Three-dimensional Panel Data Research Articles

Heterogeneous Three-Dimensional Panel Data Models

Heterogeneous Three-Dimensional Panel Data Models

The transfer of energy-intensive projects under carbon constraints: Does energy structure matter?

Transferring energy-intensive industries to regions with abundant clean energy is crucial for achieving low-carbon transformation of energy-intensive sectors during carbon neutrality. By manually collecting the project data from energy-intensive enterprises in China, we construct a three-dimensional panel data with 1.86 million observations to empirically examines the impact of the carbon intensity constraint policy (CICP) on the transfer of high energy-intensive projects (HEP). Our findings indicate that the CICP accelerates the transfer of HEP. The transfer direction shows that the HEP transferred from the eastern region to the western region have increased, but the HEP transferred from the western region to the eastern region have not decreased. To explain the above phenomena, we further discuss the underlying mechanism from the perspective of energy structure and industrial agglomeration. From the standpoint of transfer-out regions, the higher the proportion of thermal power in the energy consumption structure, the more HEP will be transferred out. From the standpoint of transfer-in regions, the industrial agglomeration will attract the transfer of the HEP. However, we do not find evidence that wind power and photovoltaics can attract the HEP. This paper provides project-level empirical evidence for promoting the low-carbon transformation of energy-intensive industries.

The incompatibility between industrial restructuring and low-carbon economy: a comprehensive analysis.

Numerous studies have demonstrated that the development of low-carbon economy and industrial restructuring cannot occur in a coordinated manner. However, academic literature does not provide further explanations for this phenomenon. In this paper, we introduce a novel decomposition method to reassess the relationship between industrial restructuring and low-carbon economy, which yields similar findings. Next, we construct a straightforward theoretical model to investigate two fundamental reasons that interrelate with this issue: excessively high proportion of secondary sector and excessive carbon intensity of tertiary sector. Finally, we implement a rigorous causal identification using three-dimensional panel data at the provincial, industrial, and yearly levels by undergoing multiple robustness tests and mitigating endogeneity issues. Our heterogeneity tests suggest that the impact of industrial restructuring is greater in high-polluting industries, the Eastern region, and non-digital pilot regions. Overall, our theoretical and empirical analysis serves as a vital reference for other developing and developed countries to attain harmonious development between low-carbon economy and industrial restructuring.

Information and communication technology and innovation performance of firms: Evidence from Chinese listed state-owned enterprises

This study examines whether information and communication technology (ICT) improves innovation performance among Chinese state-owned enterprises (SOEs). Generalized difference-in-differences, panel data regression models, and firm-level data from 2011 to 2018 were used to generate statistically significant results, indicating that publicly traded Chinese SOEs increase innovation performance through ICT adoption. Additionally, we analyze the robustness using a three-dimensional panel data model and employ two-stage least squares estimation to remedy the possible bias generated by endogeneity problems. The results of these tests were consistent with the main findings reported above. Additionally, the existing literature suggests that ICT affects innovation by increasing the openness of information sharing and cooperation, improving efficiency, and enhancing human resource management and corporate governance. We provide evidence of the underlying mechanisms between ICT and innovation among Chinese SOEs.

Digitalization, positioning in global value chain and carbon emissions embodied in exports: Evidence from global manufacturing production-based emissions

Digitalization and the emergence of global value chain (GVC) have been the two most significant changes affecting international trade in recent decades. This study examines the impact of digitalization and the position of GVC on carbon emissions embodied in exports (CEEE). In doing so, we use high-dimensional fixed-effect models and three-dimensional panel data covering 18 manufacturing industries in 38 economies from 2000 to 2014. The results show that digitalization and CEEE have an inverted U-shaped relationship, and upgrading GVC position can significantly reduce CEEE. Compared with developing countries, developed countries are prone to enter the downward phase of the inverted U-shaped curve through digitalization, thereby curbing CEEE. Similarly, high-tech industries enter the decline stage of the inverted U-shaped curve earlier than low-tech industries in digitalization process. In addition, improvements in GVC position in developed countries (or high-tech industries) have a greater impact on CEEE reduction than in developing countries (or low-tech industries). Finally, the results of nonlinear moderating effect model indicate that upgrading GVC's position flattens (or positively moderates) the inverted U-shaped curve and shifts the curve's turning point to the left. An important implication of our findings is that a country should actively raise its GVC position while focusing on digitalization for the goal of sustainable development.

