Determinantes e dependência espacial da inovação nas regiões brasileiras: evidências a partir de um Modelo Tobit Espacial

Araújo, Veneziano de Castro; Garcia, Renato

doi:10.1590/0103-6351/4456

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Artigos • Nova econ. 29 (2) • May-Aug 2019 • https://doi.org/10.1590/0103-6351/4456 copiar

Determinantes e dependência espacial da inovação nas regiões brasileiras: evidências a partir de um Modelo Tobit Espacial

Autoria SCIMAGO INSTITUTIONS RANKINGS

Resumo

Este artigo busca analisar os determinantes da inovação, os padrões espaciais e a dependência espacial da inovação nas microrregiões brasileiras. Em concreto, ele avalia como o P&D das empresas locais, a pesquisa acadêmica regional, o nível de aglomeração e a especialização ou diversificação industrial da localidade afetam a inovação regional. Para analisar esses fatores, foi estimado um modelo empírico baseado na Função de Produção do Conhecimento (FPC) com dados de patente no Brasil por um Tobit Espacial Autoregressivo (SAR-Tobit). Os resultados indicam que níveis mais elevados de P&D Industrial Regional implicam em maior inovação e que maiores níveis de pesquisa universitária impactam positivamente a inovação na região. Além disso, regiões mais aglomeradas e diversificadas apresentam melhor desempenho inovador. Em relação à dinâmica espacial, a proximidade das microrregiões mais inovadoras afeta positivamente a inovação local, o que mostra a existência de spillovers de conhecimento inter-regionais associados inovação medida por patentes.

Palavras-chave
inovação regional; patentes; Brasil; tobit espacial

Variable	Description
PatPC	Patents filed in 2004 or 2005 per 100,000 inhabitants in the regions. Source: Brazilian Patent Office
R&DInd	Industrial R&D of a region; percentage of employees in manufacturing and mining working and acting in R&D activities per total employees. Source: Ministry of Labor 2003/2004.
R&DUniv	Academic R&D of a region, obtained by principal component analysis of RDU_prof, and RDU stu (listed below); own elaboration
KI	Krugman Index of specialization-diversification² 2 Following Crescenzi et al. (2007), the Krugman index measures local employment specialization by calculating a) for each region, the share of industry k in the region’s total em ployment: vki, b) the share of the same industry employment of all other regions vik and; c) the absolute values of the difference between these shares, added over all industries: Ki=∑kabsvik−vi−k. , elaborated with data from the Ministry of Labor, 2004/2005
Agglom	Population density of micro-regions; source: Brazilian Statistical Bureau
ShrInd	Share of employment on manufacturing and mining or the share of the active population; source: Ministry of Labor, 2004/2005
Sec	Share of top 9 sectors in local employment; source: Ministry of Labor, 2004/2005
NNE	Dummy for the North, Northeast and Centre-West; own elaboration
Metro	Dummy for metropolitan regions; own elaboration
RDU_prof	Number of university professors with full dedication per 10,000 inhabitants; source: Brazilian Ministry of Education, 2003/2004
RDU_stu	Number of students in master's, doctoral or post-doctoral programmes per 10,000 inhabitants; source: Brazilian Ministry of Education, 2003/2004

