Dynamic Spatial Panel Research Articles

Regional per capita income inequality and fiscal policy in Mexico, 1989–2021

AbstractIn this paper, we use trends in GDP gaps per capita or relative GDP per capita indicator as an aggregate alternative measure to show regional per capita income inequality between the states of Mexico. The indicator compares each state's gross domestic product per capita (GDPpc) with the annual average of each year and 2019; this allows for evaluating growth performance before and after the COVID-19 pandemic. The econometric method is a spatial dynamic panel, and the convergence–divergence model is conditioned to the behavior of the fiscal policy followed in each state: The fiscal policy indicators are current expenditure and public investment. In general, it is concluded that the amounts of current spending and public investment, in an active fiscal policy context, tend to favor states with higher relative per capita income, which is why an increase in regional inequality is observed. However, it tends to affect low-income regions more, with higher ratios of current spending and investment to GDP, which reduces regional inequality. Therefore, the short-term effects of active fiscal policy to reduce regional disparities can be neutralized.

The effect of internal migration on regional growth in Italy: a dynamic spatial panel data analysis

This study assesses the effect of internal migration on regional growth in Italy at the NUTS-3 level over the period 2002-2019. The composition of the internal migration flows of the working-age population in Italy during the sample period appears substantially heterogeneous in nationality and labor skills. The analysis considers this heterogeneity, estimating various specifications of the dynamic spatial model and controlling for the endogeneity of migration variables through a control function approach. e evidence suggests that the internal migration of Italian citizens has a positive direct and spillover impact on regional growth, slowing down the convergence process. On the contrary, there is no evidence of a significant effect of internal migration of foreign citizens. Taking the skill composition of internal movements of Italian citizens into account, the adverse impact on convergence is magnified, thus corroborating the skill-selective hypothesis. Finally, the diverging impact of internal migration increases with the distance of migration flows.

Spatial–Temporal Development Trends and Influencing Factors of Government Environmental Information Disclosure: Empirical Evidence Based on China’s Provincial Panel Data

Government environmental information disclosure (GEID) plays an important role in promoting the digital transformation of environmental governance, leading the concept of sustainable development, enhancing public oversight capacity, and promoting democratic decision-making governance. Using provincial panel data from China spanning from 2009 to 2021, we conducted spatial data exploratory analysis and used the dynamic spatial panel model to investigate the spatial–temporal development trends and influencing factors of GEID. The results show that (1) GEID in China exhibits significant spatial agglomeration characteristics, with an “H-H” (High-High aggregation type) agglomeration characteristic observed in three national strategic development regions: Yangtze River Delta, southeast coastal areas, and Beijing–Tianjin–Hebei region. (2) The spillover effect from the southeast coastal provinces gradually radiates to the northwest, resulting in an overall westward movement of GEID. (3) GEID exhibits a significant path-dependency feature in the temporal dimension and a “peer effect” in the spatial dimension. (4) Population size has the greatest impact on GEID. Population size, public participation, and the industrial and transportation sectors positively influence GEID improvement at the local level. However, they generate negative spillover effects to neighbouring provinces. Environmental status and the size of the Real Estate sector have no significant effect. Therefore, China should strengthen regional cooperation, narrow regional disparities, cultivate new quality productive forces, establish a government-led proactive disclosure mechanism under public supervision, and improve the level of GEID at the national level.

Re-Examination of the Relationship between Industrial Agglomeration and Haze Pollution: From the Perspective of the Spatial Moderating Effect of Environmental Regulation

This paper uses panel data from 284 Chinese cities from 2004 to 2020 and employs a dynamic spatial panel Durbin model to re-examine the relationship between industrial agglomeration, environmental regulation, and haze pollution. It further adopts a dynamic spatial moderation effect model to explore the spatial regulatory mechanism of environmental regulation. The results show that both local and neighboring industrial agglomeration have a significant “inverted U-shaped” relationship with local haze pollution, and the scale cumulative optimization effect can only be effectively played after the industrial agglomeration level of the locality and neighboring areas exceeds the inflection point. Local environmental regulation significantly inhibits haze pollution, while neighboring environmental regulation plays a promoting role. The moderating effect of environmental regulation on the relationship between industrial agglomeration and haze pollution shows spatial heterogeneity in the local and neighboring areas. Local environmental regulation has a “U-shaped” non-linear moderating effect while neighboring environmental regulation has a positive linear moderating effect. Therefore, the government should pay attention to the joint effort and coordinated advancement of industrial agglomeration and environmental regulation to further reduce urban haze pollution and enhance urban air quality.

