Robust Test Research Articles

Impact of fund cliques on corporate cash dividends: Evidence from China

Utilizing 2007–2021 data on Chinese A-share listed companies, this study explores how fund cliques affect corporate cash dividends. The results reveal that fund cliques can significantly reduce corporate cash dividends. This conclusion is verified after addressing possible endogeneity concerns and conducting a series of robustness tests. Mechanism tests indicate that ownership concentration and agency cost are the primary channels. The results further show that the negative effect of fund cliques on cash dividends is more significant in larger firms, as well as in firms that have lower regional investor protection and greater media attention. Furthermore, we find that fund cliques can significantly increase corporate stock dividends, and the reduction of cash dividends promotes fund cliques’ share lessening, from which funds can obtain higher share returns. This study has some implications for protecting shareholder rights and maintaining capital market stability.

Coordinated Regional Economic Development: A Study of the Relationship Between Regional Policies and Business Performance

Abstract In the context of the global spread of the Corona Virus Disease 2019, improving enterprise performance, promoting regional economic development, and solving the problem of unbalanced and inadequate regional development are important considerations for regional policy formulation. To understand the relationship between regional policies and corporate performance, this study starts with theories related to regional policies and corporate performance, and proposes two hypotheses. The Western Development, Northeast Revitalization of the Old Industrial Base, and Central Rise Plan are selected as typical representatives of regional policies, and the hypotheses are verified through a double difference model. The results showed that regional policies could improve corporate performance, especially for state-owned enterprises (SOEs). The correlation between the revitalization of the old industrial base in Northeast China and the performance of SOEs was strong, with a correlation coefficient of 0.832. When conducting robustness tests by changing time periods, the correlation values between the performance of SOEs and policies in the three policy implementation areas of revitalizing Northeast China, western development, and central rise were 0.887, 0.764, and 0.652, respectively. Compared to non-SOEs, SOEs had a more significant effect on regional policy improvement. This conclusion was consistent with the empirical results of hypotheses 1 and 2. Based on this foundation, recommendations are offered for the suitability of regional policies to furnish theoretical frameworks for enhancing the regional policy structure and augmenting the precision and inclusivity of regional policy development. The research model is utilized to reveal the important role of regional policies in promoting enterprise performance improvement and promoting regional economic development. It can provide strong evidence support for policy makers. The study offers a scientific theoretical basis and empirical support that assist policymakers in comprehending policy objectives and implementation strategies with greater precision. Therefore, the coordinated advancement of regional economic development is facilitated.

The Impact of Smart City Policies on City Resilience: An Evaluation of 282 Chinese Cities

This study uses four dimensions, namely social resilience, economic resilience, infrastructure resilience, and ecological resilience, to construct an index system for urban resilience. The subject data came from the panel data of 282 prefecture-level cities in China from 2006 to 2020. We selected a multiperiod double-difference model to study the effects of smart city pilot policies on macro-urban resilience. By conducting parallel-trend tests and selecting appropriate robustness tests, this study drew the following relevant conclusion: smart city pilot policies can have a positive effect on the urban resilience level. These policies exert their influence by facilitating industrial structure upgrading, which plays a partial mediating role. Considering different city area distributions and city scales, smart city pilot policies can have significant heterogeneity in their enhancement of urban resilience. The effect is pronounced in the “east > central > west” states and is more likely to have a significant impact on small- and medium-sized cities. Therefore, promoting the scope of smart city pilots, strengthening the intermediary role of industrial structure upgrading, and implementing differentiated policies for different regions and city sizes are important for sustainable urban development.

The impact of green low-carbon development on public health: a quasi-natural experimental study of low-carbon pilot cities in China.