	(1) OLS	(2) SAR	(3) SAR-Tobit
WPatPC	-	0.52^* ***** p < 0.1%; [105.979]	0.295^* ***** p < 0.1%; [5.258]
R&DInd	0,138^* ***** p < 0.1%; [6.25]	0.118^* ***** p < 0.1%; [5.764]	0.144^* ***** p < 0.1%; [3.991]
R&DUniv	0.432^* ***** p < 0.1%; [8.477]	0.401^* ***** p < 0.1%; [8.477]	0.477^* ***** p < 0.1%; [6.298]
Agglom	0.123^ p < 1%; [2.781]	-0.008 [-0.194]	0.226^ p < 1%; [2.631]
KI	-0.321^* * p < 5%; [-2.09]	-0.569^* ***** p < 0.1%; [-3.993]	-3.082^* ***** p < 0.1%; [-9.911]
ShrInd	5.993^* ***** p < 0.1%; [10.906]	3.941^* ***** p < 0.1%; [7.737]	9.531^* ***** p < 0.1%; [9.265]
Sec	2.899^* ***** p < 0.1%; [7.05]	2.193^* ***** p < 0.1%; [5.749]	3.498^* ***** p < 0.1%; [4.701]
Metro	2.214^* ***** p < 0.1%; [6.125]	2.494^* ***** p < 0.1%; [7.438]	2.551^* ***** p < 0.1%; [4.547]
NNE	-0.922^* ***** p < 0.1%; [-5.737]	-0.004 [-0.027]	-1.152^ p < 1%; [-3.455]
Adj-R²	0.4664	0.4899	-
LMSAR	180.48^* ***** p < 0.1%;	-	-

	Pat-1nvPC	PatnUnivPC	HH	Professors	S-SE	2001-2005
	(I)	(II)	(III)	(IV)	(V)	(VI)
WPatPC_t	0.180^ p < 1%;	0.301^* ***** p < 0.1%;	0.333^* ***** p < 0.1%;	0.275^* ***** p < 0.1%;	0.298^* ***** p < 0.1%;	0.258^* ***** p < 0.1%;
WPatPC_t	[2.856]	[5.480]	[6.142]	[4.940]	[4.055]	[6.992]
R&DInd_t-1	0.099^* ***** p < 0.1%;	0.136^* ***** p < 0.1%;	0.114^ p < 1%;	0.137^* ***** p < 0.1%;	0.107^ p < 1%;	-
R&DInd_t-1	[4.407]	[3.698]	[3.111]	[3.707]	[2.742]	-
Eng	-	-	-	-	-	0.009^ p < 1%; [3.000]
R&DUniv_t-1	0.384^* ***** p < 0.1%;	0.373^* ***** p < 0.1%;	0.517^* ***** p < 0.1%;	-	0.440^* ***** p < 0.1%;	0.366^* ***** p < 0.1%;
R&DUniv_t-1	[7.765]	[4.845]	[6.669]	-	[5.528]	[7618]
Prof	-	-	-	0.074^* ***** p < 0.1%; [5.425]	-	-
Agglom	0.154^ p < 1%;	0.246^ p < 1%;	0.03	0.257^ p < 1%;	0.249	0.283^* ***** p < 0.1%;
Agglom	[2.793]	[2.921]	[0.358]	[3.044]	[1.829]	[5.400]
IED(KI)	-2.042^* ***** p < 0.1%;	-3.150^* ***** p < 0.1%;	-	-3.197^* ***** p < 0.1%;	-3.436^* ***** p < 0.1%;	-3.131^* ***** p < 0.1%;
IED(KI)	[-9.818]	[-10.120]	-	[-10.261]	[-8.309]	[-15.105]
IED(HH)	-	-	-5.678^* ***** p < 0.1%; [-7190]	-	-	-
ShrInd	5.167^* ***** p < 0.1%;	9.498^* ***** p < 0.1%;	8.302^* ***** p < 0.1%;	9.441^* ***** p < 0.1%;	12.026^* ***** p < 0.1%;	9.043^* ***** p < 0.1%;
ShrInd	[7697]	[9.065]	[7598]	[9.007]	[8.272]	[14.108]
Sec	1.753^* ***** p < 0.1%;	3.502^* ***** p < 0.1%;	1.869^* * p < 5%;	3.394^* ***** p < 0.1%;	3.049^ p < 1%;	3.360^* ***** p < 0.1%;
Sec	[3.657]	[4.529]	[2.375]	[4.369]	[3.053]	[7279]
Metro	1.331^* ***** p < 0.1%;	2.487^* ***** p < 0.1%;	3.481^* ***** p < 0.1%;	2.806^* ***** p < 0.1%;	2.92^* ***** p < 0.1%;	2.675^* ***** p < 0.1%;
Metro	[3.763]	[4.476]	[6.431]	[5.064]	[4.028]	[8.186]
NNE	-0.84^* ***** p < 0.1%;	-1.138^* ***** p < 0.1%;	-1.641^* ***** p < 0.1%;	-1.286^* ***** p < 0.1%;	-	-1.205^* ***** p < 0.1%;
NNE	[-3.875]	[-3.424]	[-5.073]	[-3.844]	-	[-5.730]

