Dynamic Spatial Panel Model Research Articles

The Productivity–Real Wage Nexus in Emerging European Economies: Evidence from Poland

ABSTRACT We investigate the nature of the wage–productivity nexus in Poland, considering the validity of the alternative theories of this relationship in the context of a recent hypothesis pointing to the role of multinational corporations (MNCs) in decoupling productivity from wages. Using spatial and nonspatial dynamic panel models, we find indirect support for this hypothesis. Sectors with the highest presence of MNCs tend to have higher labor productivity but lower wages. Unlike productivity, wages are also subject to stronger spatial spillovers, suggesting that they are sometimes set based on their geographic proximity, rather than on workers’ productivity. The policy implications of the findings are discussed.

China's economic growth and haze pollution control

The Environmental Kuznets Curve, as a topic widely discussed in academia these days, needs to be further empirically tested. Taking the statistical data of 285 cities from 2003 to 2016 in China as the research sample, this paper uses a dynamic spatial panel model to analyze the impact of economic growth on haze pollution. The conclusions reveal that from an overall perspective, economic growth and haze pollution is always closely related, presenting an inverted U-shaped relationship; that is, the Environmental Kuznets Curve has been verified. Additionally, 26.84% of the sample cities in China have already exceeded the inflection point, meaning that economic growth has gradually led to the effective treatment of haze pollution. This is, however, restricted by stages of urban economic development. To be specific, economic growth has no significant impact on haze pollution when industrialization is in its infancy. Growth, however, exacerbates haze pollution in cities in the middle stages of industrialization although haze pollution does improve for cities in the later stages of industrialization. This means that Chinese cities should formulate reasonable and effective haze pollution control policies and measures according to their own economic development stage.

Impact of environmental decentralization on pollution reduction effects in China: A test based on spatial dynamic panel model

Impact of environmental decentralization on pollution reduction effects in China: A test based on spatial dynamic panel model

Assessing the impact of energy-saving R&D on China’s energy consumption: Evidence from dynamic spatial panel model

Increasing energy demand has become a cause of significant concern in China. In previous literature, research and development (R&D) activities have been identified as an important factor in determining energy demand. This study narrowed the R&D down to its energy technological level (i.e., energy-saving R&D). In addition, energy-saving R&D can be differentiated according to the players and their purposes, and the existing literature has basically neglected the temporal and spatial characteristics of energy demand. As a result, this empirical study used the spatial dynamic panel model to analyse the role of energy-saving R&D activities in China’s energy demand by classifying them according to those who undertake them and the purposes of the research. The empirical evidence based on China’s provincial dataset over the period 2000–2016 implies that energy-saving R&D has not played a positive role in influencing energy demand. In addition, both the direct and indirect effects are insignificant. However, the energy-saving R&D conducted by enterprises for the purpose of pursuing utility exerts a positive effect on reducing energy demand through both direct and indirect channels. Based on the empirical evidence, some insightful policy implications for China to effectively control energy demand are presented.

Diagnostic tests for homoskedasticity in spatial cross-sectional or panel models

We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS–OPMD method leads to finite sample improved tests over the usual methods. More importantly in non-standard problems where a genuine (quasi) score is not available and the usual methods fail, the proposed AQS–OPMD method provides feasible solutions. The AQS tests are formally derived and asymptotic properties examined for three representative models: spatial cross-sectional, static and dynamic panel models. Monte Carlo results show that the proposed AQS tests have good finite sample properties.

