Spatial effects and Verdoorn law in the Portuguese context

Martinho, Vítor João Pereira Domingues (2011): Spatial effects and Verdoorn law in the Portuguese context. Forthcoming in: International Journal of Academic Research , Vol. Volume, (2011)

Abstract

The consideration of spatial effects at a regional level is becoming increasingly frequent and the work of Anselin (1988), among others, has contributed to this. This study analyses, through cross-section estimation methods, the influence of spatial effects in productivity (product per worker) in the NUTs III economic sectors of mainland Portugal from 1995 to 1999 and from 2000 to 2005 (taking in count the availability of data), considering the Verdoorn relationship. To analyse the data, by using Moran I statistics, it is stated that productivity is subject to a positive spatial autocorrelation (productivity of each of the regions develops in a similar manner to each of the neighbouring regions), above all in services. The total of all sectors present, also, indicators of being subject to positive autocorrelation in productivity. Bearing in mind the results of estimations, it can been that the effects of spatial spillovers, spatial lags (measuring spatial autocorrelation through the spatially lagged dependent variable) and spatial error (measuring spatial autocorrelation through the spatially lagged error terms), influence the Verdoorn relationship when it is applied to the economic sectors of Portuguese regions.

• Item Type: MPRA Paper
• Language: English
• Keywords: Spatial Econometric; Verdoorn Law; Portuguese Regions
• Subjects: R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R5 - Regional Government Analysis > R58 - Regional Development Planning and Policy; O - Economic Development, Innovation, Technological Change, and Growth > O4 - Economic Growth and Aggregate Productivity > O40 - General; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions

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