	-KI	Interaction
WPatPC	0.294^* ***** p < 0.1%;	0.275^* ***** p < 0.1%;
WPatPC	[4.956]	[4.956]
R&DInd	0.142^* ***** p < 0.1%;	0.152^* ***** p < 0.1%;
R&DInd	[3.874]	[4.287]
R&DUniv	0.478^* ***** p < 0.1%;	0.472^* ***** p < 0.1%;
R&DUniv	[6.354]	[6.251]
Agglom	0.227^ p < 1%;	0.050
Agglom	[2.608]	[0.478]
-KI	3.083^* ***** p < 0.1%;	0.468
-KI	[9.928]	[0.466]
*-KI ^ * p < 5%; Agglom**	-	1.622^ p < 1%;
*-KI ^ * p < 5%; Agglom**	-	[2.677]
ShrInd	9.533^* ***** p < 0.1%;	9.531^* ***** p < 0.1%;
ShrInd	[9.240]	[9.257]
Sec	3.476^* ***** p < 0.1%;	3.055^* ***** p < 0.1%;
Sec	[4.571]	[3.923]
Metro	2.563^* ***** p < 0.1%;	2.897^* ***** p < 0.1%;
Metro	[4.531]	[5.253]
N	-1.156^* ***** p < 0.1%;	-1.273^* ***** p < 0.1%;
N	[-3.475]	[-3.818]

Year	Moran Index	t stat	Std deviation
2005	0.3869	17.3	0.0224
2004	0.4136	18.4	0.0255
2003	0.4014	17.9	0.0224
2002	0.2885	13.5	0.0213
2001	0.3514	16.0	0.0220

Component	Eigen value	Difference	Proportion	Cumulated
Comp 1	1.76428	1.52857	0.8821	0.8018
Comp 2	0.235716	0	0.1179	0.9745

Variable			Comp 1	Comp 2
Graduate programs			0.7071	0.7071
Full-time professors			0.7071	-0.7071

n = 1116	20-nearest (A)	Inverse distance (B)	Queen (c)
WPatPC	0.102^* ***** p < 0.1%;	0.281^* ***** p < 0.1%;	0.154^* ***** p < 0.1%;
WPatPC	[2.578]	[4.995]	[3.569]
R&DInd	0.153^* ***** p < 0.1%;	0.144^* ***** p < 0.1%;	0.145^* ***** p < 0.1%;
R&DInd	[4.368]	[3.889]	[3.958]
R&DUniv	0.492^* ***** p < 0.1%;	0.488^* ***** p < 0.1%;	0.506^* ***** p < 0.1%;
R&DUniv	[6.534]	[6.417]	[6.620]
Agglom	0.266^ p < 1%;	0.242^ p < 1%;	0.256^ p < 1%;
Agglom	[3.115]	[2.819]	[3.033]
KI	-3.192^* ***** p < 0.1%;	-3.189^* ***** p < 0.1%;	-3.066^* ***** p < 0.1%;
KI	[-10.629]	[-10.314]	[-10.083]
ShrInd	10.886^* ***** p < 0.1%;	9.599^* ***** p < 0.1%;	10.193^* ***** p < 0.1%;
ShrInd	[10.504]	[9.195]	[9.850]
Sec	3.710^* ***** p < 0.1%;	3.604^* ***** p < 0.1%;	3.578^* ***** p < 0.1%;
Sec	[4.986]	[4.723]	[4.671]
Metro	2.365^* ***** p < 0.1%;	2.35^* ***** p < 0.1%;	2.381^* ***** p < 0.1%;
Metro	[4.325]	[4.232]	[4.311]
NNE	-1.934^* ***** p < 0.1%;	-1.183^* ***** p < 0.1%;	-1.812^* ***** p < 0.1%;
NNE	[-6.156]	[-3.44]	[-5.880]