Impact of digital transformation on pollution emissions of manufacturing enterprises in China: A micro-level analysis based on three-dimensional panel data

Impact of digital transformation on pollution emissions of manufacturing enterprises in China: A micro-level analysis based on three-dimensional panel data

Has servitization reduced the embodied carbon emissions of manufacturing export trade? Evidence from 38 countries

Manufacturing servitization, which introduces elements of the service industry to enhance the manufacturing structure, is increasingly being proposed as a carbon-reduction solution. However, the impact of rising service inputs on the embodied carbon emissions (ECE) of exports from the manufacturing sector has not been sufficiently investigated. This study departs from existing work by empirically examining the effect of manufacturing servitization on ECE in exports using a multi-way fixed-effect model and three-dimensional panel data from 18 manufacturing industries in 38 countries from 2000 to 2014. The results show that input servitization significantly reduces the carbon emissions embodied in manufacturing export trade. Furthermore, manufacturing servitization has a greater effect on reducing ECE in exports in developed countries (pollution-intensive industries) than in developing countries (non-pollution-intensive industries). While using the mediating effect model, the mechanism analysis shows that input servitization reduces ECE in manufacturing exports primarily through productivity improvement, energy substitution, and global value chain effects. Finally, we find that input servitization and the ECE from manufacturing exports exhibits nonlinear characteristics with changes in technological and economic development levels. The study’s findings highlight the importance of promoting deeper integration of service elements and manufacturing production processes in achieving the goal of global sustainable development. • Explore the impact of Servitization on CO 2 emissions embodied in export worldwide. • Calculating the degree of servitization using the complete consumption coefficient. • Using the Multi-way fixed-effect model and three-dimensional panel data. • Productivity improvement, energy substitution, and GVC effect are main mechanisms. • The impact of input servitization on ECE in exports has nonlinear characteristic.

Impact assessment of clean air action on total factor energy productivity: A three-dimensional analysis

Energy is the engine of China's rapid economic growth. Whereas, under the requirements of economic transition, energy productivity is in urgent need of improvement. To curb air pollutant emission, the Chinese government promulgated the Clean Air Action Plan in 2013, and the policy puts forward specific measures on energy utilization. How does energy productivity vary before and after policy implementation? Can environmental regulations promote energy productivity? Based on three-dimensional panel data—time (2008–2017), industry (19 2-digit industrial sectors), and region (30 provinces), this paper uses stochastic frontier analysis to measure total factor energy productivity of the industrial sector. The results show an upward trend in energy productivity, and the total factor productivity of energy-intensive sectors has grown faster. Regarding the environmental regulation as a quasi-natural experiment, the difference-in-difference-in-difference estimations validate and quantify the promotion effect on energy productivity. Furthermore, the mechanism analysis specifies the transfer channels as energy structure optimization and green technology innovation. China is currently at a critical period of transition to green and clean development. Our findings will provide theoretical support for the mechanism design of environmental and energy policies.

Determination of different types of fixed effects in three-dimensional panels*

In this paper, we propose a jackknife method to determine the type of fixed effects in three-dimensional panel data models. We show that with probability approaching 1, the method can select the correct type of fixed effects in the presence of only weak serial or cross-sectional dependence among the error terms. In the presence of strong serial correlation, we propose a modified jackknife method and justify its selection consistency. Monte Carlo simulations demonstrate the excellent finite sample performance of our method. Applications to two datasets in macroeconomics and international trade reveal the usefulness of our method.

Testing the Uncertainty of Outcome Hypothesis in Football Leagues: An Application of Italian Football League

We applied a three-dimensional panel data model of individual match attendance, while controlling for all nuisance variables affecting the home team, visiting team, and season, to the panel dataset of the Italian football league from 2000/2001 to 2016/2017. We attempted to construct a European-specific attendance regression model that takes into account the ‘open’ league structure, as well as inter-league competitions. In particular, we consider three types of outcome uncertainty: game uncertainty, relegation uncertainty, and Champions League qualification uncertainty. Our empirical results do not support the uncertainty of outcome hypothesis respect to game uncertainty and Champions League qualification uncertainty. However, the uncertainty of outcome hypothesis cannot be rejected with respect to relegation uncertainty.