In recent years, climate change and environmental pollution have posed significant threats to public health. As environmental policies such as low-carbon city initiatives are progressively implemented, their role in enhancing public health has become a topic of growing interest. This study aimed to investigate the relationship between green low-carbon development and public health and to analyze the underlying mechanisms. We utilized data from 271 prefecture-level cities in China spanning from 2007 to 2020, focusing on green low-carbon development, climate change, environmental pollution, and public health. Employing the quasi-natural experimental framework of China's low-carbon city pilot projects, we constructed a multi-site difference-in-differences (DID) model for empirical analysis. Various robustness checks, including parallel trend tests, placebo tests, sample selection bias checks, and adjustments to the temporal and spatial scope of the samples, were conducted to ensure the reliability of the results. Additionally, we explored the positive effects of green low-carbon development on public health through dual mediation pathways involving climate change mitigation and pollution reduction. Finally, we examined the heterogeneity of the results across different city tiers, economic growth rates, levels of technological investment, and green finance development. The findings indicate that green low-carbon development significantly enhances public health, a conclusion supported by robustness tests. Mechanism analysis reveals that the benefits of green low-carbon development on public health are realized through mitigating climate change and reducing environmental pollution. Further analysis reveals that the positive impact on public health is more pronounced in first-and second-tier cities, as well as in cities with faster economic growth, greater technological investment, and more developed green finance sectors. This study highlights the crucial role of urban green low-carbon development in improving environmental quality and public health. In addition to providing empirical evidence that supports the promotion of green low-carbon development in cities, the results point to policy recommendations for enhancing public health. Moreover, the findings contribute to the development of environmental policies and the implementation of the "Healthy China" strategy.

Dividing Global Business Process: Safeguarding Confidential Data

This study shows that manufacturing unit process dispersion serves as an operational-level variable that allows businesses to modify their information protection strategy when it comes to sourcing business services overseas to suit the regulatory framework of the chosen host nation as well as the potential for using internal controls over activities carried out overseas. Our hypothesis is that businesses are more inclined to split up their processes among several foreign production facilities rather of concentrating every procedure duties in one unit when the aforementioned mechanisms are unavailable. Businesses may take advantage of the synergies between the scattered pieces of a process while lowering the risk of individual fragment theft because of IT-enabled integration capabilities. Robustness tests and empirical findings closely align with these theories. Additionally, we discover that the likelihood of using the defense mechanism against process fragmentation increases with the firm's expertise in the host nation as well as the alternative worth of the activity's unique knowledge that is outsourced.

Hidden hearing loss in a Charcot-Marie-Tooth type 1A mouse model.

Hidden hearing loss (HHL), a recently described auditory neuropathy characterized by normal audiometric thresholds but reduced sound-evoked cochlear compound action potentials, has been proposed to contribute to hearing difficulty in noisy environments in people with normal hearing thresholds and has become a widespread complaint. While most studies on HHL pathogenesis have focused on inner hair cell (IHC) synaptopathy, we recently showed that transient auditory nerve (AN) demyelination also causes HHL in mice. To test the effect of myelinopathy on hearing in a clinically relevant model, we studied a mouse model of Charcot-Marie-Tooth type 1A (CMT1A), the most prevalent hereditary peripheral neuropathy in humans. CMT1A mice exhibited the functional hallmarks of HHL together with disorganization of AN heminodes near the IHCs with minor loss of AN fibers. These results support the hypothesis that mild disruptions of AN myelination can cause HHL and that heminodal defects contribute to the alterations in the sound-evoked cochlear compound action potentials seen in this mouse model. Furthermore, these findings suggest that patients with CMT1A or other mild peripheral neuropathies are likely to suffer from HHL. Furthermore, these results suggest that studies of hearing in patients with CMT1A might help develop robust clinical tests for HHL, which are currently lacking.

From Financialization to Sustainability: The Impact of Climate Risks on Shadow Banking Activities in Non-Financial Firms in China

Climate risks are increasingly shaping corporate strategies, raising important considerations for sustainability. This study explores the impact of climate risks on the shadow banking activities of non-financial firms, examining how these risks influence sustainable financial practices. Analyzing a sample of Chinese listed firms from 2010 to 2022, this study finds that climate risks are negatively associated with shadow banking activities, reflecting a shift towards more sustainable financial management. This finding remains robust through various robustness checks and tests for endogeneity. Heterogeneity results indicate that the reduction in shadow activities due to climate risks is more pronounced in firms with higher dependence on external financing and weaker profitability. Mechanism results suggest that climate risks amplify cross-sectional risks for firms, increasing risk sources. Simultaneously, companies enhance their preference for holding cash to address potential risks. The combined effect of these factors leads to a decrease in shadow banking activities among non-financial firms, aligning with a shift towards sustainability. This study provides new insights into understanding how climate risks drive sustainable financial decision-making and enriches the research on the determinants of firm financialization.

