Significant Spillover Effects Research Articles

EXPRESS: Digital Economy and Regional Tourism Development: Mediating Role of Information Search from Spatial Spillover Perspective

Although studies have confirmed the positive nexus between the growth of the digital economy and regional tourism development, little light has been shed on the mechanism in between. This research makes the first attempt to identify the mechanism through which the digital economy affects regional tourism development with consideration of spatial spillover effects using China’s prefecture-level city multisource panel data from 2011 to 2019. The findings reveal that information search is a mediating channel through which the digital economy affects regional tourism development, with significant spillover effects. The spatial spillover effects of the digital economy on regional tourism development become stronger as the distance between cities increases. However, the information search in neighbouring regions may restrict tourism development in the focal region. This study deepens the understanding of the relationship between the digital economy and regional tourism development, providing insights for policymakers on regional cooperation in the context of digitalisation.

The first is free: do employee stocks incentivize stock market participation?

Relatively few people participate in the stock market. We investigate whether getting employee stocks as part of one’s compensation makes people more likely to actively invest in stocks. We find the probability of actively participating in the stock market increases by 15% for those who have been given employee stocks. This lends strong support to cognitive costs in stock market entry. We further find that people predominantly increase direct stock holdings as opposed to indirect holdings via mutual funds. Despite the significant spillover effects from holding employee stocks and an overall larger fraction of individual wealth being invested into stocks, it does not result in well diversified investing.

Unleashing the power of industrial robotics on firm productivity: Evidence from China

This study investigates the effect of industrial robots on firm productivity in China. Employing propensity score matching (PSM) and staggered difference-in-difference (SDID) estimations, we find a statistically significant and positive casual effect of robot adoption on firm productivity at the 1 % significance level, leading to an approximate 10 % increase in total factor productivity (TFP) among manufacturing firms in China. The effect was most pronounced in the first two years following adoption but lost statistical significance from the third year onward. Additionally, we find that robot exposure, measured by both adoption rate and adoption intensity, had positive and statistically significant spillover effects on large-scale non-adopting firms within the same industry and city, leading to significant improvements in their TFP. Furthermore, the effect of robot adoption on firm productivity varied by robot type, firm size, and industry. Multi-functional robots (MFRs) exhibited a more substantial effect compared to singlefunctional robots (SFRs). The automotive, plastic/chemical, and metal industries had great productivity gains compared to other industries. These results withstand various rigorous robustness checks, reaffirming the validity and consistency of our findings.

Impact of capital and labour based technological progress on carbon productivity

In 2020, China pledged at the United Nations General Assembly to achieve carbon peaking by 2030 and carbon neutrality by 2060, and so far, China has made remarkable achievements in actively realizing carbon emission reduction and maintaining the international ecological environment. Among them, the role played by the technological progress bias of different elements is worth paying attention to. In addition, according to the current situation of energy consumption in China, based on the capital-labor technological progress bias, considering the introduction of the direction of technological progress of the energy factor plays a key role in analyzing the carbon productivity of China. This study utilizes panel regression to explore the paths and effects of capital-labor and factor-augmenting technological progress biases on carbon productivity. We also closely examine the regional heterogeneity and spatial spillover effects of technological progress biases. This study is based on inter-provincial panel information from China from 2006 to 2020. The study found that: (1) When the technological progress bias favors the capital factor among capital and labor, it will significantly increase the level of carbon productivity; The introduction of energy factor innovations,energy-enhanced technological progress, base on the technological progress bias of capital and labor will significantly reduce the level of carbon productivity. (2) Through the threshold model analysis, it is found that there is a nonlinear effect of technological progress bias on carbon productivity. Among them, in capital-biased and energy-enhanced provinces, the impact of capital-technology progress bias on carbon productivity shows an inverted “U" trend of first increasing and then decreasing. (3) In capital-labor technological progress, when technological progress is biased toward capital, it will significantly increase carbon productivity in the Northeast, East and West regions, while the opposite is true in the Central region, and the spillover effect of capital-labor technological progress is not significant; energy-enhanced technological progress bias has a negative impact on carbon productivity in all four major regions, and there is a significant spillover effect in all four major regions.