Spillover and dynamic effects of energy transition and economic growth on carbon dioxide emissions for the European Union: A dynamic spatial panel model

AbstractThis paper introduces “spatial effects” and “dynamic effects” to investigate the influences of economic growth and energy transition on cross‐country CO2 emissions movements within the European Union (EU). We apply the fixed‐effects dynamic spatial Durbin error model to empirically gauge the magnitude of the spatial impacts and dynamic impacts for a sample of 26 EU countries throughout 1990–2015. By analyzing the empirical results, we conclude that: (1) Compared with dynamic spatial Durbin error model, the traditional dynamic panel model over‐estimates the parameters because traditional regression methods only capture the direct impacts, and neglect the indirect impacts. (2) A significant positive spatial spillover of CO2 emissions from neighboring countries to the local country is recognized, justifying the use of our spatial model. (3) Economic growth has positive impacts on CO2 emissions, while the spatial effects of economic growth exert negative impacts. Moreover, the total effects of economic growth are positive in both short‐term and long‐term. (4) Although the spatial effects of renewable energy are not significant, renewable energy has negative influences on CO2 emissions. (5) The impacts and spatial effects of natural gas are positive; therefore, its total effects are positive in both short‐run and long‐run. Based on our finding, we provide several policy recommendations, such as the emphasize of cooperation with CO2 reduction policies, the promotion of green economy and renewable energy, and the substitution of natural gas in the future.

Ecological effects of new-type urbanization in China

The development of urbanization in China has changed from a traditional form of urbanization that focuses on the rate of growth to a new type of urbanization that stresses improvements in quality. To evaluate this new type of urbanization, this paper constructs a system that looks at the four dimensions of economy, population, society, and environment, and then, using provincial-level panel data, employs a dynamic spatial panel model to empirically test the ecological effects of the new type urbanization. The study finds that the new-type urbanization in China increased gradually from 2003 to 2017, focused on improvements to the ecological environment, and displayed obvious inter-provincial differences. Moreover, China's new-type urbanization has not only effectively reduced pollution emissions and improved energy efficiency but has also been significant in terms of its ecological effect. Moreover, economic urbanization, population urbanization, social urbanization and environmental urbanization exhibit the obvious ecological effects of “pollution reduction and efficiency improvement.” In the process of this new type of urbanization, both the government's “severe constraints” on pollution emissions and the active introduction of foreign capital are further important avenues that lead to achieving “pollution reduction and efficiency improvements.”

Prediction of PM2.5 daily concentrations for grid points throughout a vast area using remote sensing data and an improved dynamic spatial panel model

Abstract The incorporation of spatial and temporal correlations can significantly improve the accuracy of PM2.5 concentration prediction models. However, the dynamic spatial panel model which explicitly deals with these two correlations remains absent from current approaches to out-of-sample concentration prediction. An issue is that the prediction of daily concentrations for grid points across a vast area may well overwhelm existing algorithms, as it requires an enormous amount of computational resources and an enlarged spatial weight matrix. This paper develops improved algorithms that address this issue. The dynamic spatial panel approaches used in this paper predict daily series PM2.5 concentrations for grid points covering Mainland China, using daily aerosol, vegetational and meteorological remote sensing data as the explanatory variables. The predicted concentration maps offer more realistic detail in areas where monitoring stations are sparse. Indeed, the error map for the out-of-sample prediction shows that MAPE is less than 30% in most regions, and the average MAPE is 24.28%, which is relatively low compared with similar studies. In contrast to methods which cannot provide coefficients of variables, the developed method offers coefficients to assess the contributions of explanatory variables and temporal-spatial correlation terms, allows quantification of convergence effects, and can distinguish between spillover effects and local effects. A performance comparison of models with various spatial weight matrices shows that model achieves the optimal fitting levels by using the neighbouring unit number threshold of 18 or the distance threshold of 150 km. The case analysis in this paper finds that spillover effects are about three times larger than local effects, and the spatial correlation is greater than the cumulative effects of earlier concentrations. This finding adds further weight to the notion that management of PM2.5 pollution and associated impacts requires multi-regional and even multi-national coordination and effort.

Incentivized for Leveling the Playing Field: Do State Economic Incentives Compensate for High Taxes?