Variable	Description	Source
Pat-1ndPC_t	Invention Patents Filed per 10.000 inhabitants in micro-region	INPI and IBGE
PatnUnvPC_t	Total Patents Filed excluding Academic Patents per 10.000 in habitants in micro-region	INPI and IBGE
Eng	Industrial R&D- Engineers per 10.000 workers in micro-region	RAIS
Prof	University R&D- Full-time Professors per 10.000 inhab	INEP and IBGE
HH	Herfindhal-Hirschman Index - employment CNAE 1.0 Manufac- turing and Extractive Industries	RAIS

	PatPC	R&DInd	R&DUniv	Agglom	Metro	ShrInd	NNE	Sec	KI	HH
PatPC	1
R&DInd	0.3272	1
R&DUniv	0.3328	0.2137	1
Agglom	0.4401	0.3467	0.3734	1
Metro	0.2300	0.2730	0.3242	0.5630	1
ShrInd	0.3375	0.1736	0.0221	0.3809	0.0063	1
NNE	-0.4578	-0.2374	-0.2085	-0.4361	-0.0711	-0.3971	1
Sec	0.3486	0.2895	0.1979	0.4088	0.1901	0.1938	-0.3674	1
KI	-0.3834	-0.1982	-0.2888	-0.5742	-0.2857	-0.1267	0.4404	-0.3921	1
HH	-0.2884	-0.2242	-0.2023	-0.3800	-0.1903	-0.0559	0.3875	-0.4662	0.7193	1

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[1] Veneziano de Castro Araújo - veneziano.araujo@unifesp.br

Universidade Federal de São Paulo, Osasco, SP, Brazil - Departamento de Economia.

ORCID: https://orcid.org/0000-0003-0994-8327.

	(1) OLS	(2) SAR	(3) SAR-Tobit
WPatPC	-	0.52^* ***** p < 0.1%; [105.979]	0.295^* ***** p < 0.1%; [5.258]
R&DInd	0,138^* ***** p < 0.1%; [6.25]	0.118^* ***** p < 0.1%; [5.764]	0.144^* ***** p < 0.1%; [3.991]
R&DUniv	0.432^* ***** p < 0.1%; [8.477]	0.401^* ***** p < 0.1%; [8.477]	0.477^* ***** p < 0.1%; [6.298]
Agglom	0.123^ p < 1%; [2.781]	-0.008 [-0.194]	0.226^ p < 1%; [2.631]
KI	-0.321^* * p < 5%; [-2.09]	-0.569^* ***** p < 0.1%; [-3.993]	-3.082^* ***** p < 0.1%; [-9.911]
ShrInd	5.993^* ***** p < 0.1%; [10.906]	3.941^* ***** p < 0.1%; [7.737]	9.531^* ***** p < 0.1%; [9.265]
Sec	2.899^* ***** p < 0.1%; [7.05]	2.193^* ***** p < 0.1%; [5.749]	3.498^* ***** p < 0.1%; [4.701]
Metro	2.214^* ***** p < 0.1%; [6.125]	2.494^* ***** p < 0.1%; [7.438]	2.551^* ***** p < 0.1%; [4.547]
NNE	-0.922^* ***** p < 0.1%; [-5.737]	-0.004 [-0.027]	-1.152^ p < 1%; [-3.455]
Adj-R²	0.4664	0.4899	-
LMSAR	180.48^* ***** p < 0.1%;	-	-