Tourism in pandemic: the role of digital travel vouchers in China

The COVID-19 pandemic has created significant challenges to the global economy, especially within the tourism industry. Economic downturns and psychological barriers make it even more challenging to attract tourists back. This paper explores the stimulating effect of digital travel vouchers on tourist mobility, as well as the heterogeneous effects of different vouchers modes. Using monthly data on tourist inflows in 306 cities from September 2020 to December 2021, collected by a web-crawling tool, and manually gathered information of digital travel vouchers issued during this period, our analyses show that digital travel vouchers can boost tourist inflows. Their effectiveness, however, depends on the method of issuance and the total number distributed. Furthermore, significant spillover effects among neighboring cities are found through a spatial econometric model.

Insight into clean energy market’s role in the connectedness between joint-consumption metals

The characteristic of joint consumption creates complex linkage between metal markets, which helps provide new insights into the energy-metal nexus relationship. To this end, this paper analyzes the price spillover effects between clean energy metals with joint consumption properties and further captures clean energy market's role in this dynamic connectedness. The results show that: (1) There are significant spillover effects within the clean energy metal markets, mainly dominant by short-term factors. The price fluctuation risk in the wind energy metal markets is more easily transmitted than the solar energy metal markets. (2) Copper and Tellurium markets are the main sources of risk in the spillover network of solar energy metal markets, while Copper and Aluminum act as the centers of risk transmission in the spillover network of wind energy metal markets. (3) Clean energy markets significantly impact the spillovers of metal markets, and exhibit non-linear movements, altering the spillover roles of some metal markets in the spillover network. Our findings reveal the system-wide price fluctuation patterns in clean energy metal markets, helping provide targeted recommendations in the risk management of energy-metal nexus system.

Spillover dynamics in DeFi, G7 banks, and equity markets during global crises: A TVP-VAR analysis

Decentralized finance (DeFi) has become of significant interest for investors in both the financial and digital sectors. We use a time-varying parameter vector autoregression (TVP-VAR) approach to estimate the static and dynamic connections between and within DeFi, G7 banking, and equity markets. We focus on critical events such as the COVID-19 pandemic, the cryptocurrency bubble, and the Russia-Ukraine conflict. The results highlight interconnectedness and significant spillovers within and between the markets, especially during the COVID-19 pandemic. Notably, there were significant spillover effects from the G7 banking and equity markets to Japan and DeFi assets. The findings demonstrate a robust connection between DeFi platforms, G7 banking, and stock markets throughout these tumultuous periods. Policymakers, investors, and entrepreneurs are recommended to keep a close eye on changes in traditional banking and equity markets to adjust the risk of DeFi assets.

Synthesization of macroeconomic policies and stock return synchronicity: Evidence from countries along the Belt and Road Initiative

This study investigated the impact of macroeconomic policy changes in China on the stock markets of its Belt and Road Initiative (BRI) partners, whose economies have significant spillover effects. This topic is critical, because the BRI is a pivotal regional cooperation framework, which substantially influences economic dynamics and trade expansion across participating countries. We studied 47 countries from 2006 to 2021, and found a positive relationship between the effectiveness of macroeconomic policies and stock return synchronicity in BRI countries. We also explored monetary and fiscal policies and discovered that increased policy intensities correlate with higher synchronicity in BRI stock markets. Additionally, the benefit of macroeconomic policies was more pronounced in countries with closer trade ties and greater policy coordination with China. Further investigation showed that during periods of increased economic policy uncertainties within China and globally, the increase of stock return synchronicity is more pronounced.

Researching the Influence of Rural University Campuses on Rural Economic Development: Evidence from Chinese Counties between 2001 and 2020

While there have been studies on the relationship between higher education institutions and regional economic growth, few have delved into the economic impact of decentralized higher education institutions at the county level and associated reginal disparities in terms of socio-economic development. Utilizing the data of the Chinese universities that started to establish their campuses in counties since the year 1999, this study investigates the influence of rural university campuses on county-level GDP and industrial composition spanning from 2001 to 2020. It also delves into the temporal dynamics and regional discrepancies associated with this impact. The findings of this study show that (a) rural university campuses wield a notable positive influence on the GDP of their respective counties, particularly shaping the structure and ratio of secondary and tertiary industries; (b) the magnitude of this effect is contingent upon the duration of campus establishment and growth, intensifying over time; (c) variations in this impact are evident across the eastern, central, and western regions of China, where there are vast socio-economic differences. This study underscores the significant spillover effect of higher education decentralization on county-level economies and advocates for the pivotal role of rural university campuses in propelling county-level economic progress. Additionally, it proposes coordinated policy support from national, regional, and rural university campus authorities; the establishment of requisite support structures; and the comprehensive consideration of regional nuances.

