Spatial Durbin Panel Data Model Research Articles

The third-country effects of CO2 emissions in BRI countries: A verification on China’s impacts by spatial Durbin panel data model

Since the WTO accession in 2001, China’s outward foreign direct investment (OFDI) and foreign trade have been leaping forward, especially reflected as the “One Belt & One Road” initiative (BRI), proposed in 2013, which has been its largest public product for international community thus far. On that basis, this study focuses on the third-country effects of carbon dioxide (CO2) emissions in BRI related countries, and delves into its spatial interaction from rising impacts of China’s global economic cooperation activities, with the economic & political distance from China as institutional factors, using spatial econometric method in a panel data of 60 BRI countries from 2007 to 2016. Accordingly, the empirical results first significantly reveal the existence of spatial autocorrelation for CO2 emissions per capita in BRI countries by Moran’s I test. Furthermore, according to the main estimated results of the spatial Durbin model with spatial fixed effects, China’s OFDI and imports to BRI countries exert third-country effects or spatial spillover effects; from the perspective of sustainability and emission reduction during BRI, China is required to launch more economic cooperation in emerging and low carbon industries with limited political claims. In response, BRI countries are supposed to develop its high tech and low carbon complementary industries with surrounding countries, so as to keep the CO2 emissions at a low level.

Investigating the Impacts of Urbanization on PM2.5 Pollution in the Yangtze River Delta of China: A Spatial Panel Data Approach

Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017; (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.

Regional convergence and spatial dependence: a worldwide perspective

This paper incorporates technological interdependence into a neoclassical regional growth framework with imperfect factor mobility, leading to a convergence equation with spatial effects. The empirical analysis is based on the estimation of a spatial Durbin panel data model and the implementation of multiple imputation techniques. Our results show that taking into account both unobserved heterogeneity and spatial dependence increases the estimated regional convergence rate. This provides an explanation for puzzling findings in the related literature. We also obtain evidence of heterogeneity across country groups regarding the regional speed of convergence and the degree of diffusion of technology.

Regional Convergence and Spatial Dependence: A Worldwide Perspective

This paper incorporates technological interdependence into a neoclassical regional growth framework with imperfect factor mobility, leading to a convergence equation with spatial effects. The empirical analysis is based on the estimation of a spatial Durbin panel data model and the implementation of multiple imputation techniques. Our results show that taking into account both unobserved heterogeneity and spatial dependence increases the estimated regional convergence rate. This provides an explanation for puzzling findings in the related literature. We also obtain evidence of heterogeneity across country groups regarding the regional speed of convergence and the degree of diffusion of technology.

Labor Absorption and the Growth of Agricultural Output: A Simultaneous Spatial Durbin Panel Data Model Perspective of Fiscal Decentralization’s Impact in Indonesia

This study aims to examine the empirical and short-term relationship between the implementation of fiscal decentralization policies and employment in Indonesia during the period from 2007-2015. We observe the impact of local government policies during the decentralization period through regional fiscal policy instruments, using the simultaneous equation of spatial Durbin panel data with the GMM approach. Significant changes that were expected from the post-centralization era after 1.5 decades of this policy have not been seen. Our main findings document that fiscal decentralization has not yet had a positive effect on employment and the agricultural sector’s production.

Broadband adoption in Latin American countries: does geographic proximity matter?

This paper analyzes whether geographic proximity among countries can be a significant factor in explaining fixed and mobile broadband adoption in Latin America. To address the research question if neighbor effects could impact on the broadband penetration of a country, we estimate a Spatial Durbin panel data model over the period 2011-2015, for 18 Latin American countries. Results obtained suggest a significant spatial effect of the broadband penetration. In particular, results show positive spatial effects for fixed broadband (confirming previous studies in Europe) but negative spatial effects for mobile broadband adoption.

Carbon emission effect of urbanization at regional level: empirical evidence from China

Abstract Historically, global urbanization has been an essential ingredient for national economic growth and beneficial social transformation. However, with the global urban population currently generating two-thirds of all carbon emissions, global policymakers are urging mayors and regional leaders to make difficult decisions to reduce the negative impacts of urbanization on the environment. The authors begin their examination of the implications of local and regional factors by applying the Dynamic Spatial Durbin Panel Data Model to empirically examine aspects of developing low-carbon strategies for the rapidly expanding size and number of the world’s urban areas. The results indicate that the contribution of urbanization to carbon emissions can be positively affected when regional policy makers collaborate to focus on spillover effects to simultaneously manage the scope, diversity, and complexity of economic and environmental issues from the perspective of creating a balance between rapid urbanization and relevant regional factors. Regional leaders can make a difference by creating both short-term goals and long-term strategies for maintaining low-carbon urbanization, nurturing regional coordination, monitoring and managing eco-friendly regional spillover effects, supporting low-carbon technology innovations, and maintaining optimal city size.