State tax and nontax incentives have been widespread in the United States, though their efficacy in job creation and economic development has been repeatedly questioned in the literature by scholars and policy makers. Why, then, do states persistently pursue these incentive policies? Using the newly developed Panel Database on Incentives and Taxes, we adopt a dynamic spatial Durbin panel model to account for both temporal and spatial dependence and to shed light on this question. Empirical evidence suggests a statistically significant and positive relationship between tax credits and tax burdens (i.e., elevated tax breaks are used to offset higher tax differentials). States therefore may seek to create a level playing field in business attraction and retention by overcoming tax disadvantages. Besides, high-serial dependence is present in the use of various tax credits, suggesting a high self-perpetuating tendency that tax breaks, once introduced, are likely to be persistent over time. States are also found to be engaged both in spatial competition or imitation among geographically proximate states, and in strategic benchmarking among states that are geographically distant but economically alike.

The impact of foreign direct investment on urban PM2.5 pollution in China

The hypothesis of “Pollution Heaven” or “Pollution Halo” has, for some time, been one of the central issues in environmental economics. There is a controversy in the conclusions with regards to China, it thus needs further empirical testing. Based on 2003 to 2016 statistical data on 285 Chinese cities, this paper uses a dynamic spatial panel model to empirically analyze the impact of foreign direct investment (FDI) on PM2.5 pollution. The results demonstrate that urban PM2.5 pollution shows both significant global spatial autocorrelation and local spatial agglomeration effects. Overall, FDI significantly aggravates China's urban PM2.5 pollution, thus confirming the “Pollution Heaven” hypothesis. This effect, however, depends on the stage of urban economic development. Although FDI has no significant effect on urban PM2.5 pollution in the initial stage of industrialization, it does aggravate urban PM2.5 pollution in the midterm stage. In the later period of industrialization, FDI actually improves urban PM2.5 pollution, but the effect of this improvement is relatively weak. Furthermore, PM2.5 pollution shows significant spatial spillover and dynamic effects. It follows that both joint prevention and control and continuous efforts must be made to control PM2.5 pollution.

Impacts of industrial structure and technical progress on carbon emission intensity: Evidence from 281 cities in China

In the context of global climate change and rapid urbanization, the low-carbon economy has become the fundamental means of achieving sustainable development. To find an effective solution to reduce carbon emissions, it is important to identify the dominant factors contributing to carbon emission intensity (CEI). Based on refined indicators and a dynamic spatial panel model, we build a comprehensive framework to quantify the impact of the industrial structure and technical progress on the CEI and conduct empirical research on 281 prefecture-level cities in China during 2006–2016. The results show that both spatial autocorrelation and heterogeneity of CEI values are significant and positive among cities. Technical change and efficiency improvements are the dominant factors behind CEI change. Technical progress plays a significant role in reducing the CEI, whereas the carbon emissions rebound effect decreases these positive impacts. Further, the combined effect of industrial structure optimization and technical progress on reducing carbon intensity is not significant as we have expected. Based on our findings, we suggest specific, targeted policies to reduce CEI, including promoting regional green technology, focusing on combining green technologies with green cities, formulating different urban development strategies and strengthening cooperation among cities.

The effect of environmental regulation on capacity utilization in China's manufacturing industry.

How environmental regulations are used to improve manufacturing capacity is crucial to the future development of China's manufacturing industry from a quality point of view. This paper adopts a dynamic spatial panel model to empirically analyze both the effects of different types of environmental regulation policies on capacity utilization in China's manufacturing industry and their mechanisms. The results show that China's manufacturing capacity utilization varies a great deal, with improvements in technical efficiency playing the main role, although with significant regional differences. Both command-and-control and market-incentive environmental regulation are conducive to improvements in manufacturing capacity utilization; market-incentive environmental regulation, however, plays the stronger role. The above mechanisms have certain differences though. Both types of environmental regulations are able to improve manufacturing capacity utilization by improving technical efficiency and equipment utilization but the command-and-control policy tool exerts a greater effect in improving technical efficiency while the market-incentive mechanism has a greater effect in improving equipment utilization.