Exploration and future trends on spatial correlation of green innovation efficiency in strategic emerging industries under the digital economy: A social network analysis

With digital technological change and the increasing frequency of interregional innovation links, the spatial correlation and diversity of strategic emerging industries' green innovation efficiency (SEI-GIE) need to be explored in depth. This paper innovatively constructs the SEI-GIE input-output index system under digital economy. The proposed grey model FINGBM(1,1) with ω-order accumulation and weighted initial value optimization realizes effective prediction of 7 input-output indicators of 30 provinces in China from 2021 to 2025. Super-SBM-DEA, gravity model, and social network analysis are applied to explore spatial network structure's dynamic process of SEI-GIE from 12th to 14th Five-Year-Plan period (2011–2025). Empirical results show that (1) Under the effect of digital economy, the SEI-GIE in China generally shows a U-shaped fluctuation trend, in which the growth trend in the central region is obvious, and the western region shows significant fluctuations. (2) The spatial correlation network of SEI-GIE presents a complex and stable center-periphery circle. Particularly, the overall increase in network efficiency highlights the strong small-world characteristics. (3) Beijing, Shanghai, Zhejiang and Jiangsu have always been in the leading core position, with strong influence and control; And Tianjin's core position in the network will decline. Additionally, Guangxi and Chongqing have great potential, but Guangdong needs to strengthen its radiation effect. (4) Block model shows that plate-I (Beijing, Tianjin) receive spatial spillovers from others, while plates-III,IV have significant spillover effects. This study provides theoretical reference for policymakers from a network perspective to promote development of China's SEI-GIE.

Can green technology mergers and acquisitions enhance sustainable development? Evidence from ESG ratings

AbstractIn order to better understand the impact of green technology mergers and acquisitions (GTMA) on sustainable development, the difference‐in‐differences model is applied to investigate the direct impact and underlying mechanisms of GTMA on the environment, society, and governance (ESG) in China. Additionally, we established a two‐dimensional framework of “market versus government” to analyze the heterogeneity. The results show that: (1) GTMA is positively aligned with greater ESG performance, indicating that GTMA is an effective way to enhance sustainable development. (2) GTMA fosters ESG performance by boosting green subsidies (i.e., signal transmission channel), lowering financing costs (i.e., impression management channel), and enhancing green innovation (i.e., technology synergy channel). (3) The positive relationship is largely dependent on the collaborative supervision environment (i.e., “strong” market and “strong” government) and the appropriate policy environment (i.e., “big” market and “small” government). (4) Furthermore, GTMA carries significant spillover effects, with other enterprises in the same industry improving their ESG performance. Additionally, this paper proposes targeted directions for the government and enterprises to take advantage of GTMA dividends to realize superior ESG performance.

Quantile spillovers and connectedness between oil shocks and stock markets of the largest oil producers and consumers

This study explores the connectedness between major oil-producing and consuming countries' stock markets (United States, China, Russia, India) and different oil shocks categorized as demand, supply, and risk shocks, following Ready's (2018) framework. Employing a quantile-based connectedness approach and quantile cross-spectral dependence, our analysis spans from July 02, 2007 to May 31, 2023, encompassing diverse market conditions and events. These methodologies help identify interdependence patterns in extreme market scenarios at different time intervals. Key findings show variations in how these stock markets respond to oil shocks, depending on market conditions and quantiles. Demand-related shocks have the most significant spillover effects on the United States, Russia, and India, while risk-related shocks dominate as transmitters of shocks to the United States, China, and India in median quantiles. Market interconnectedness strengthens during extreme market conditions, reflecting historical events. Additionally, bearish markets offer diversification opportunities between these countries and crude oil. This study emphasizes the need for tailored investment strategies, monitoring global oil demand trends, dynamic portfolio management, crude oil inclusion in portfolios, and proactive responses to market players and geopolitical events. These insights benefit investors and policymakers seeking to optimize strategies in the interconnected global financial landscape.

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.

