Growth spillover through trade: A spatial dynamic panel data approach

Ho, Chun‐Yu; Wei, Wang; Yu, Jihai

doi:10.1016/j.econlet.2013.05.027

Cited by 54 publications

(43 citation statements)

References 15 publications

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“…This is consistent with the evidence reported in Ertur and Koch (), Lee and Yu (), and Ho et al. (). In the literature, the implied growth rate is 2–4% with samples of countries, whereas this paper estimates a rate of about 8% for a provincial sample in China.…”

Section: Estimation Resultssupporting

confidence: 94%

“…Following Mankiw, Romer, and Weil (), Barro and Sala‐i Martin (), Ertur and Koch (), and Ho et al. (), we suppose that all regions have the same convergence rate λ. Assume that the intervening time period is one year, then Equation has the matrix form

{boldY}_{boldNt} = ρ {boldW}_{boldNt} {boldY}_{boldNt} + φ {boldY}_{N, t - 1} + θ {boldW}_{N, t - 1} {boldY}_{N, t - 1} + {boldX}_{boldNt} β + c {boldl}_{boldN} + {boldV}_{boldNt},

where

\begin{matrix} true \\ right {boldY}_{boldNt} & = & left {false(prefixln y_{1 t}, prefixln y_{2 t}, \dots, prefixln y_{N t} false)}^{'}, \\ right {boldX}_{boldNt} & = & left false[prefixln S_{Nt}, prefixln N_{Nt}, W_{Nt} prefixln S_{Nt}, W_{Nt} prefixln N_{Nt} false], \\ right prefixln S_{Nt} & = & left {false(prefixln s_{1 t}, prefixln s_{2 t}, \dots, prefixln s_{N t} false)}^{'}, \\ right prefixln N_{Nt} & = & left false(prefixln (n_{1 t} + g + δ), prefixln (n_{2 t} + \end{matrix}

…”

Section: Modelmentioning

confidence: 99%

“…Intuitively, railroads can promote markets, people and ideas access to each other, and therefore provide a benefit to the economy. Spillovers on economic growth have been studied in the framework of endogenous growth models with spatial externalities (Ertur & Koch, ; Ho, Wang, & Yu, ; Yu & Lee, ), our theoretical work extends the model by allowing that railroad construction and the increasing train speed constantly affects temporal‐spatial externalities over time. In this paper, new railroads and faster trains can shorten temporal‐spatial distances and strengthen spillover effects among economies.…”

Section: Introductionmentioning

confidence: 99%

“…As the recent development of the spatial model with time‐variant spatial distances (Ho et al., ; Lee & Yu, ; Wang & Yu, ), this paper models railroads as a dynamic network instead of a static network or isolated infrastructure. This paper assumes that there exist temporal‐spatial externalities among nearby areas linked by railroads.…”

Section: Introductionmentioning

confidence: 99%

“…The empirical work of this paper estimates the effects of investment rates, population growth rates, and neighboring region values of those on real income per capita. Our estimation follows growing literature that focuses on modeling and estimation of a panel data model with time‐varying spatial distances (see Ho et al., ; Lee & Yu, ; Wang & Yu, , etc.). We find significant positive spatial externalities, and that all coefficients are in the predicted directions.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Railroad development, temporal‐spatial externalities, and growth spillover: Theory and empirical evidence

Chen

Wei

2018

Journal of Regional Science

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This paper develops an extended Solow model by considering the time-variant spillover effect of interregional economies. Instead of using fixed spatial distances among regions, the regions' temporalspatial distances are measured by constructing a database of train types and calculating the fastest region-to-region routes. We collect provincial economic data and railroads data over the period 2003-2012 in China to establish a dynamic temporal-spatial panel model. The estimation results indicate significant temporal-spatial external-ities. Compared with classical spatial models, the temporal-spatial model not only has a much better goodness-of-fit but also has consistent total effects, direct effects and indirect effects. K E Y W O R D S growth, railroad development, travel time, temporal-spatial externalities