The impact of green trade barriers on China’s photovoltaic products exports to ASEAN

In the context of the global push towards a green economy, this research investigates the impact of green trade barriers on the export trade volume of Chinese photovoltaic products to ASEAN from 2010 to 2021. The strong evidence indicates that green trade barriers have a positive effect on improving the export trade volume of Chinese photovoltaic products to ASEAN. This conclusion remains robust through multiple robustness tests, including excluding data from pandemic-affected years and the only developed country Singapore. Moreover, the impact of green trade barriers varies with exchange rate levels and language environments. In countries with higher exchange rates and countries with different official languages, the positive effect of green trade barriers on trade is more significant. In contrast, in countries with the same official language, green trade barriers have a negative effect on improving the trade volume. Additionally, higher levels of renewable energy consumption diminish the beneficial effect of TBT on photovoltaic trade, and CO2 emissions lessen the detrimental effect of TBT on photovoltaic trade. The findings provide valuable insights for policymakers, suggesting that align with international standards, provide differentiated technical assistance and adopt transparent trade rules when formulating green trade barriers to better support the competitiveness and export growth of Chinese photovoltaic products in the international market.

Unveiling the Concealed Risks: The Influence of Digital Finance on Cyber Financial Crime in China

Digital finance has emerged as a pivotal catalyst for economic growth in China, profoundly altering social structures and behaviors. Despite its benefits, it has also engendered financial risks, thereby complicating social governance. This study utilizes a panel dataset from China’s provinces spanning 2011 to 2021 to analyze the influence of digital finance development on cyber financial crime rates. The results reveal a positive correlation: an increment of one unit in the digital finance index is associated with a 0.058 unit increase in cyber financial crime rates. These findings are robustly supported by tests for endogeneity and robustness. Variations in impact are observed across urbanization levels and average wage brackets. And the study identifies income distribution and traditional financial demand as the mechanisms of the effect. Furthermore, this study presents strategic recommendations to enhance financial inclusion, facilitate income redistribution, strengthen cyber crime governance, and integrate digital and traditional finance for the advancement of financial modernization.

Prediction of optical properties of uniaxial hyperbolic nanospheres via artificial neural network

In this study, absorption and scattering multilayer perceptron models are developed and validated to predict the optical spectra of uniaxial hyperbolic nanospheres. The finite difference time domain method is used to generate the dataset of the absorption and scattering optical spectra. The models’ optimal performance is achieved for 5 hidden layers and 80 neurons for the absorption and the same hidden layers with 120 neurons for the scattering. The predictions by the model on the test dataset give a low average mean squared error of 0.000145 for the absorption and 0.00071 for the scattering. We also performed a robustness test by using parameters outside the initial parameters used for the training and the predictions are in good agreement with the actual datasets for both absorption and scattering. This research shows the application of artificial neural networks to predict the optical properties of hyperbolic materials and lays the groundwork for developing more complicated neural network models to predict complex phenomena in hyperbolic metamaterials, which are faster and computationally less expensive than using conventional simulation methods. Hyperbolic metamaterials offer unique optical properties that can be used to design new optical devices.

Negative Externalities of Regulation: Identity‐relevant Information in Mandatory Short‐selling Disclosures

Since November 2012, short sellers have been required to disclose all their significant short positions against European firms. While this regulation was primarily implemented to have implications for financial markets, I explore its negative externalities on shorted firms. Using mandatory disclosures of short positions between 2013 and 2019, I find that short‐selling notifications and the number of short sellers with an open short position are positively associated with shorted firms’ dividend policies. Notably, the association is more pronounced for upward and initial notifications compared to downward and follow‐up notifications. These results are robust to several matching methods and a battery of robustness tests. Additional results reveal that shorted firms might increase their dividends because short‐selling disclosures reveal the identity of active and large short sellers, highlighting the importance of short sellers’ heterogeneity. I also show that shorted firms increase their dividends temporarily and do not react homogenously, as they engage in a cost–benefit analysis to decide whether to use their dividend policy. Overall, I document the real effects on shorted firms of an alternative form of short selling, bridging the gap between passive and activist short selling. I conclude that mandatory short‐selling disclosure regimes carry negative externalities for firms due to the provision of short sellers’ identity‐relevant information.

