Regional Externalities And Growth: Evidence From European Regions*

López‐Bazo, Enrique; Vayá, Esther; Artı́s, Manuel

doi:10.1111/j.1085-9489.2004.00327.x

Cited by 202 publications

(134 citation statements)

References 52 publications

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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%

“…Moreover, the productivity growth and convergence within the EU are fundamentals of the Lisbon strategy (Denis, Mc Morrow, & Veugelers, 2005) and remain an important pillar of the Europe 2020 growth strategy (European Commission, 2010). The economic literature indicates the positive effects that an increased productivity can have on economic convergence and on reducing the development gaps (Ezcurra, Gil, Pascual, & Rapún, 2005;Gardiner, Martin, & Tyler, 2004;Inklaar & Timmer, 2009;López-Bazo, Vayá, & Artís, 2004). Together with this, Krugman (1994) states that productivity is highly important in the long run.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Productivity clustering and growth in Central and Eastern Europe

Niţoi

Pochea

2016

Baltic Journal of Economics

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“…Indeed, the evolution of each region is closely related to the evolution of, at least, neighboring regions. From a theoretical point of view, the inclusion of spatial dependence in regional income convergence models has been motivated by Armstrong [43], Rey and Montouri [18], and López-Bazo et al [44]. In this context of spatial dependence, the distribution of regional per capita income is unlikely to be spatially independent and random.…”

Section: Spatial Dependence In the Panel Data Convergence Modelmentioning

confidence: 99%

Toward Sustainable Economic Growth: A Spatial Panel Data Analysis of Regional Income Convergence in US BEA Economic Areas

Lim

Kim

2015

Sustainability

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Abstract:A question fundamental to sustainable economic growth is whether a poor region tends to grow faster than a rich one, such that the poor region catches up with the rich region in terms of the level of per capita income. In this article, we apply the spatial panel data approach to the analysis of regional income convergence across 177 economic areas in the contiguous US states over the period from 1969 to 2009. Using data on per capita incomes in the functionally defined economic areas, we find that the absolute value of the estimated coefficient of the initial per capita income decreases in the spatial and time-period fixed effects spatial lag model and increases in the spatial and time-period fixed effects spatial error model. This result implies that the growth rate in a specific economic area will be not only directly affected by an exogenous shock introduced into that economic area but also be impacted more by both the indirect effects of the first-order neighboring economic areas and the induced effects of the higher-order neighboring economic areas. This gives helpful hints on the issue of spatial interaction and regional policy coordination to start a virtuous circle of sustainable economic growth.

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“…To control for the industrial mix, we follow López-Bazo et al (2004) and consider the logged levels of employment in: agriculture, construction, foodÁbeveragesÁtobacco, textiles, fuelsÁchemicalsÁrubber, electronics, transportation equipment, other manufactures, wholesaleÁretail trade, hotelsÁrestaurants, transportationÁpublic utilities, finance, and other services. We also include an index of market potential that measures the export demand each region faces given its geographical location and that of its trading partners.…”

Section: The Sample Datamentioning

confidence: 99%

Spatial Growth Regressions: Model Specification, Estimation and Interpretation

LeSage¹,

Fischer²

2008

Spatial Economic Analysis

382

237

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We attempt to clarify a number of points regarding use of spatial regression models for regional growth analysis. We show that as in the case of non-spatial growth regressions, the effect of initial regional income levels wears off over time. Unlike the non-spatial case, long-run regional income levels depend on: own region as well as neighbouring region characteristics, the spatial connectivity structure of the regions, and the strength of spatial dependence. Given this, the search for regional characteristics that exert important influences on income levels or growth rates should take place using spatial econometric methods that account for spatial dependence as well as own and neighbouring region characteristics, the type of spatial regression model specification, and weight matrix. The framework adopted here illustrates a unified approach for dealing with these issues.Régressions de croissance spatiale: spécification de modèles, estimation et interprétation RÉ SUMÉ Nous efforçons de clarifler un certain nombre de questions concernant I'utilisation de mode´les de regression spatiale pour l'analyse de l'expansion re´gionale. Nous de´montrons que, tout comme dans le cas de re´gressions non spatiales de la croissance, l'effet initial des niveaux de revenus re´gionaux finit par s'estomper. Contrairement au cas non spatial, le niveaux de revenus re´gionaux a`long terme sont tributaires: des caracte´ristiques de notrepropre re´gion et de celles des re´gions environnantes; de la structure de connectivite´spatiale des re´gions; et de la force de la de´pendance spatiale. En conse´quence, la recherche de caracte´ristiques re´gionales exerçant une grande influence sur les niveaux de revenus ou les taux de croissance doit eˆtre effectue´e enfaisant usage de me´thodes e´conome´triques spatiales tenant compte a`lafois de la de´pendance spatiale et des caracte´ristiques de la re´gion en question et des re´gions environnantes; du type de spe´cification du mode´le de re´gression spatial; et de la matrice de ponderation. Le cadre adopte´ici illustre line me´thode unifie´e pour aborder ces questions.Regresiones de crecimiento espacial: especificació n, estimació n e interpretació n de modelo RESUMEN Intentamos clarificar una serie de puntos relacionados con el uso de modelos de regresio´n espacial para el ana´lisis del crecimiento regional. Mostramos que, como en el caso de regresiones de Spatial Economic Analysis, Vol. 3, No. 3, November 2008 crecimiento no espacial, el efecto de niveles de ingresos regionales iniciales desaparece con el tiempo. A diferencia del caso no espacial, los niveles de ingresos regionales a largo plazo dependen de: las características de la propia regio´n así como tambie´n de las regiones vecinas, la estructura de conectividad espacial de las regiones, y la fuerza de la dependencia espacial. Al ser así, la búsqueda de características regionales que tengan marcadas influencias sobre los niveles de ingresos o las tasas de crecimiento, debería usar me´todos espaciales econome´tricos que expli...

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Regional Externalities And Growth: Evidence From European Regions*

Cited by 202 publications

References 52 publications

Productivity clustering and growth in Central and Eastern Europe

Productivity clustering and growth in Central and Eastern Europe

Toward Sustainable Economic Growth: A Spatial Panel Data Analysis of Regional Income Convergence in US BEA Economic Areas

Spatial Growth Regressions: Model Specification, Estimation and Interpretation

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