GEOPOLITICS, UNCERTAINTY, AND CRYPTOCURRENCY: A LOVE TRIANGLE GONE WRONG

This study aims to investigate the spillover effects from geopolitical risks (proxied by the geopolitical risk index GPRD) and cryptocurrencies-related uncertainty (proxied by the Cryptocurrency Uncertainty Index UCRY) to cryptocurrencies. We utilize the Baruník and Křehlík (2018) framework to detect time-frequency connectedness. Our investigation for the period 2017 to 2022 discovers significant spillover effects from both indices (GPRD and UCRY) to cryptocurrencies. Utilizing the information transmission theory and network graphs, our findings reveal that some cryptocurrencies function as net receivers of spillovers from geopolitical risks and uncertainty in the short-term, while over longer time horizons they transform into net transmitters of spillovers to uncertainty. The study underscores the importance of comprehending how uncertainty due to various factors (geopolitical, policy changes, regulatory changes, etc.) could affect the cryptocurrencies’ markets.

Volatility spillovers of cloud stocks: Evidence from China using the dynamic connectedness approach

AbstractBased on daily data from 2013 to 2022, this study examines the spillover effects of volatility between cloud stocks and other asset classes (global stocks, treasury bonds, gold and crude oil) using the VAR connectedness approach. The results show that there is a significant spillover effect from global stocks and crude oil markets to the cloud stock market. The spillover effects become stronger whenever there are shocks such as economic crisis, turbulence in the international financial markets, COVID‐19 and global inflation. However, nearly 91% of the variations of cloud stocks come from within, suggesting that the diversification/hedging value of cloud stocks is potentially high.

Assessing and mapping water-energy-food nexus smart innovations and practices in Vhembe District Municipality, Limpopo Province, South Africa

Water, energy, and food and their interactions (commonly referred to as the WEF nexus) are critical pillars to resolving the intractable global challenges such as poverty, hunger, malnutrition, poor sanitation, climate, and health crises. The nexus approach, practices, and innovations at the household level are critical determinants of whether resource use efficiency, co-benefits, basic rights to water and food, and sustainability governance are attained. In particular, smart WEF innovations can contribute to the current generations' economic, social, and environmental needs without compromising the needs of the future generation. The study aimed to identify smart innovations, practices, and factors influencing their adoption to inform policy and decision-making processes. The study intends to support scaling up the adoption of innovations and practices that enhance sustainability and resource security in support of the sustainable development goals (SDGs). Semi-structured interviews and key informant interviews (KII) supplemented with observational checklists were used to identify the WEF nexus smart technologies, innovations, and practices in Vhembe District Municipality, Limpopo Province, South Africa. Data were collected from a sample size of 128 households in the study area. Our findings revealed synergistic smart innovation practices across WEF resource use and management practices. Though indigenous knowledge (IK) practices were widely evident in the study area, non-existent WEF smart knowledge support systems existed in the study area. Indigenous knowledge practices were the most elicited innovation by 99.2% of households, suggesting it is critical to advancing WEF smart innovations and practices and needs to be integrated into any policy and governance interventions. A proportion of households recycle water (27%), whilst 53% use untreated water. Furthermore, the knowledge systems on smart WEF innovations were fragmented despite their potential to synergize sustainability objectives. Exploring innovation platforms (IPs) as vehicles for dissemination, innovation, and extension and advisory service delivery, as well as validation of Indigenous Knowledge Systems (IKS), has the potential to contribute to the diffusion, uptake, and scaling of existing innovation and practices with significant spill-over effects on WEF resource security and sustainability outcomes both at local and extra local scales.

Can innovation factor agglomeration enhance the government’s environmental governance capacity?

Innovative manpower agglomeration and innovative capital agglomeration provide vitality and guarantees for a country to achieve high-quality development, and the improvement of the government's environmental governance capacity can guarantee the smooth advancement of the country's ecological civilization construction work; thus, it is very important to study the impact of innovation factor agglomeration on the government's environmental governance capacity. Based on this, this article establishes a mediated moderating effect model and a spatial econometric model, adopts the panel data of 30 provinces in China from 2009 to 2020 as an example, and divides innovation factor agglomeration into innovation manpower agglomeration and innovation capital agglomeration to study the relationship between them and the government's environmental governance capacity. The results of the empirical analysis show that (1) both innovation manpower agglomeration and innovation capital agglomeration can significantly promote the improvement of the government's environmental governance capacity. (2) Technological innovation plays a mediating role in promoting the government's environmental governance capacity through innovation factor agglomeration. Voluntary environmental regulation can promote the government's environmental governance capacity, but it attenuates the role of innovation capital agglomeration in promoting the government's environmental governance capacity. (3) Local innovative manpower agglomeration has a significant spillover effect, and innovative manpower agglomeration in neighboring places inhibits the enhancement of local governments’ environmental governance capacity. (4) Innovation capital agglomeration in neighboring places will inhibit the local government's environmental governance capacity, but its effect is much smaller than the promotion effect of local innovation capital agglomeration on the government's environmental governance capacity.