High-Precision Heterogeneous Satellite Image Manipulation Localization: Feature Point Rules and Semantic Similarity Measurement

Misusing image tampering software makes it easier to manipulate satellite images, leading to a crisis of trust and security concerns in society. This study compares the inconsistencies between heterogeneous images to locate tampered areas and proposes a high-precision heterogeneous satellite image manipulation localization (HSIML) framework to distinguish tampered from real landcover changes, such as artificial constructions, and pseudo-changes, such as seasonal variations. The model operates at the patch level and comprises three modules: The heterogeneous image preprocessing module aligns heterogeneous images and filters noisy data. The feature point constraint module mitigates the effects of lighting and seasonal variations in the images by performing feature point matching, applying filtering rules to conduct an initial screening to identify candidate tampered patches. The semantic similarity measurement module designs a classification network to assess RS image feature saliency. It determines image consistency based on the similarity of semantic features and implements IML using predefined classification rules. Additionally, a dataset for IML is constructed based on satellite images. Extensive experiments compared with existing SOTA models demonstrate that our method achieved the highest F1 score in both localization accuracy and robustness tests and demonstrates the capability for handling large-scale areas.

Can value-added tax refund policy inhibit corporate financial fraud? Evidence from China

This paper examines the impact of China's 2018 VAT refund policy on corporate financial fraud, using a difference-in-differences approach. Findings demonstrate that the policy significantly reduces corporate financial fraud, which is confirmed through various robustness tests. The VAT refund policy deters corporate financial fraud by addressing motivational pressures, limiting opportunities, and challenging justifications, effectively targeting the fraud triangle's core elements. The policy's effects are especially strong in high-tech and privately-held firms. The study extends knowledge of VAT policies' roles in corporate decision-making and offers policy guidelines to combat corporate malfeasance.

The haze reduction effect in china under the digital economy

Haze pollution is becoming increasingly serious as it endangers human health and pressurizes economic development. This study incorporates the digital economy and haze pollution management into a unified research framework, and uses the panel data of 285 cities in China from 2011 to 2020 to precisely identify the haze reduction effect of the digital economy, governance mechanisms, and the non-linear and spatial spillover characteristics of the haze reduction effect in China. The findings demonstrate that: First, the digital economy can significantly reduce haze pollution, and this conclusion still holds after considering endogeneity and other robustness tests. Second, the digital economy can inhibit haze pollution by promoting industrial upgrading and improving total factor energy efficiency, and there is a chain mechanism of “industrial green transformation → total factor energy efficiency” and “factor market distortion → total factor energy efficiency.” However, the haze reduction mechanism of digital economy is heterogeneous to cities with different levels of economic development. Independent transmission mechanism plays a complete intermediary role in cities with low level of economic development, while it plays a partial intermediary role in cities with high level of economic development. Chain transmission mechanism only has an impact on cities with high level of economic development. Third, there is an evident threshold effect on the digital economy, and its haze reduction effect has N-type non-linear characteristics. Meanwhile, Digital economy has threshold effect on the mechanism of restraining haze pollution through total factor energy efficiency, industrial green transformation and factor market distortion. With the development of total factor energy efficiency, industrial green transformation and the reduction of factor market distortion, the haze reduction effect of digital economy can produce a qualitative leap. Fourth, Haze pollution in the spatial, temporal, and spatial dimensions has spillover and warning effects, and the haze reduction effect of digital economy shows strong positive externalities. Therefore, the digital economy should be actively developed, and industrial green transformation and factor market reform should be promoted to curb haze pollution by promoting industrial upgrading, improving energy efficiency and strengthening regional joint prevention and control mechanisms.

Adoption of green production technologies by farmers through traditional and digital agro-technology promotion–An example of physical versus biological control technologies

Traditional and digital agro-technology promotion are two critical methods for disseminating agricultural technology information, which effectively encourages farmers to employ green production technologies. This paper empirically examines the effects of traditional and digital agro-technology promotion on the adoption of green production technologies by 619 apple producers in the primary apple production area of Guanzhong Plain, China, using micro-survey data. The study's findings suggest that both promotion models significantly influence the adoption of green production technologies by farmers. This conclusion remains valid after the instrumental variables approach and a series of robustness tests are implemented to address endogeneity concerns. The heterogeneity analysis revealed that the impact of the two promotion modes on the adoption behavior of green production technologies by farmers varied by scale. Specifically, digital agro-technology promotion had a greater impact on the adoption of physical control technologies by small-scale farmers, while traditional agro-technology promotion had a greater impact on the adoption of biological control technologies by large-scale households. In addition, the technological differences between traditional agrotechnology promotion and digital agrotechnology promotion result in clear complementary and substitution effects. The two modes of promotion have evident complementary effects for biological control technologies. The two modalities of promotion for physical control technologies exhibit complementary effects between social communication-type promotions and traditional agro-technology promotions, as well as substitution effects between short-video promotions and traditional agro-technology promotions.