Decomposition of Bilateral Trade Flows Using a Three-Dimensional Panel Data Model

This study decomposes the bilateral trade flows using a three-dimensional panel data model. Under the scenario that all three dimensions diverge to infinity, we pro- pose an estimation approach to identify the number of global shocks and country- specific shocks sequentially, and establish the asymptotic theories accordingly. From the practical point of view, being able to separate the pervasive and nonpervasive shocks in a multi-dimensional panel data is crucial for a range of applications, such as, international financial linkages, migration flows, etc. In the numerical studies, we first conduct intensive simulations to examine the theoretical findings, and then use the proposed approach to investigate the international trade flows from two major trading groups (APEC and EU) over 1982-2019, and quantify the network of bilateral trade.

A grey spatiotemporal incidence model with application to factors causing air pollution

The factors causing air pollution in China has caused extensive concern, but there are still many problems in the grey incidence model of identifying air pollution factors. The results produced by the existing grey incidence models are not stable when the order of objects in a given panel data is changed. In order to improve the reliability and uniformity of the grey incidence model, a new grey incidence model, called the grey spatiotemporal incidence model, abbreviated as the GSTI model, is designed in this paper. In the proposed model, the spatiotemporal data which can represent the spatial relationship among different objects rather than the three-dimensional panel data are defined. In addition, the new model includes two procedures. Firstly, the trend coefficient is used to measure the positive and negative connections between two data sequences. Secondly, the measurement coefficient is utilized to calculate the size of grey incidence degree. Subsequently, five properties of the GSTI model are discussed. To demonstrate its practicability and compatibility, the novel model is utilized to identify south Jiangsu province's main factors causing air pollution according to monthly data for 2018. The abundant comparison shows the applicability and superiority of the model in the identification of air pollution factors and the construction of grey incidence model.

Gravity models of interprovincial migration flows in Canada with hierarchical multifactor structure

Following recent contributions on migration flows, we contribute to the literature by relaxing restrictions on how multilateral resistance to migration (MRM) may affect province-pair-specific migration flows. We follow recent advancements in the three-dimensional (3D) panel data models with a hierarchical multifactor structure and develop the more flexible specification for MRM. In addition to including unobserved global (country) factors with province-pair-specific coefficients, we can control for local origin (destination)-specific factors that have heterogeneous effects on destinations (origins). We apply the 3DCCE estimator advanced by Kapetanios et al. (J Econom, 2020) to an analysis of the determinants of interprovincial migration flows in Canada from 1976 to 2014. In particular, we find that the recent rise in the internal migration flows, registered in Canada from 2009 onwards, is more likely to be associated with the relative income inequality and network presence rather than the conventional long-run determinants such as income and unemployment differentials.

Research on the logistics development level and spatial differentiation in node city with gray target decision model

PurposeThe purpose of this study is to master the development process and the construction effectiveness of backbone circulation network in an all-round way, formulate regional logistics development planning as well as promote the development of logistics industry by scientifically evaluating the logistics development of node cities with a view to analyzing their spatial differentiation features.Design/methodology/approachIn this paper, an integrated evaluation model is constructed by adopting factor analysis, gray target decision-making model based on cone volume and other methods so as to evaluate the logistics development of node cities. The dimensionality of three-dimensional panel data is reduced by factor analysis at first. Then, the gray target decision-making method based on cone volume is adopted to evaluate the development of node cities, whose evaluation results are carried out through the clustering analysis. The clustering analysis is used to determine the development level of node cities and to extract the spatial differentiation features of node cities.FindingsThe results show that the proposed model can comprehensively evaluate the logistics level of node cities and clarify the overall logistics development and spatial differentiation of node cities, which could provide objective evidence for formulating national policies as well as promoting the balanced and coordinated development of regional logistics in China.Originality/valueThe paper succeeds in overcoming the disadvantages of existing methods assessing the logistics development level, such as principal component analysis and factor analysis, which are not applicable to panel data.