Spatio-temporal pattern assessment of China's environmental performance and its spatial drivers: evidence from city-level data over 2003-2019.

A comprehensive assessment of China's environmental performance (EP) and an investigation into its driving factors are essential prerequisites for advancing environmental protection efforts. However, existing studies have often exhibited a one-sided EP evaluation approach and lacked a systematic perspective. Consequently, this study has adopted a holistic approach by integrating environmental protection and pollution within the same theoretical framework. We have employed the "P-S-R" model to comprehensively assess the EP of 272 cities from 2003 to 2019. Concurrently, we have applied the spatial Durbin model to analyze EP drivers utilizing three spatial matrices. The findings of this study reveal several vital insights. Firstly, the mean EP value for China is 0.1138, indicating a low level, but it demonstrates a consistent upward trend over the years. When comparing cities with high EP, they are predominantly situated in northern China, northeastern China, and certain areas along the southeastern coast. Secondly, from a spatial perspective, the directionality of EP exhibits a trend from "northeast to the southwest," with the center of gravity located in and around Zhumadian, Henan Province, gradually shifting towards the northeast. The majority of cities fall within the H-H and L-L clusters, displaying significant positive spatial autocorrelation effects. Thirdly, EP drivers encompass a wide range of factors, including economic development, urbanization, resource dependence, industrial structure, infrastructure construction, environmental regulation, government regulatory capacity, scientific and technological innovation, and foreign direct investment. These drivers also exhibit significant spillover effects. Finally, the characteristics of EP development vary between resource-based cities (RBCs) and non-resource-based cities (non-RBCs), as well as among the eastern, central, and western regions. Moreover, there are disparities in the driving factors' direct, indirect, and overall effects. Consequently, we must propose tailored strategies and recommendations to enhance EP, considering the heterogeneous effects of influencing factors across different city types, regions, and collaboration approaches.

Biased innovation and network evolution: digital driver for green innovation of manufacturing in China

ABSTRACT The study aims to explore the spatial association network characteristics of biased green innovation in the manufacturing sector and its core drivers. This study constructs a Malmquist-Luenberger decomposition index model to identify the input and output biases of green technological innovation (GIIM and GIOM) in the manufacturing industry. This study uses a modified gravity model and social network analysis method to conduct a robust assessment of GIIM spatial association network of 30 provinces in China from 2012 to 2021. The results show: (1) The GIIM association network structure is stable and has good accessibility, with close connections between provinces and blocks, and significant spillover effects between provinces. (2) The regional network shows a “core-periphery” spatial variation, with the core area expanding and the peripheral area shrinking. (3) The digital transformation characteristics of the network components and the intensity of environmental regulation have a significant impact on GIIM.

The implication of cryptocurrency volatility on five largest African financial system stability

This study examined the interconnectedness and volatility correlation between cryptocurrency and traditional financial markets in the five largest African countries, addressing concerns about potential spillover effects, especially the high volatility and lack of regulation in the cryptocurrency market. The study employed both diagonal BEKK-GARCH and DCC-GARCH to analyze the existence of spillover effects and correlation between both markets. A daily time series dataset from January 1, 2017, to December 31, 2021, was employed to analyze the contagion effect. Our findings reveal a significant spillover effect from cryptocurrency to the African traditional financial market; however, the percentage spillover effect is still low but growing. Specifically, evidence is insufficient to suggest a spillover effect from cryptocurrency to Egypt and Morocco’s financial markets, at least in the short run. Evidence in South Africa, Nigeria, and Kenya indicates a moderate but growing spillover effect from cryptocurrency to the financial market. Similarly, we found no evidence of a spillover effect from the African financial market to the cryptocurrency market. The conditional correlation result from the DCC-GARCH revealed a positive low to moderate correlation between cryptocurrency volatility and the African financial market. Specifically, the DCC-GARCH revealed a greater integration in both markets, especially in the long run. The findings have policy implications for financial regulators concerning the dynamics of both markets and for investors interested in portfolio diversification within the two markets.