Impact of climate change on agricultural productivity: a combination of spatial Durbin model and entropy approaches

PurposeGlobal climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.Design/methodology/approachThe authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.FindingsThe results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.Practical implicationsThe findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.Originality/valueThe paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

Impact of foreign remittances, disaggregated energy use, on environmental pollution in the top recipient countries for remittances

AbstractForeign remittance has become an essential source of wealth in recent decades, with far‐reaching effects on various economic indices and CO2 emissions. This research examines the impact of remittances and disaggregated energy consumption on CO2 emissions in an ‘Environmental Kuznets Curve (EKC)’ framework utilising a global sample of 46 top remittance‐receiving countries during 1996–2020. The study confirms the EKC proposition and demonstrates that a decline in CO2 emissions is connected to remittance, renewable energy use and financial development. Additionally, our results stand up to various robustness tests, ensuring the reliability of our findings. Our research suggests that to reduce the adverse effects of non‐renewable energy on environmental quality, governments, regulators and other stakeholders should implement rigorous market regulations and allocate substantial financial resources to R&D for innovating environmentally friendly production technologies. The government may also provide incentives for importing environmentally friendly production tech, such as tax rebates and subsidies. Lastly, foreign remittance and renewable energy are two tools governments may employ to cut CO2 emissions and improve environmental quality.

Empirical evidence on the impact of the "new round" of Sino-US trade frictions on China's foreign trade industrial policy and high-quality development.

The global attention on industrial policy's effectiveness spans across various sectors, particularly in international trade where the focus is on enhancing the quality of foreign trade, which is crucial to understand how research hotspots and key issues can synergize. We consider the 2018 "new round" of Sino-US trade friction as an external event and analyze panel data from 2009 to 2022 comprising 1141 Chinese A-share listed enterprises. Our empirical research unequivocally demonstrates that industrial policy has significantly propelled China's foreign trade towards high-quality development by 1.8240%. We conducted rigorous tests for robustness, heterogeneity, and endogeneity. Additionally, our results reveal that inhibitory influences arising from industrial policy on total assets or R&D investment attenuate this promotion effect; There exists a diminishing promotion effect between total assets and R&D investment. Our paper provides valuable insights into understanding their logical relationship while offering guidance for enterprises, markets, and governments in overcoming challenges collectively.

Veto players and gun violence in drug markets: Analysis based on field observations of eighty drug-selling spots on the Westside of Chicago

PurposeThe study analyzed whether the number of drug-selling spots has an impact on shooting incidents in drug markets on the Westside of Chicago. MethodsA theory of gun violence in drug markets was developed by building on veto players theory, and tested using a series of two-way fixed-effects Poisson regressions. The main variable of interest—drug-selling spots—comes from an original dataset comprised of eighty drug-selling spots identified through covert field observations. Controls included: the average age and average number of drug spot workers at the drug-selling spots, concentrated disadvantage, population, police anti-drug operations, and drug demand. FindingsIn support of the theory, the main statistical models and robustness tests consistently found that the larger the number of drug-selling spots in a given drug market, the higher the number of shooting incidents over time. Only the control for population remained consistently statistically significant across model specifications. ConclusionThe study contributes to the drug market literature by offering a novel theory of drug market violence, as well as a replicable way for researchers to observe drug market activity without disturbing the natural behaviors of market participants. The operationalization of drug-selling spots as sub-factions of organized criminal groups also provides a novel approach to the study of drug market violence.

Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning

ABSTRACTWe propose a new momentum‐determined indicator‐switching (N‐MDIS) strategy, harnessing the power of machine learning to enhance the accuracy of equity premium prediction. Specifically, we re‐examine the regime‐dependent feature of univariate predictive regression relative to the benchmark. Furthermore, we investigate the prediction mechanism of the momentum‐determined indicator‐switching (MDIS) strategy and validate the significance of market regime information for the MDIS. Our findings demonstrate an overwhelmingly superior ex‐post forecasting performance compared with the MDIS. More notably, our empirical results substantiate that machine learning greatly aids in momentum indicator selection. The results show that the N‐MDIS with machine learning generates more accurate ex‐ante equity premium forecasts than both MDIS strategy and N‐MDIS strategy with logistic regression, yielding statistically and economically significant results. Moreover, our new approach exhibits robust forecasting performance across a series of robustness tests.