Ecological footprint spillover effects in Europe: New insights from dynamic spatial panel data model with common shocks

This comprehensive study addresses the urgent global challenges of climate change and environmental degradation by focusing on the Ecological Footprint (EF). Unlike previous studies, it introduces a novel approach incorporating spatial spillover, temporal effects, and common shocks in panel data analysis. The spatial spillover effect highlights the influence of trade, pollution havens, and competition between neighboring countries on EF. The temporal effects emphasize the significance of historical production patterns and export strategies in shaping the current EF. The study also considers the impact of exogenous common shocks, such as international agreements and global events, on EF. Utilizing a dynamic spatial panel data model with common shocks, the research examines 40 European countries from 1992 to 2020, revealing the significant impact of biocapacity, energy consumption, industrialization, and globalization on EF. Findings indicate that spatial spillover effects contribute to EF transfer, emphasizing the need for collaborative global efforts. The study sheds light on the interconnectedness of environmental impacts and underscores the importance of considering both weak and strong forms of cross-sectional dependence in achieving accurate estimations. The research enriches our understanding of EF determinants and provides nuanced insights for policymakers striving to develop effective strategies for sustainable resource management and environmental conservation.

Unveiling the impact and mechanism of digital technology on agricultural economic resilience

Enhancing the economic resilience of agriculture is essential for promoting sustainable and high-quality agricultural development. The emergence of digital technology has created new opportunities in this field. However, existing research predominantly focuses on traditional agricultural factors and technologies. Therefore, the impact of digital technology on agricultural economic resilience within the broader context of the “production–operation–industry” system in agriculture has not been comprehensively explored. To bridge this gap, this study analyzes panel data from 30 Chinese provinces from 2011 to 2020. It employs the static Van Dorn’s law and a dynamic spatial panel model to examine how digital technology empowers agricultural resilience. The findings indicate a continuous strengthening of digital technology development in China, albeit with significant polarization and spatial imbalances. Moreover, the resilience of the agricultural economy undergoes notable fluctuations, initially narrowing and subsequently displaying an upward trend. Digital technology clearly plays a pivotal role in empowering resilience through agricultural scale operation, industrial transformation, and technological progress. Its impact, particularly on the promotion of resilience in the eastern region and non-grain-producing areas and on high-level agricultural economies, also shows regional and technological variations.

Inference of Dynamic Spatial Panel Data Model and Its Application in Carbon Emission Analysis

The Spatial Dynamic Panel Data (SDPD) model is a widely used statistical model in the fields of economics and social sciences and has been the subject of extensive research by many scholars in recent years. Existing methods for parameter estimation primarily focus on improvements to the Generalized Method of Moments (GMM) and Quasi-Maximum Likelihood (QML). In this paper, we employ the method of Empirical Likelihood (EL) for statistical inference of the dynamic spatial panel data model and obtain confidence regions for the parameters. Through numerical simulations, we present the performance of the confidence regions obtained using the Empirical Likelihood (EL) and the Asymptotically Normal (NA) methods under finite samples and compare the two approaches. Finally, we analyze carbon using the suggested model and techniques.

Demand and distribution in a dynamic spatial panel model for the United States: Evidence from state‐level data

AbstractWe estimate a modified demand‐and‐distribution system for the 48 contiguous US states and the District of Columbia (DC) employing dynamic spatial panel data for 1980–2019. We allow for endogenous regressors, test for the presence, significance, and magnitude of spatial spillovers, and estimate immediate and cumulative effects on our endogenous variables of interest. Without testing for spatial dependence and spillovers, we estimate that output growth and capacity utilization in the sample US states and DC rise in response to an increase in their own wage share. When we test for spatial dependence and spillovers as required by the state‐level nature of the data, and consider that the functional distribution of income and the level of economic activity are jointly determined, we estimate that a higher state wage share raises output growth in the own state and the neighboring states. Yet the effect of a change in the state wage share on capacity utilization in the own state and the neighboring states is not statistically significant. Meanwhile, a higher state output growth raises the wage share in the own state, but its impact on the wage share in the neighboring states is not statistically significant. A higher state capacity utilization raises the wage share in the own state, yet it reduces the wage share in the neighboring states.