show abstract

Section: Estimation Resultssupporting

confidence: 94%

{boldY}_{boldNt} = ρ {boldW}_{boldNt} {boldY}_{boldNt} + φ {boldY}_{N, t - 1} + θ {boldW}_{N, t - 1} {boldY}_{N, t - 1} + {boldX}_{boldNt} β + c {boldl}_{boldN} + {boldV}_{boldNt},

where

\begin{matrix} true \\ right {boldY}_{boldNt} & = & left {false(prefixln y_{1 t}, prefixln y_{2 t}, \dots, prefixln y_{N t} false)}^{'}, \\ right {boldX}_{boldNt} & = & left false[prefixln S_{Nt}, prefixln N_{Nt}, W_{Nt} prefixln S_{Nt}, W_{Nt} prefixln N_{Nt} false], \\ right prefixln S_{Nt} & = & left {false(prefixln s_{1 t}, prefixln s_{2 t}, \dots, prefixln s_{N t} false)}^{'}, \\ right prefixln N_{Nt} & = & left false(prefixln (n_{1 t} + g + δ), prefixln (n_{2 t} + \end{matrix}

…”

Section: Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Railroad development, temporal‐spatial externalities, and growth spillover: Theory and empirical evidence

Chen

Wei

2018

Journal of Regional Science

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Does the legacy of state planning put pressure on ecological efficiency? Evidence from China

et al. 2022

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Building a green and healthy economic system and achieving high‐quality economic growth have long been the development goals pursued by the Chinese government. Under the local government competition system, coordinating the relationship between economic growth and ecological sustainability is a decisive factor for China's high‐quality development. As a former state planned economy, China inherited a legacy of setting economic growth targets at various levels of government. This paper analyzes the relationship between economic growth targets and ecological efficiency by using panel data models and spatial econometric techniques. Moreover, the mediating effect model and dynamic threshold effect model are used to study their influence mechanism and the possible nonlinear relationship. The results indicate that there is an inverted U‐shaped relationship between economic growth targets and ecological efficiency. Optimizing industrial structure and R&D investment can improve regional ecological efficiency. The economic growth targets in neighboring regions have a “U”‐shaped relationship with local ecological efficiency. The mechanism test results suggest that economic growth targets affect ecological efficiency by influencing financial development, foreign direct investment, infrastructure investment, environmental regulation, and technological innovation. The impact of economic growth targets on ecological efficiency shows a significant threshold effect when economic growth targets, industrial structure, R&D levels, financial development, foreign direct investment, infrastructure investment, and environmental regulation are at different threshold values. Therefore, to promote regional healthy competition and ecological efficiency, the government should formulate scientific and reasonable economic growth goals and improve the official evaluation system.

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Financial development, institutions, and economic growth nexus: A spatial econometrics analysis using geographical and institutional proximities

Ahmad

Law

2023

Int. J Fin Econ

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This paper investigates the nexus between financial development (FD), institutions, and economic growth by employing a spatial autoregressive model on a panel dataset covering 82 countries from 1990 to 2019. The spatial dependence between countries is measured via geographical and institutional proximities, the latter hitherto has rarely been explored in the finance-growth literature. Institutional proximity concept postulates that institutionally similar countries are expected to have similar level of economic growth and greater size of spillover once the spatial effects of FD and institutional quality are controlled for. Overall, the findings give empirical support to the above proposition, as FD and political institutions are shown to have significant positive effects on growth. In the case of FD, its growth-effect is beneficial up to a certain threshold beyond which it becomes negative. The results also find significant positive spatial lag growth term in the model indicating the presence of indirect spillover effects of FD and institutions onto the growth of neighbouring countries, both in geographical institutional spheres. Furthermore, the spatial growth model with institutional proximity matrix is shown to have higher rate of convergence and greater size of spillover than the model with geographical proximity. These findings are robust to various model specifications, and the paper concludes with some policy recommendations.

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Growth spillover through trade: A spatial dynamic panel data approach

Cited by 54 publications

References 15 publications

Railroad development, temporal‐spatial externalities, and growth spillover: Theory and empirical evidence

Railroad development, temporal‐spatial externalities, and growth spillover: Theory and empirical evidence

Does the legacy of state planning put pressure on ecological efficiency? Evidence from China

Financial development, institutions, and economic growth nexus: A spatial econometrics analysis using geographical and institutional proximities

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