Regional productivity patterns in Europe: An alternative approach

Cuadrado‐Roura, Juan R.; Navarro, Tomás Mancha; Yserte, Rubén Garrido

doi:10.1007/s001680000019

Cited by 45 publications

(29 citation statements)

References 5 publications

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“…As for identifying the exact role played by technological heterogeneity in this process, we are now required to assess whether our evidence is capable to discriminate between the two 14 Our result confirms the previous findings on the convergence process across the European regions; see, among many others, Neven and Gouyette (1995), Paci (1997), Tondl (1999), Magrini (1999, Cuadrado-Roura et al (2000). 15 Our conclusion would be wrong if our measure of the propensity to innovate turned out to be (a) uncorrelated with the (uniform) technology levels, and (b) positively correlated with the (heterogeneous) propensity to accumulate human capital, which we do not include in our regression.…”

supporting

confidence: 74%

Technological Diffusion, Spatial Spillovers and Regional Convergence in Europe

Paci

Pigliaru

2002

Advances in Spatial Science

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In this paper we study two closely related issues. First, the role of technology heterogeneity and diffusion in the convergence of GDP per worker observed across the European regions, in the absence of data on regional TFP. Second, the spatial pattern of the observed regional heterogeneity in technology and the relevance of this pattern for the econometric analysis of regional convergence in Europe. As for the first issue, our aim is to assess whether the convergence observed across European regions is due to convergence in technology as well as to convergence in capital-labor ratios. We first develop a growth model where technology accumulation in lagging regions depends on their own propensity to innovate and on technology diffusion from the leading region, and convergence in GDP per worker is due to both capital deepening and catch-up. We use data (1978-97) on 131 European regions. Propensities to innovate are computed by assigning each patent collected by the European Patent Office to its region of origin. Our findings are consistent with the hypothesis that technology differs across regions and that convergence is partly due to technological catch-up. As for the second empirical issue, we study to what extent each region's propensity to innovate is correlated with that of the surrounding regions. Our results show, first, that the performance of each region does depend on that of the surrounding areas. Second, that the intensity of such spillovers fades with distance. Taken together, these findings suggest the existence of significant localized spillovers of technological knowledge. Finally, we show that these spillovers are strong enough to play a role that cannot be ignored in the econometric analysis of the convergence process in Europe.JEL: O41, O33,

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confidence: 74%

Technological Diffusion, Spatial Spillovers and Regional Convergence in Europe

Paci

Pigliaru

2002

Advances in Spatial Science

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“…Villaverde and Maza (2008) found a weak beta convergence process at the aggregate and sectoral levels for the European regions. These findings are in line with those obtained by CuadradoRoura, Mancha-Navarro, and Garrido-Yserte (2000) and López-Bazo et al (2004) at the aggregate level. Also, the results of Le Gallo and Dall'erba (2008) indicated productivity convergence for the total economy and for services, but not for other sectors.…”

Section: Introductionsupporting

confidence: 92%

Productivity clustering and growth in Central and Eastern Europe

Niţoi

Pochea

2016

Baltic Journal of Economics

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This article uses a non-linear time-varying model to test productivity convergence in 10 emerging countries within Central and Eastern Europe. The results show that the convergence algorithm has rejected the null hypothesis of convergence for all countries in most of the sectors. Also, we found evidence that the productivity clusters for total economy and other sectors are very different in terms of number and countries. Additionally, even if the productivity gaps in the region have been reduced, we still notice significant disparities between countries. The clustering algorithm shows countries which have a high productivity growth in some sectors and a low productivity growth in others. This reveals the prevalence of idiosyncratic factors in productivity determinants. Baltic countries are catching up, while other countries such as Bulgaria are underperformers. ARTICLE HISTORY

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“…Dollar and Wolff (1994) and Bernard and Jones (1996 a,b) are early exceptions at a national level. Recent papers drawing on regional data have emphasized the importance of a disaggregate analysis at sectoral level (Paci and Pigliaru 1997;Cuadrado-Roura et al 2000;Esteban 2000;Sorenson 2001;De la Fuente 2002;TumpelGugerell and Mooslechner 2003;Carluer 2004). They suggest that to take the continuous process of sectoral reallocation of resources into account could be decisive in understanding growth and convergence.…”

Section: Introductionmentioning

confidence: 99%