Growth and Convergence in a Multiregional Model with Space–Time Dynamics. 多区域时空动态模型的增长与收敛

Elhorst, J. Paul; Piras, Gianfranco; Arbia, Giuseppe

doi:10.1111/j.1538-4632.2010.00796.x

Cited by 98 publications

(49 citation statements)

References 30 publications

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“…The switch from a single cross-section to a panel framework is made possible by dividing the entire period into several shorter time spans. This is the most common approach to using five-year time intervals rather than a year-by-year specification, because short-run variations in income growth rates are inevitably influenced by business cycle fluctuations [9,28,29,42]. In line with these motivations, we use five-year growth rates rather than a year-by-year specification.…”

Section: Spatial Dependence In the Panel Data Convergence Modelmentioning

confidence: 99%

“…Atems [26] examines the dynamic relationship between income inequality and economic growth using US county-level data and finds a significant negative relationship between inequality and growth across the 3109 counties of the US. Recently, the growth regression has been extended to consider the role of spatial effects in the panel data analysis [27,29]. Using the dynamic spatial dependence model for panel data, Atems [27] examines the spatial dynamics of income inequality and economic growth for the 3109 counties of the US over the period from 1970 to 2007.…”

Section: A Brief Survey Of the Empirics Of Regional Income Convergencementioning

confidence: 99%

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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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Section: Spatial Dependence In the Panel Data Convergence Modelmentioning

confidence: 99%

Section: A Brief Survey Of the Empirics Of Regional Income Convergencementioning

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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“…These approaches were originally focused on crosssectional (Anselin, 1988;Anselin and Bera, 1998;Anselin, 2006) and static panel datasets (Elhorst, 2003) and they have been extended to the case of dynamic panel estimators (Badinger, Müller and Tondl, 2004;Yu, de Jong and Lee, 2008). Recently, further approaches have been introduced, such as including both spatial lag and spatial error simultaneously (Kelejian and Prucha, 1998;Lee, 2003) or including spatially weighted independent variables (the so-called spatial Durban model, see, e.g., Elhorst, Piras and Arbia, 2006;Ertur and Koch, 2007). Unfortunately, there is as yet no estimator which controls for both spatial spillover and endogeneity of further independent variables (besides the lagged dependent variable) within a panel data framework.…”

Section: Main Econometric Issues and Potential Solutionsmentioning

confidence: 99%

Econometric Evaluation of EU Cohesion Policy: A Survey

Hagen

Mohl²

2016

Empirical Evidence on the Macroeconomic Effects of EU Cohesion Policy

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Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW.Download this ZEW Discussion Paper from our ftp server:ftp://ftp.zew.de/pub/zew-docs/dp/dp09052.pdf Non-technical summaryMore than one third of the total EU budget is spent on so-called Cohesion Policy via the structural funds. Its main purpose is to reduce disparities among EU regions and to promote economic growth and convergence. Therefore, the convergence process of EU regions is a question of high political importance. The overall empirical evidence points to a small convergence effect of all or at least some European regions. However, whether or not this potential success results from EU Cohesion Policy remains an open question. The existing empirical evidence has provided mixed, if not contradictory, results. While some authors do find evidence of a positive impact of structural funds on economic growth, others find little to no impact at all. Against this background, this paper provides a fundamental review of the econometric evaluation of EU Cohesion Policy in order to shed light on the reasons for the diverging results. It has been shown that the econometric evaluation of EU Cohesion Policy is hampered by several econometric issues. Based on these issues we discuss potential solutions on how to cope with these problems and discuss the related literature.The most that can be concluded from empirical studies using country-level data is that Cohesion Policy seems to be only conditionally effective. Given a good quality institutional setup or decentralised governmental structures, Cohesion Policy has a positive impact on growth. However, using regional level data might be the preferable alternative because, first, EU Cohesion Policy focuses on the development and convergence of regions and, second, the robustness of the results is increased by the higher number of cross sections. The majority of the studies based on EU regions find at least a weak positive effect.One explanation for the weak results might be the fact that almost all studies are derived from a neoclassical growth model assuming that EU Cohesion Policy increases investments, and ultimately raising the economic growth rate. However, there is some empirical evidence that Cohesion Policy may only have a modest impact on investments. Moreover, we know very little about the labour market impact of EU Cohesion Policy. Hence, one task for future studies will be to investigate more thoroughly the channels through i which EU Cohesion Policy works.Another reason for the inconclusive empirical results m...

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“…Following the strategy proposed by Elhorst [41], investigators should start with the spatial Durbin model (SDM) as a general specification and test for alternatives. We estimated the SDPM, but we also wanted to know if it was the best model for the data at hand.…”

Section: Model Specificationmentioning

confidence: 99%

Modeling the Dynamics and Spillovers of the Health Labor Market: Evidence from China’s Provincial Panel Data

Zhu

Liu

2018

Sustainability

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Abstract:Health workforce misdistribution is a major challenge faced by almost all countries. A more profound understanding of the dynamics of the health labor market provides evidence for policy makers to balance health workforce distribution with solid evidence. However, one major deficit of existing theoretical and empirical studies is that they often ignore the intra-regional spillovers of the health labor market. This study builds a theoretical "supply-demand-spillover" model that considers both intra-regional supply and demand-side factors, and inter-regional spillovers, hence providing a theoretical reference point for further in-depth studies. Using spatial econometric panel models, the effect of all determinants and spillovers were empirically measured based on a Chinese panel data set, shedding light on health workforce policies in China.

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Growth and Convergence in a Multiregional Model with Space–Time Dynamics. 多区域时空动态模型的增长与收敛

Cited by 98 publications

References 30 publications

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

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

Econometric Evaluation of EU Cohesion Policy: A Survey

Modeling the Dynamics and Spillovers of the Health Labor Market: Evidence from China’s Provincial Panel Data

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