Multiple dimensions of regional economic growth: The Brazilian case, 1991−2000

Abstract

Abstract:This paper seeks to understand how and why the determinants of economic growth (including spatial spillovers) in Brazil may manifest themselves differently at different spatial scales (municipalities, micro-regions, spatial clusters, and states) between 1991 and 2000. Analysing this issue it sheds light on the geography of the structural process underlying the economic growth at different scales. It means that the definition of each scale level could have a well-defined role in the economic growth process. A complementary approach is related to the Modifiable Areal Unit Problem (MAUP) and Ecological Fallacy Problem. These two measurement problems stem from the fact that there is an aggregation problem which might prevent us from identifying the actual scale at which processes operate. This paper suggests a general framework that allows dealing with multiple spatial scales, spatial autocorrelation and model uncertainty. The analysis reveals that if single regression is estimated at the different scale levels, the results change as scale level changes. However, the robustness test was able to identify variables that are simultaneously significant at different spatial scales: higher education and health capital and better local infra-structure are related to higher economic growth rates. Among other results, this paper identifies that spatial spillovers are operating especially at finer scales. At municipal level, several variables exhibit externality effects across space in Brazil, such as physical capital, education and health capital and local infrastructure. Finally, the study also concludes that Brazil is a country that regions (at all scale levels) converged too slowly over the nineties.

Toward Sustainable Economic Growth: A Spatial Panel Data Analysis of Regional Income Convergence in US BEA Economic Areas

“…Ertur et al [19], Dall'erba et al [20], and Ramajo et al [6] investigated the role of spatial effects in the analysis of economic convergence processes among NUTS (Nomenclature of Units for Territorial Statistics) level-2 regions in the EU countries. More recently, several empirical studies find increasing polarization in per capita income across regions and explain this effect as primarily the result of the formation of geographical clusters across regions within countries [21][22][23][24][25]. In light of these findings, a clear need exists to develop a spatially explicit analytical model for the analysis of regional economic convergence.…”

Un modelo de simulación computacional integrado a SIG para explorar la dinámica de crecimiento de la Zona Metropolitana de la Ciudad de México (1998-2008) / A Computer Simulation Model Integrated into SIG to Explore the Dynamics of Growth of the Metropoli

“…Adicionalmente debe señalarse que el impacto de las externalidades espaciales sobre el crecimiento puede verse también afectado por la partición escogida; y si bien este aspecto todavía ha sido poco abordado en la literatura, debe tomarse en cuenta. Al respecto Resende (2011) evalúa el crecimiento económico y el efecto de las externalidades espaciales en diferentes escalas regionales (estados, municipios, microrregiones y aglomeraciones espaciales) para el caso de Brasil, y entre sus hallazgos destaca que las externalidades espaciales sólo aparecen en las escalas pequeñas (municipales y microrregionales). En el presente estudio estamos considerando una unidad de partición relativamente pequeña (municipio), por lo que la presencia de externalidades en las ecuaciones (A) y (A.1) es en principio factible.…”

Spatial Dimensions of Economic Growth in Brazil

“…Magrini [35] points out that other authors call for greater attention to the issue of what spatial scale is most appropriate for regional analysis [54][55][56]. Recently, Resende [3] analyses Brazilian economic growth on four spatial scales (municipalities, micro-regions, spatial clusters, and states) between the years of 1991 and 2000. Growth equations were systematically estimated-using the same time period and explanatory variables-across those spatial scales to demonstrate that the estimated coefficients change with the geographic scale.…”

“…the need for spatial models (and therefore, the use of W matrices) depends on the level of spatial aggregation used in the data. In other words, the empirical exercise conducted by Resende [3] shows that spatial autocorrelation appears only at finer scales. With the exception this latter work, the studies thus far have only examined the existence of spatial autocorrelation in the process of economic growth at a single spatial scale to infer the consistency of spatial growth models with reality (e.g., [4][5][6][7][8]).…”

“…However, as suggested by Resende [3] (Resende [3] focuses on the examination of the determinants of Brazilian regional economic growth at a variety of geographic scales using a cross-sectional data set only over the 1990s period.) the need for spatial models (and therefore, the use of W matrices) depends on the level of spatial aggregation used in the data.…”