First difference estimation of spatial dynamic panel data models with fixed effects

This paper investigates the first difference (FD) estimation of spatial dynamic panel data (SDPD) models with fixed effects using quasi-maximum likelihood (QML) approach, where both n and T are large. We show that the QML estimation for the SDPD with FD can be reduced to the direct estimation of individual effects, except for the estimation of variance parameter. After bias correction, these two approaches would yield asymptotically equivalent estimates for all parameters including the variance parameter. Our results extend the equivalence of LSDV estimate and GLS estimate of FD equation in the panel regression model to the spatial dynamic panel model, which includes the conventional dynamic panel as a special case. Our analysis highlights the importance of initial values rather than the many fixed effects in spatial panel models.

Corrigendum to ‘Impacts of the natural gas infrastructure and consumption on fine particulate matter concentration in China’s prefectural cities: A new perspective from spatial dynamic panel models’ [J. Clean. Prod. 239 (2019)

Corrigendum to ‘Impacts of the natural gas infrastructure and consumption on fine particulate matter concentration in China’s prefectural cities: A new perspective from spatial dynamic panel models’ [J. Clean. Prod. 239 (2019)

Industrial structure, technological innovation, and total-factor energy efficiency in China.

The promotion of industrial restructuring and technological innovation is the most important and realistic way of improving energy efficiency. This thesis uses the modified Super-SBM method to measure China's total-factor energy efficiency and then uses the dynamic spatial panel model (DSPM) to verify the effect of industrial structure and technological innovation on total-factor energy efficiency. The study found that from 2003 to 2016, China's total-factor energy efficiency showed a fluctuating trend of "falling first and then rising." The inflection point appeared in 2012; total-factor energy efficiency in the Eastern region was significantly higher than the national average, while in the Central and Western regions, it was significantly lower. The results of the analysis show that both the service adjustment of the inter-industry structure and the productivity growth of the intra-industry structure significantly promote improvements in total-factor energy efficiency. However, due to the low conversion rate of scientific and technological achievements in China, the impact of technological innovation input on total-factor energy efficiency is not significant. This is in contradistinction to technological innovation output which does significantly improve total-factor energy efficiency. The above research conclusion is still robust and reliable after changing the measurement method and spatial weight matrix.

Dynamic Spatial Panel Models: Networks, Common Shocks, and Sequential Exogeneity

This paper considers a class of generalized methods of moments (GMM) estimators for general dynamic panel models, allowing for weakly exogenous covariates and cross‐sectional dependence due to spatial lags, unspecified common shocks, and time‐varying interactive effects. We significantly expand the scope of the existing literature by allowing for endogenous time‐varying spatial weight matrices without imposing explicit structural assumptions on how the weights are formed. An important area of application is in social interaction and network models where our specification can accommodate data dependent network formation. We consider an exemplary social interaction model and show how identification of the interaction parameters is achieved through a combination of linear and quadratic moment conditions. For the general setup we develop an orthogonal forward differencing transformation to aid in the estimation of factor components while maintaining orthogonality of moment conditions. This is an important ingredient to a tractable asymptotic distribution of our estimators. In general, the asymptotic distribution of our estimators is found to be mixed normal due to random norming. However, the asymptotic distribution of our test statistics is still chi‐square.

Diagnostic Tests for Homoskedasticity in Spatial Cross-Sectional or Panel Models

We propose an Adjusted Quasi-Score (AQS) method for constructing tests for homoskedasticity in spatial econometric models. We first obtain an AQS function by adjusting the score-type function from the given model to achieve unbiasedness, and then develop an Outer-Product-of-Martingale-Difference (OPMD) estimate of its variance. In standard problems where a genuine (quasi) score vector is available, the AQS-OPMD method leads to finite sample improved tests over the usual methods. More importantly in non-standard problems where a genuine (quasi) score is not available and the usual methods fail, the proposed AQS-OPMD method provides feasible solutions. The AQS tests are formally derived and asymptotic properties examined for three representative models: spatial cross-sectional, static or dynamic panel models. Monte Carlo results show that the proposed AQS tests have good finite sample properties.