The direct and indirect drag effects of land and energy on urban economic growth in the Yangtze River Delta, China

This paper estimates the direct and indirect drag effects of land and energy on urban economic growth in the Yangtze River Delta in China over the period of 2003–2012. Using the spatial Durbin panel data model (SDPDM), we found that such urban economic growth is spatially autocorrelated and that resource input has a positive impact on economic growth. The SDPDM results show that the direct drag effect of land and energy is, respectively, 0.75 and 2.38%, indicating that the constraints of land and energy will reduce the economic growth rate by 0.75 and 2.38%. Besides, the indirect drag effect is 0.40 and 4.86%, suggesting that the focal unit’s economic growth would also be influenced by the behavior of its neighbors. Based on these results, this paper proposes some policy recommendations.

Agricultural R&D, spatial spillover and regional economic growth in different R&D sectors of performance: evidence from a spatial panel in regions of the EU-28

Agricultural R&D has been identified as an important determinant of economic output in the agricultural sector. Surprisingly, in previous studies, spatial spillover associated with R&D spending in the agricultural sector has not been taken into account. This paper investigates the effects of spatial spillover of agricultural R&D on regional economic growth across EU-28 NUTS-II regions in the period 1995–2014. In particular, we extend previous studies by considering spillover in all sectors of agricultural R&D performance including business enterprise, government and higher education. The spatial Durbin panel data model is employed to estimate brooders effect including direct and indirect effects. Empirical results show a positive effect of agricultural R&D and its spatial spillover on regional growth in all performance sectors. Moreover, the impact of spatial spillover of agricultural R&D on regional growth depends on the performance of the R&D sectors; positive spillovers are stronger in the business enterprise sector. Finally, the interaction effect between the economic output of the agricultural sector of each region with that of its neighbours is significantly positive.

What drives local government spending in Spain? A dynamic spatial panel approach

ABSTRACTWhat drives local government spending in Spain? A dynamic spatial panel approach. Spatial Economic Analysis. This paper extends traditional spatial spillover models of government spending by including dynamic effects and exogenous interaction effects. Using annual data for a sample of 3032 Spanish municipalities during 2000–12, we estimate a dynamic spatial Durbin panel data model to quantify the relevance of spatial spillovers and diffusion effects over time as well as the impact of a variety of spending determinants. We find that government spending at the local level is mainly explained by economic factors, while demographic factors and political factors appear to be less relevant.

Austrian Outbound Foreign Direct Investment in Europe: A Spatial Econometric Study

This paper focuses on Austrian outbound foreign direct investment (FDI, measured by sales of Austrian affiliates abroad) in Europe over the period 2009–2013, using a spatial Durbin panel data model specification with fixed effects, and a spatial weight matrix based on the first-order contiguity relationship of the countries and normalised by its largest eigenvalue. Third-country effects essentially enter the empirical analysis in two major ways: first, by the endogenous spatial lag on FDI (measured by FDI into markets nearby the host country), and, second, by including an exogenous market potential variable that measures the size of markets nearby the FDI host country in terms of gross domestic product. The question whether the empirical result is compatible with horizontal, vertical, export-platform or complex vertical FDI then depends on the sign and significance levels of both the coefficient of the spatial lag on FDI and the direct impact estimate of the market potential variable. The paper yields robust results that provide significant empirical evidence for horizontal FDI as the main driver of Austrian outbound FDI in Europe. This result is strengthened by the indirect impact estimate of the market potential variable indicating that spatial spillovers do not matter.

The public sector and convergence with spatial interdependence: empirical evidence from Spain

ABSTRACTWe propose a framework to analyse convergence between regions, incorporating the public sector and technological knowledge spillovers in the context of a Neoclassical Growth Model. Second, we apply novel estimation methods pertaining to the spatial econometrics literature introducing a spatial Durbin panel data model based on instrumental variables and maximum-likelihood estimation. Our model makes it possible to analyse, in terms of convergence, the results obtained in Spanish regions with the policies implemented during the period 1980–2011. The results support the idea that education and fiscal policies have a positive effect on regional development and cohesion. Therefore, we can conclude that it is possible to obtain better results for regional convergence with higher rates of public investment in education and tax revenues. We also obtain interesting results that confirm the existence of spillover effects in economic growth and public policies, identifying their magnitude and significance.