Nonlinear Spatial Dynamic Panel Data Models with Endogenous Dominant Units: An Application to Share Data

This article develops a nonlinear spatial dynamic panel data model with one particularly interesting application to a structural interaction model for share data. To account for effects from dominant (popular) units, the spatial weights matrix in our model can allow for unbounded column sums. To account for heterogeneity, our model includes two-way fixed effects and heteroscedastic errors. We further consider the potential endogeneity of the spatial weight matrix constructed from socioeconomic distance. We investigate the quasi-maximum likelihood estimator (QMLE), generalized methods of moments estimator (GMME), and root estimator (RTE), and establish their consistency and asymptotic normality based on the near epoch dependence (NED) framework. The RTE can derive a relatively computationally simple and closed-form solution without evaluating the QMLE’s Jacobian matrix as well as the iterations by GMME. We consider both n , T → ∞ , and the strength of the dominant units is equal to 1 when T → ∞ . For the purpose of empirical analysis, we derive the marginal effects and their limiting distributions based on the proposed estimators. In an empirical application, we apply our model to China’s prefecture city-level data, revealing significant spillover effects of the tertiary industry share. These findings suggest that the development of the tertiary sector in large cities can foster its growth in small cities.

Testing Spatial Dynamic Panel Data Models with Heterogeneous Spatial and Regression Coefficients

Spatio‐temporal data are often analysed by means of spatial dynamic panel data (SDPD) models. In the last decade, several versions of these models have been proposed, generally based on specific assumptions and estimator properties. We focus on an SDPD model with heterogeneous coefficients both in the spatial and exogeneous regression components. We propose a strategy to identify the specific structure of the SDPD model through a multiple testing procedure that allows to choose between a general version of the model and a nested version derived from the general one by imposing restrictions on the parameters. Our proposal can be used to test the homogeneity of the model parameters as well as the absence of specific components, such as spatial effects, dynamic effects or exogenous regressors. It is also possible to use the proposed testing procedure for the identification of relevant locations. The theoretical results highlight the consistency of the testing procedure in the high‐dimensional setup, when the number of spatial units goes to infinity and exceeds the number of time‐observations per spatial unit. Further, we also conduct a Monte Carlo simulation study, which gives empirical evidence of the good performance of the testing procedure in finite samples.

The Regional Heterogeneity of the Impact of Agricultural Market Integration on Regional Economic Development: An Analysis of Pre-COVID-19 Data in China

The abrupt onset of the COVID-19 pandemic in late 2019 significantly disrupted China’s domestic agricultural production and supply chain stability. Local governments, responding to urgent circumstances, implemented various trade restrictions that profoundly affected regional economic development. This study, covering data from 2010 to 2019 across 31 provinces, investigates agricultural market integration and regional economic development. Employing a dynamic spatial panel Durbin model, it systematically analyzes the complex relationship between these variables. International trade variables related to agricultural products are then introduced to examine their “substitution effect” in promoting regional economic development through agricultural market integration. The research findings are summarized as follows: (1) disregarding international agricultural trade, a one-unit increase in the agricultural market integration index corresponds to a 0.156% rise in regional economic development. (2) In an open economy, the substitution coefficients for agricultural imports, exports, and total trade concerning market integration are −0.00097, −0.0012, and −0.0038, respectively. (3) The strength of the substitution effect from the international agricultural market to the domestic market varies regionally, with coefficients of −0.00099 and −0.00217 for the eastern and western regions, respectively.

How does economic agglomeration affect carbon emissions at the county level in Liaoning China?

China's regional economic development has become a regional agglomeration model. The implementation of urbanization strategies promotes spatial concentration and catalyzes economic agglomeration. Due to the heterogeneity of economic development at various spatial scales, economic agglomeration has different environmental externalities. Peak carbon and carbon neutrality make it vital to explore these externalities in China's urbanization process, especially at smaller scales. This study uses a long time series of macro data for 42 counties in Liaoning Province and constructs a dynamic spatial panel Dubin model to assess the impact of economic agglomeration on carbon emissions, showing that agglomeration and emissions exhibit an “inverted N-shaped” curve at the county level. There is a threshold of emission increases and reductions for economic agglomeration. When economic agglomeration exceeds a threshold, it shows a significant emission reduction effect. After further decomposition of spatial effects, economic agglomeration affects carbon emissions through two channels: the direct and the indirect effect with urbanization as a mediator. There are also spillover effects of economic agglomeration on carbon intensity, meaning that economic agglomeration impacts carbon emissions in the region and surrounding areas. The results provide a reference for development guidelines and emission reduction policies at the county level, emphasizing the need to balance the economy and the environment in county regional development.