Market Segmentation and Regional Productivity: Theoretical Mechanism and Empirical Evidence

Based on the provincial panel data of China covering the period 1998-2016, this paper analyzes the impact and transmission mechanism of the commodity market segmentation and factor market segmentation on regional productivity by using the dynamic spatial panel model and mediating effect model. The results show that: Labor market segmentation is the strongest, followed by commodity market, and capital market segmentation is the weakest. The commodity market segmentation is inverted U-shaped relationship with regional productivity, namely the low intensity of commodity market segmentation is conducive to regional productivity, but in the long run, more than a certain intensity of it will have an obvious effect. Currently, the commodity market segmentation of most provinces in China is within the threshold value of improving regional productivity. However, factor market segmentation hinders regional productivity growth. Further influence mechanism test finds that different types of market segmentation have different transmission paths for regional productivity: In commodity market segmentation, market competition, demand scale and technology spillover are all important ways to affect regional productivity, and demand scale plays a leading role; factor market segmentation will affect regional productivity through resource allocation and enterprise decision-making behavior, among which the resource allocation effect plays a major role.

Estimating the local employment impacts of immigration: A dynamic spatial panel model

This paper highlights a number of important gaps in the UK evidence base on the employment impacts of immigration, namely: (1) the lack of research on the local impacts of immigration – existing studies only estimate the impact for the country as a whole; (2) the absence of long-term estimates – research has focused on relatively short time spans – there are no estimates of the impact over several decades, for example; (3) the tendency to ignore spatial dependence of employment which can bias the results and distort inference – there are no robust spatial econometric estimates we are aware of. We aim to address these shortcomings by creating a unique data set of linked Census geographies spanning five Censuses since 1971. These yield a large enough sample to estimate the local impacts of immigration using a novel spatial panel model which controls for endogenous selection effects arising from migrants being attracted to high-employment areas. We illustrate our approach with an application to London and find that no migrant group has a statistically significant long-term negative effect on employment. EU migrants, however, are found to have a significant positive impact, which may have important implications for the Brexit debate. Our approach opens up a new avenue of inquiry into subnational variations in the impacts of immigration on employment.

Food Safety Incident, Public Health Concern, and Risk Spillover Heterogeneity: Avian Influenza Shocks as Natural Experiments in China's Consumer Markets.

Background: Food safety incidents have aroused widespread public health concern, causing food price risk. However, the causal paths remain largely unexplored in previous literature. This paper sets out to identify the relations of local and spatial spillovers of food safety incidents and public health concerns to food price risk in consumer markets within a setting with heterogeneous food safety risk levels. Methods: (i) Theoretically, unlike prior work, this paper decomposes food safety risks into food safety incidents (objective incident component) and public health concern (subjective concern component). This article develops a theoretical framework of causality to capture the underlying causal pathways motivated by the theories of limited attention and two-step flow of communication. (ii) Empirically, using avian influenza shocks in China’s poultry markets as natural experiments, this paper differentiates between low- and high-risk food and incidents. The article adopts dynamic spatial panel models to analyze potential nonlinearity, moderation, and mediation in the spillover of food safety risk to food price risk for a long panel of 30 provinces covering the November 2007 to November 2017 period. Results: (i) Food safety incident alone only triggers high-risk food price risk, not low-risk food price risk. (ii) Public health concern amplifies nonlinear food price risk triggered by food safety incident. (iii) High-risk incident intensifies negative pressure of public health concern on food price risk. (iv) Food safety incident indirectly affects high-risk food price risk through public health concern. Conclusions: Using a setting with heterogeneous risk levels, this paper documents that (i) food safety incident itself does not necessarily determine food price risk, whereas it is actually public health concern that directly causes nonlinear food price risk; (ii) public health concern spillover to food price risk is negatively moderated by high-risk incident, and (iii) food safety incident spillover to high-risk food price risk is mediated by public health concern. The findings complement current research by (i) elucidating the diverse impacts of food safety incident and public health concern on food price risk, which are obscure in previous literature, and (ii) highlighting that heterogeneous food and incident risk levels matter for determining food price risk spillover.