Analysis of tourism demand using a multi-dimensional panel gravity model

PurposeThis study aims to estimate the determinants of international tourist flows between destinations by using the panel gravity model.Design/methodology/approachThe multi-dimensional panel gravity model was used to analyse tourism originating from 30 different countries to the 14 most-visited countries in the world between 2008 and 2016. Income (i.e. per capita gross domestic product for both the origin and destination countries), distance between countries, various economic indicators and six dummy variables were added to the gravity model as control variables.FindingsThe results indicated that tourist arrivals depended mainly on economic factors, i.e. income and trade variables were significant determinants of tourist arrivals. The results also suggested that estimated international tourist flows are a negative function of distance, as is postulated in economic theory.Originality/valueIn recent years, gravity models have been used frequently to analyse international tourism demand and have demonstrated their ability to evaluate the effects of various determinants of international tourism for many countries. The literature includes studies that used a two-dimensional panel gravity model to analyse the determinants of tourism demand to a single country from many different countries. This study differs in terms of specificity; in that, it relied on a three-dimensional panel gravity model that allowed for modelling of multiple destination countries. As a result, more comprehensive and general results relative to the determinants of tourism demand were obtained. In addition, the application of a non-nested three-dimensional panel data model, which has limited use, contributes a new perspective to the econometric literature.

A Gravity Model to Explain Flows of Wild Edible Mushroom Picking. A Panel Data Analysis

Picking wild edible mushrooms is becomingly an increasingly widespread activity. Recent research is reporting a change in the way pickers access this resource, particularly in the more developed countries. The latest studies focus on exploring the demand functions of harvesting, with the emphasis shifting away from analyses that address the issue from a commercial standpoint. Yet these studies fail to deal with the topic from a global perspective and provide only partial information that is felt to be insufficient when attempting to manage the resource efficiently. The present work seeks to provide an approach to the problem by applying, for the first time, a gravity model to study the system governing the sale of harvesting permits (www.micocyl.es, Castilla y León-Spain). The main advantage of this application is that, for the first time, three-dimensional panel data are used to link economic variables to climate variables and their interaction to the supply and demand of picking permits. Results show that the method provides key management information. Managing the picking of wild edible mushrooms should aim to focus more on handling the tourist flows it generates.

The driving force of carbon emissions in china: 1995-2015 hierarchically provincial evidence

This paper computes the provincial carbon emissions, use a “region-province-time” three-dimensional panel data set from 1995 to 2015, and conduct an empirical study to explore the driving force of carbon emissions in China. A hierarchically spatial autoregressive error (HSEAR) model is established, which takes into account the hierarchical structure and spatial error effect. The empirical results suggest the carbon emission and per capita GDP forming an “N” shape Environment Kuznets Curve (EKC). Meanwhile, by the growth of coal population, consumption proportion and the number of private cars will significantly increase the carbon emissions. The significantly positive spatial correlation of the error terms implies that the error impact of carbon emissions in the area has a significant positive spatial correlation with the adjacent area, which corrects the error of the general spatial error model to the socio-economic reality.

Study on Spatial Econometrics of Level Spatial Data of Knowledge Spillover Effect in Industrial Industry in Guangdong Province

This paper uses an econometric model of hierarchical data space to do empirical research on the knowledge spillover effect of industrial industries in Guangdong Province. Specifically, three-dimensional panel data (regions, industries, and time) of 21 prefecture-level cities and 31 industrial industries in Guangdong Province from 2005 to 2016 were used and spatial correlation, time factors, and hierarchical nesting effects were taken into consideration to establish hierarchical data. The spatial econometric model uses an industrial structure to empirically analyze the impact of the knowledge spillover effect of the industrial industry in Guangdong on its economic development.

Testing for serial correlation in three-dimensional panel data models

This paper studies serial correlation testing for a general three-dimensional panel data model. As a step for hypothesis testing, the robust within estimation of parameter coefficients is investigated, and shown to asymptotically consistent and normal under some mild conditions. A residual-based statistic is then constructed to test for serial correlation in the idiosyncratic errors, which is based on the parameter estimates for an artificial autoregression modeled by centering and differencing residuals. The test can be shown to asymptotically chisquare distributed under the null hypothesis. Power study shows that the test can detect local alternatives distinct at the parametric rate from the null hypothesis. The test needs no distribution assumptions of the error components, and is robust to the misspecification of various specific effects. Monte Carlo simulations are carried out for illustration.

How do Overnight Stays React to Exchange Rate Changes?

SummaryThis paper analyses the effect of a change in the real exchange rate on the number of overnight stays in Swiss hotels. It uses unique three-dimensional panel data on the monthly number of overnight stays by the visitor’s country of origin in 141 Swiss communities during the ten-year period from January 2005 to December 2014. We find low exchange rate elasticities of 0.2 for cities, but much higher elasticities of 1.4 for touristic communities. On the source market side, we find large exchange rate elasticities for German, Dutch, and Belgian visitors, while travellers from France and Italy are less price sensitive.