Construction Strategy of Regional Investment and Financial Management System under Spatial Effect Modeling

Abstract In recent years, China’s regional economic differences have become more and more apparent, and the problem of unbalanced allocation of financial resources has become more and more prominent. In this context, it is of great practical significance to study the construction strategy of regional investment and financial management systems using the spatial effect model. This study adopts the spatial econometric method to establish the spatial effect model. Considering the limitations of the spatial panel data model, this paper also uses the general dynamic spatial panel model with fixed effects to analyze the relationship between regional investment and the financial management system. Finally, through the Granger causality test on the spatial effect analysis of regional investment and financial management, this paper verifies the study of the interaction between the financial management system and regional investment. The research data show that in the spatial panel model, the regression coefficient of the explanatory variable F financial management system is 0.2431, which is significant at the 1% level, indicating that with a little increase in the degree of financial management system, the regional economy grows by 0.2431. The fixed effect general dynamic spatial panel model’s superiority is confirmed by the higher goodness of fit of the general dynamic spatial panel model compared to the spatial panel model.

Spatialtemporal evolution characteristics of ozone in China and its response to urbanization.

Based on the background of urbanization in China, we used the dynamic spatial panel Durbin model to study the driving mechanism of ozone pollution empirically. We also analyzed the spatial distribution of ozone driving factors using the GTWR. The results show that: i) The average annual increase of ozone concentration in ambient air in China from 2015 to 2019 was 1.68μg/m3, and 8.39μg/m3 elevated the year 2019 compared with 2015. ii) The Moran's I value of ozone in ambient air was 0.027 in 2015 and 0.209 in 2019, showing the spatial distribution characteristics of "east heavy and west light" and "south low and north high". iii) Per capita GDP industrial structure, population density, land expansion, and urbanization rate have significant spillover effects on ozone concentration, and the regional spillover effect is greater than the local effect. R&D intensity and education level have a significant negative impact on ozone concentration. iv) There is a decreasing trend in the inhibitory effect of educational attainment and R&D intensity on ozone concentration, and an increasing trend in the promotional effect of population urbanization rate, land expansion, and economic development on ozone concentration. Empirical results suggest a twofold policy meaning: i) to explore the causes behind the distribution of ozone from the new perspective of urbanization, and to further the atmospheric environmental protection system and ii) to eliminate the adverse impacts of ozone pollution on nature and harmonious social development.

INNOVATION, COOPETITION AND SPILLOVER EFFECTS IN EUROPEAN REGIONS

Innovation and investment are critical to economic growth. In this article, we address the complex task of evaluating the capacity of regional innovation to increase investment and generate spillovers in regions of the European Union (EU) from both spatial and temporal perspectives. Using panel data estimation methods and exploring the effects of dynamic spatial autocorrelation, our findings show a positive spatial autocorrelation at the level of EU regions. We also observed spatial competition, both in terms of the distribution of investments and in terms of the diffusion of short-term innovation gains. We argue that, in the short term, EU regions tend to behave as competitors for investment fixing, but in the long run, innovation has the potential to generate spillover effects on neighbouring regions. Furthermore, we find that investment patterns were characterized by a significant temporal autocorrelation, showing that shocks to investment in regions tend to be absorbed in a few periods. This paper attempts to fill existing gaps by using estimation methods for dynamic spatial panel data to identify and explore the effects of regional innovation on investment for the 154 European Union regions, and reports original findings as regards the knowledge spillover across European regions.

A Spatial Econometric Analysis of Productivity Variations Across US Cities

Using a dynamic spatial panel model applied to 377 US Metropolitan Statistical Areas (MSAs), estimated over the period 2011-2021, significant differences are found between large MSAs regarding the relationship between labour productivity and economic mass, as measured by GDP. The methodology adopted illustrates the state of the art for spatial econometric modeling as it is often needed in practice, allowing for multiple endogenous regressors and dynamic effects. The estimation method applies synthetic instruments designed to limit negative effects of instrument overabundance.

