Convergence Clubs and Spatial Externalities: Models and by Stilianos Alexiadis

By Stilianos Alexiadis

Do dynamic externalities, within the type of know-how production, adoption and spatial agglomeration form the trend of local progress in Europe? This learn presents another view on nearby convergence. A version is constructed which attributes club-convergence to current transformations with recognize to the measure of know-how adoption. within the first example, empirical effects recommend that the NUTS-2 areas of the EU-27 converge at a truly sluggish expense. additional exams, even if, point out that convergence is specific to a selected subset of areas. Such conclusions are validated extra, utilizing an alternate version of club-convergence, which contains the effect of spatial interplay, agglomeration externalities and expertise. This exhibits that the convergence-club in Europe follows a undeniable geographical development and all contributors percentage related features concerning expertise production and adoption, and agglomeration externalities. ​

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Additional info for Convergence Clubs and Spatial Externalities: Models and Applications of Regional Convergence in Europe

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Furthermore, persistent variations in rates of technological progress have the potential to accentuate or reduce the extent to which regions converge to each other, depending upon which particular regions experience higher or lower rates of progress. Thus, technology and technological change are fundamental to the question of convergence, or nonconvergence, between regions, and more detailed discussion of these issues is therefore provided in Chaps. 3 and 4. At this juncture, in the context of the one-sector neoclassical model, it may be argued that rates of technological progress will tend to be the same across different regions, for the following reasons.

2010). Consider the production function:yi;t ¼ f ðki;t Þ, where yi;t ¼ Y i;t =Ai;t Li;t and ki;t ¼ K i;t = Ai;t Li;t. Let Li;t ¼ Li;0 ent, Ai;t ¼ Ai;0 egt, L_i;t =Li;t ¼ n and A_ i;t =Ai;t ¼ g, then AL_i;t =ALi;t ¼ n þ g. Given thatY i;t ¼ Ci;t þ Si;t ) Si;t ¼ Y i;t À Ci;t , K_ i;t ¼ I i;t À dK i;t , where Ii;t is net investment, and Si;t ¼ s Y i;t Si;t  Ii;t , then K_ i;t ¼ Si;t À dK i;t ) K_ i;t ¼ Y i;t À Ci;t À dK i;t . Dividing by Ai;t Li;t yields: K_ i;t =Ai;t Li;t ¼ yi;t À ci;t À dki;t ) K_ i;t =Ai;t Li;t ¼ f ðki;t Þ À ci;t À dk , where ci;t ¼ Ci;t =Ai;t Li;t and k_i;t =ki;t ¼ K_ i;t =K i;t À AL_i;t =ALi;t .

Second, as previously noted, the rate of return on capital is lower in economies with more capital per-worker. Additional capital investment will be less profitable, due to diminishing marginal productivity of capital. e. from poor to rich economies. This will generate tendencies to convergence. Third, if there are lags in the diffusion of knowledge, differences in output per-worker might emerge due to the fact that some economies are not yet employing the best available technologies. As poor economies obtain access to new technologies, income gaps between poor and rich economies will tend to narrow.

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