O trabalho caracteriza os níveis e padrões da concentração da indústria brasileira entre 1994 e 2004, e identifica os determinantes econômicos do crescimento do emprego industrial estadual brasileiro no período. As evidências mostram que desconcentração industrial é mais forte para o segmento intensivo em recursos naturais e mais fraca no de intensivo capital. Novos polos de crescimento do emprego parecem surgir no Nordeste, especialmente, para o segmento intensivo trabalho. As evidências obtidas apontam para a importância das externalidades dinâmicas - as quais são mensuradas pela variável diversidade industrial - dos linkages de mercados e dos custos de transportes para o crescimento do emprego. Essas evidências são coerentes com os argumentos da Nova Geografia Econômica e de Jacobs.
The work characterizes the concentration levels and patterns of Brazilian transformation Industry, and identifies the economical determinants of the industrial employment growth for the states of Brazil, in 1994 and 2004. The evidences show that industrial desconcentration is stronger for the intensive capital segment and weaker for natural resources segment. New poles of employment growth seem to appear in the Northeast, especially, for the segment intensive labor. The market linkages, or pecuniary externalities, and of the dynamic externalities, measured by the industrial diversity, are important evidence for the employment growth of transformation industry of Brazilian states. While the transport costs, when controlled by fixed effects, they present negative statistical correlation with the employment growth, consistent to NEG
-This paper analyzed the process of convergence in the gross value of wood production in mesoregions of Northeast Brazil, in the period of 1994 and 2013. The object of study was the Gross Value of Production (GVP) of firewood per km 2 of the mesoregions of the Northeast of Brazil. In the methodology the Absolute Convergence Model was applied and estimated through the classical model and spatial models. In the spatial approach we used the Spatial Autoregressive Model (SAR) and the Spatial Error Model (SEM). From the results obtained, the following conclusions were reached: The mesoregions of the Northeast of Brazil had an average fall of 3.94% a.a. of the GVP/km 2 of native wood for the period 1994 to 2013. Considering the classical linear regression model, convergence was verified and also the presence of spatial dependence for GVP/km 2 of firewood. In order to correct the spatial dependence, the SAR and SEM Models were adequate and according to Akaike's Information Criterion and used the rook matrix the SEM was configured the best model. This study showed the importance of the involvement of the spatial question in the models, either by the overlap of information of the GVP and in the development of public policies that positively affect the neighborhood.Keywords: Forest economics; Bioenergy; Spatial econometrics. CONVERGÊNCIA ESPACIAL DO VALOR BRUTO DE PRODUÇÃO DE LENHA NAS MESORREGIÕES DO NORDESTE BRASILEIRO
O objetivo da pesquisa é avaliar a relação entre as exportações, desagregadas por nível de intensidade tecnológica, e o crescimento econômico das microrregiões nordestinas no período de 2010 a 2016. Para isso, foi estimado o modelo de crescimento econômico proposto por Cuaresma e Wörz (2005) em dados em painel. Os resultados apontaram que as exportações de baixa intensidade tecnológica contribuem para redução do crescimento econômico das microrregiões do Nordeste, ao possuírem produtividade inferior que a do setor doméstico. Nesse sentido, as microrregiões Nordestinas necessitam de investimentos nos setores de infraestrutura e P&D, para se desenvolver economicamente.
This study was to present a methodology for analyzing the distribution and spatial-time dependence of the supply of forest bioelectricity in Brazil, from 2000 to 2019. The data for the granting of forest biomass thermoelectric plants were obtained from the National Electric Energy Agency (in Portuguese Agência Nacional de Energia Elétrica-ANEEL). It used the exploratory analysis of spatial data, and as neighborhood criteria the spatial weighting matrices of k-neighbors closest and based on distance, as well as the global and local Moran indices to detect the presence of spatial dependence. The analysis of the situation, by quartile, showed the states of the central-southern portion of Brazil as the main locations of forest thermals. I Moran_Global highlighted the decrease in spatial autocorrelation between the years 2000 and 2019, associated with the implementation of new thermoelectric plants by the large groups Fibria, Klabin and Suzano Celulose throughout the national territory. The local index (I Moran_Local ) pointed out high-powered clusters, especially in the early years, highlighting: the Bahia, Espírito Santo and Minas Gerais axis, the Paraná thermal plants and Mato Grosso do Sul thermal plants. The local index also pointed outliers, which indicated possibility of associating thermoelectric plants with other activities, such as wood production and industries in the steel and paper and cellulose segment. The knowledge of the spatial pattern of the forest bioelectricity sector presented can help new feasibility studies for the implementation in bioelectricity and/or stimulate the development of public policies, focused on cluster regions and adjacent areas, which results in the growth of forest generation.INDEX TERMS Green energy, energy economy, bioenergy, industrial ecology, forest economy, spatial economy, spatial autocorrelation, space-time change.The associate editor coordinating the review of this manuscript and approving it for publication was Zhigang Liu .
Este artigo utiliza a Análise Espacial de Concentração para a identificação e a análise da evolução dinâmica dos clusters produtivos nos setores de confecções e couro-calçadista dos municípios do Nordeste entre 1997 e 2012. Para isso, utiliza-se o uso do Índice de Concentração Normalizado e a Análise Exploratória de Dados Espaciais. Os resultados encontrados sugerem que o setor de confecções apresenta concentração espacial dos clusters produtivos nos Estados de Pernambuco, Ceará e Rio Grande do Norte. O setor de couros e calçados, por sua vez, apresenta maior concentração nos municípios do Estado do Ceará e da Bahia. Além disso, percebe-se transbordamento espacial dos clusters nos dois setores entre os municípios no período em estudo.
Este artigo tem como objetivo testar a hipótese de dependência espacial na taxa de criminalidade dos municípios da região Sul do Brasil. Para tanto, utilizou-se dados relativos aos homicídios provenientes do Sistema de Informação de Mortalidade (SIM-DATASUS), pertencente ao Ministério da Saúde, e variáveis do DATASUS e do Censo Demográfico. As análises descritivas revelaram que a criminalidade segue um determinado padrão comportamental quanto à escolha dos municípios em que a mesma ocorre. Com base na Análise Exploratória de Dados Espaciais (AEDE), observou-se que tanto o indicador de associação global quanto o local apontam indícios de não aleatoriedade do crime no espaço. A partir desse resultado, estimou-se o Modelo Clássico de Regressão Linear, visando constatar tal dependência e, em seguida, o modelo econométrico-espacial SAR-MQ2E para a obtenção de estimativas mais precisas e robustas. Os resultados do modelo espacial mostraram que a criminalidade sulista está correlacionada ao crime passado, ao desemprego, à densidade demográfica, ao Índice de Desenvolvimento Humano e às diferenças individuais de cada Estado (captadas pela dummies de Estado). AbstractThis paper aims to test the spatial dependence hypothesis on crime rate of the municipalities of the south region of Brazil in the year 2012. To this end, it were collected data from the Mortality Information System (SIM-DATASUS), belonging to the Ministry of Health, together with variables from DATASUS and the Demographic Census. Descriptive analyzes revealed that crime follows a certain behavioral pattern regarding the choice of the municipalities in which it occurs. Based on the Exploratory Spatial Data Analysis (ESDA), it was observed that both the indicator of global and local association show evidence of the non-randomness of crime in space. From this result, it was estimated the Classical Linear Regression Model, aiming to verify such dependency, and then the SAR-MQ2E econometric-spatial model to obtain more accurate and robust estimates. The results of the spatial model showed that southern crime correlates with past crime, unemployment, demographic density, Human Development Index and individual differences in each state (captured by the state dummies).
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