Analysis of the spatial urbanization effect of digital finance in China

PurposeThis study aims to empirically examine the impact of digital finance on spatial urbanization and elucidate its underlying mechanisms.Design/methodology/approachUsing panel data of Chinese prefecture-level cities from 2011 to 2021, and using a spatial dynamic panel model, the authors analyzed the effects of digital finance on spatial urbanization and the mechanism of its action.FindingsThe findings of the study reveal that digital finance, along with its sub-dimensions, namely coverage breadth, usage depth and digitization degree, all contribute to the enhancement of spatial urbanization. The information channel effect generated by the development of postal and telecommunication businesses, the goods delivery effect generated by the development of retail businesses and the wealth accumulation effect generated by the accumulation of household wealth are all important channels through which digital finance promotes spatial urbanization. Digital finance exerts a significant promotional effect on spatial urbanization in second-tier cities, third-tier cities and their subsequent tiers. This observation alludes to the regionally inclusive nature of spatial urbanization promotion facilitated by digital finance.Originality/valueThe present study endeavors to fill this void by employing empirical analysis to investigate the ramifications of digital finance on spatial urbanization, thereby shedding light on the pivotal role played by digital finance in expediting the progression of spatial urbanization. This study undertakes an examination of the spatial spillover effects, thus providing a comprehensive exposition of the influence of digital finance on spatial urbanization. This study introduces this crucial dimension, and the empirical findings elucidate that digital finance fosters the evolution of spatial urbanization by broadening the coverage of information channels, augmenting the efficiency of goods distribution and enhancing wealth accumulation efficacy.

Estimation and variable selection for high-dimensional spatial dynamic panel data models

Spatiotemporal modeling of networks is of great practical importance, with modern applications in epidemiology and social network analysis. Despite rapid methodological advances, how to effectively and efficiently estimate the parameters of spatial dynamic panel models remains a challenging problem. To tackle this problem, we construct consistent complex least-squares estimators by the eigendecomposition of a spatial weight matrix method originally proposed for undirected networks. We no longer require all eigenvalues and eigenvectors to be real, which is a remarkable achievement as it implies that the proposed method is now applicable to spatiotemporal data modeling of directed networks. Under mild, interpretable conditions, we show that the proposed parameter estimators are consistent and asymptotically normally distributed. We also present a complex orthogonal greedy algorithm for variable selection and rigorously investigate its convergence properties. Moreover, we incorporate fixed effects into the spatial dynamic panel models and provide a model transformation so that the proposed method can also be applied to the transformed model. Extensive simulation studies and data examples demonstrate the effectiveness of the proposed method.

Impact of technological innovation on agricultural green total factor productivity in China: considering the threshold effect of environmental regulation

Based on the panel data for 30 provinces in China from 2004 to 2020, this study explores the impact of technological innovation and environmental regulation on agricultural green total factor productivity by using the dynamic spatial Durbin panel model and the spatial panel threshold regression model. The results show that technological innovation significantly contributes to the improvement of AGTFP. Such positive impact is derived from the direct effect of technological innovation within the local province and the spatial spillover effect of technological innovation in neighboring provinces. Additionally, environmental regulation has a significant double-threshold effect on the technological innovation - AGTFP relationship. Within medium-level environmental regulation, the positive impact of technological innovation on AGTFP achieves a leap forward. Therefore, technological innovation serves as a motivator of agricultural green development, and appropriate intensity of environmental regulation is recommended in the endeavor of promoting green total factor productivity by technological innovation.

Spatial Dependence via the Internal Capital Markets of U.S. Global Banks

AbstractIn this paper, we estimate the cross‐border liquidity network created by U.S. global banks via their internal capital markets to capture the spatial dependence or bilateral proximity between branches located in different countries. Results from dynamic spatial panel regressions indicate that the foreign branches in nearby countries show common lending patterns. A 1% increase in lending in branches in proximate locations is associated with an estimated increase of 0.25% in local lending. Further interdependencies are evidenced by the positive liquidity and monetary policy spillovers on local lending, with marginal spatial effects estimated to be around 1.11% and 0.14%, respectively.