.035)
   
7 
 
-0.237
     
(-1.247)
   
-0.182
   
(-1.371)
   
0.330
   
(0.273)
   
0.995
(*) 
(3.153)
   
0.134
   
(0.146)
   
-0.185
   
(-0.655)
   
0.139
   
(1.560)
   
0.553
   
(1.848)
   
0.470
   
(1.265)
   
8 
 
-0.036
     
(-1.538)
   
0.038
(**) 
(2.043)
   
0.407
(**) 
(2.105)
   
0.087
(**) 
(2.351)
   
0.101
   
(0.964)
   
0.004
   
(0.143)
   
0.072
(*) 
(7.404)
   
-0.036
   
(-0.997)
   
-0.017
   
(-0.387)
   
Sum  of  the 
elasticities 
1.031  1.067  8.109  -2.524  0.175  1.347  1.136  1.181  1.047 
R
2 adjusted  0.999  0.999  0.999  0.999  0.999  0.999  0.999  0.999  0.999 
Residual part  0.001  0.001  0.001  0.001  0.001  0.001  0.001  0.001  0.001 
Durbin-Watson  2.343  2.282  1.988  2.221  2.191  2.021  3.074  2.747  2.400 
Hausman test  (c)  (c)  (c)  (c)  (c)  (c)  16.063
(b)(*)  33381.450
(b)(*)  197.160
(b)(*) 
For  each  of  the  industries,  the  first  values correspond  to  the  coefficients  of  each  of  the  variables  and  values in 
brackets represent t-statistic of each; (1) Estimation with variables "dummies"; (2) Estimation with random effects; (*) 
coefficient statistically significant at 5%  (**) Coefficient  statistically significant at 10%; IMT, metals industries; IMI, 
industrial  mineral;,  IPQ,  the  chemicals  industries;  IEE,  equipment  and  electrical  goods  industries;  EIT,  transport 
equipment industry; ITB, food industry; ITE, textiles industries; IPA, paper industry; IPD, manufacturing of various 
products; (a) accepted the hypothesis of random effects; (b) reject the hypothesis of random effects; (c) Amount not 
statistically acceptable. 
 
  7. CONCLUSIONS 
 
The signs of absolute convergence are different from one manufactured industries to another, but 
there  is  a  curious  results  for  the  equipment  transport  industry,  because  present  strong  evidence  of 
absolute convergence and we know that this industry is a dynamic sector. In another hand we have the 
textile industry that we expect find strong signs of absolute convergence, because we know this is a sector 
with weak dynamics, but we do not see these evidences. 
Of referring that the location of the Portuguese manufacturing industry is still mostly explained by 
specific factors of locations and the industrial policies of modernization and innovation are not relevant, 
especially those that have come from the European Union, what is more worrying. 
So, we can say that the surprising signs of convergence in some industries are because the 
location  of  the  manufactured  industries  in  Portugal  is  mostly  explained  by  the  specific  factors  of  the 
locations.  
 
8. REFERENCES 
 
1.  V.J.P.D.  Martinho.  Sectoral  convergence  in  output  per  worker  between  Portuguese  regions.  MPRA 
Paper 32269, University Library of Munich, Germany (2011a). 
2. V.J.P.D. Martinho. Geographic concentration in Portugal and regional specific factors.  MPRA Paper 
32317, University Library of Munich, Germany (2011b). 
3. N. Islam. Growth Empirics : A Panel Data Approach. Quarterly Journal of Economics, 110, 1127-1170 
(1995). 
4. R. Solow. A Contribution to the Theory of Economic Growth. Quarterly Journal of Economics (1956). 
5.  S.  Kim.  Regions,  resources,  and  economic  geography:  Sources  of  U.S.  regional  comparative 
advantage, 1880-1987. Regional Science and Urban Economics (29), 1-32 (1999). 
 
Alert! This author has published many duplicate versions of very similar papers with slightly 
different titles, but without an appropriate notice. This may apply to this contribution, too. 

A Contribution to the Theory of Economic Growth.

The convergence theories and the geographic concentration in the Portuguese manufactured industry. Another approach

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