Mostrando recursos 1 - 3 de 3

  1. The impact of knowledge capital on regional total factor productivity

    LeSage, James P.; Fischer, Manfred M.
    This paper explores the contribution of knowledge capital to total factor productivity differences among regions within a regression framework. The dependent variable is total factor productivity, defined as output (in terms of gross value added) per unit of labour and physical capital combined, while the explanatory variable is a patent stock measure of regional knowledge endowments. We provide an econometric derivation of the relationship, which in the presence of unobservable knowledge capital leads to a spatial regression model relationship. This model form is extended to account for technological dependence between regions, which allows us to quantify disembodied knowledge spillover impacts arising from both spatial and technological proximity. A...
    (application/pdf) - 18-oct-2016

  2. Knowledge spillovers and total factor productivity. Evidence using a spatial panel data model

    Fischer, Manfred M.; Scherngell, Thomas; Reismann, Martin
    This paper investigates the impact of knowledge capital stocks on total factor productivity through the lens of the knowledge capital model proposed by Griliches (1979), augmented with a spatially discounted cross-region knowledge spillover pool variable. The objective is to shift attention from firms and industries to regions and to estimate the impact of cross-region knowledge spillovers on total factor productivity (TFP) in Europe. The dependent variable is the region-level TFP, measured in terms of the superlative TFP index suggested by Caves, Christensen and Diewert (1982). This index describes how efficiently each region transforms physical capital and labour into output. The explanatory variables are internal and out-of-region stocks of knowledge,...
    (application/pdf) - 05-may-2018

  3. Remaining within-cluster heterogeneity: a meta-analysis of the "dark side" of clustering methods

    Franke, Nikolaus; Reisinger, Heribert; Hoppe, Daniel
    In a meta-analysis of articles employing clustering methods, we find that little attention is paid to remaining within-cluster heterogeneity and that average values are relatively high. We suggest addressing this potentially problematic "dark side" of cluster analysis by providing two coefficients as standard information in any cluster analysis findings: a goodness-of-fit measure and a measure which relates explained variation of analysed empirical data to explained variation of simulated random data. The second coefficient is referred to as the Index of Clustering Appropriateness (ICA). Finally, we develop a classification scheme depicting acceptable levels of remaining within-cluster heterogeneity. (authors' abstract)
    (application/pdf) - 18-oct-2016

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