Abstract
We present a new empirical approach for identifying sub-centers within urban areas and apply it to the São Paulo metropolitan area (SPMA). We use geographically weighted regressions (GWR) to overcome the limitations presented by previous methods, which rely on previous knowledge of the employment distribution and use arbitrary threshold values and band sizes. We find three SBD in 2002 and only two in 2014, suggesting that SPMA is polycentric but presents only one business core that concentrates more than 90% of all employees working in an SBD. We apply the widely recognized method of McMillen and Smith (2003) to our database and compare the results. Our method is more conservative in identifying areas as sub-centers (SBD) and presents lower standard errors.
Highlights
An extensive literature discusses the role of the Central Business Districts (CBD) and Subcenter Business Districts (SBD) areas in the urban economy and how these areas affect wages, land prices, and the transportation system
Empirical results derived from monocentric models identify the CBD as the area with the highest land prices and wages, as compared to any other intra-urban region, and these prices decrease with distance to the CBD (Alonso, 1964; Beckmann, 1974; Muth, 1969, 1975; Mills, 1967, 1972; Solow, 1973; Wheaton, 1974)
Rents and wages are higher in the SDB than anywhere else but the CBD, due to the supply of infrastructure and proximity to the workplaces (Papageorgiou, 1971; White, 1976, 1988, 1999; Hartwick and Hartwick, 1974; Romanos, 1979; Sullivan, 1986; Wieand, 1987; Sivitanidou and Wheaton, 1992; Hotchkiss and White, 1993; Yinger, 1992; Ross and Yinger, 1995; Wrede, 2015)
Summary
An extensive literature discusses the role of the Central Business Districts (CBD) and Subcenter Business Districts (SBD) areas in the urban economy and how these areas affect wages, land prices, and the transportation system. Azzoni existence of one or more sub-centers results from the decentralization of economic activity and is taken as given (White, 1976, 1988, 1999; Romanos, 1979; Sullivan, 1986; Wieand, 1987; Sivitanidou and Wheaton, 1992; Papageorgiou, 1971; Hartwick and Hartwick, 1974; Hotchkiss and White, 1993; Yinger, 1992; Ross and Yinger, 1995; Henderson and Mitra, 1996; Zhang and Komei, 1997, 2000; Wrede, 2015) These models’ focus is to evaluate how households and workers decide on where to live and work and the resulting spatial pattern of land prices, population density, and commuting.
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