Evaluating the spatial spillover effects of transportation infrastructure on agricultural output across the United States

The role of spatial spillover associated with the infrastructure–economy relationship has received increasing attention in the literature. Surprisingly, in previous studies, the potential spatial spillover effect of transportation infrastructure on economic output in the U.S. agricultural sector has not been taken into account given the sector’s importance in the economy and dependence on transportation. The broader effects, including the direct effect and spatial spillover effect, of transportation infrastructure on agricultural output across 44 states in the United States from 1981 to 2004 are estimated in this study. A spatial Durbin panel data model that catches spatial dependence in both dependent and explanatory variables is used. The results suggest that road disbursement in a given state has positive direct effects on its own agricultural output. Also, the spillover effect of road infrastructure on agricultural output in neighboring states varies with respect to the spatial weight matrix used in the model. Moreover, our analysis shows that enhancement in road infrastructure in the states in the U.S. central region has a larger positive spillover effect on agricultural output when compared to coastal or border states.

The Mundlak Approach in the Spatial Durbin Panel Data Model

This paper extends the Mundlak approach to the spatial Durbin panel data model (SDM) to help the applied researcher to determine the adequacy of the random effects specification in this setup. We propose a likelihood ratio (LR) test that assesses the significance of the correlation between regressors and individual effects. By contrast to the Hausman test, the Mundlak approach identifies (to some extent) the regressors correlated with individual effects. The second advantage is that once the correlation with individual effects has been modelled through an auxiliary regression, the random effects specification provides consistent estimators and the effect of time-constant variables can be estimated. Some Monte Carlo simulations study the properties of this proposed LR test in small samples and show that in some cases, it has a better behaviour than the Hausman test. We finally illustrate the usefulness of the extended Mundlak approach by estimating a house price model where some of the price determinants are time-constant. We show that ignoring the endogeneity of regressors with respect to individual effects leads to unreliable estimated parameters while results obtained using the Mundlak approach and the fixed effects specification are similar (concerning time-varying variables), implying that correlation between regressors and individual effects is well captured. RÉSUMÉ la présente communication applique l'approche de Mundlak au modèle de données spatiales de Durbin pour aider le chercheur appliqué à déterminer dans quelle mesure la spécification des effets aléatoires est adéquate dans cette configuration. Nous proposons un test de ratio de vraisemblance évaluant l'importance de la corrélation entre régresseurs et effets individuels. Contrairement au test de Hausman, l'approche de Mundlak identifie (dans une certaine mesure) les régresseurs corrélés à des effets individuels. Le deuxième avantage est que lorsque la corrélation avec les effets individuels a été modélisée via une régression auxiliaire, la spécification des effets aléatoires fournit des estimateurs convergents, et il est alors possible d’évaluer l'effet de variables constantes dans le temps. Des simulations Monte Carlo étudient les propriétés de ce test de ratio de vraisemblance proposé dans des échantillons de taille finie, et indiquent que, dans certains cas, il présente un meilleur comportement que le test de Hausman. Nous illustrons enfin l'utilité de l'approche étendue de Mundlak en évaluant un modèle de prix des maisons, dans lequel certains déterminants des prix sont constants dans le temps. Nous montrons que si on ne prend pas en compte l'endogénéité des régresseurs par rapport aux effets individuels, on obtient des paramétres estimés non fiables, alors que les résultats obtenus avec l'approche de Mundlak et la spécification des effets fixes sont similaires (sur le plan des variables variant dans le temps), ce qui implique que la corrélation entre régresseurs et effets individuels est bien captée. EXTRACTO Este estudio extiende el planteamiento Mundlak al modelo espacial de datos de panel (SDM) Durbin para ayudar al investigador aplicado a determinar la idoneidad de la especificación de efectos aleatorios dentro de esta configuración. Proponemos una prueba de relación de la probabilidad (LR) que evalúa la significancia de la correlación entre regresores y efectos individuales. En contraste con la prueba Hausman, el planteamiento Mundlak identifica (hasta cierto punto) los regresores correlacionados con efectos individuales. La segunda ventaja es que, una vez modelada la correlación con efectos individuales a través de una regresión auxiliar, la especificación de efectos aleatorios proporciona estimadores consistentes y puede estimarse el efecto de las variables constantes en el tiempo. Algunas simulaciones de Monte Carlo estudian las propiedades de esta prueba LR propuesta en muestras pequeñas y demuestran que, en algunos casos, se comporta mejor que la prueba Hausman. Finalmente, ilustramos la utilidad del planteamiento Mundlak ampliado estimando el precio de una vivienda donde varios determinantes del precio son constantes en el tiempo. Mostramos que ignorar la endogeneidad de los regresores con respecto a efectos individuales conduce a parámetros estimados no fiables, mientras que los resultados obtenidos mediante el planteamiento Mundlak y la especificación de efectos fijos son similares (en lo concerniente a variables que varan en el tiempo), sugiriendo que la correlación entre regresores y efectos individuales se ha capturado satisfactoriamente