Deposit dollarization in Turkey has been on the rise, reaching record levels in 2022. This was caused by the worsening macroeconomic fundamentals along with the transition to the presidential system in 2018 and the associated deviation from the monetary policy stance afterward. The unique case of Turkey presents an excellent natural experiment to explore the dynamics of deposit dollarization and how it is affected by the changes in the institutional structure as well as various social, economic, and financial shocks in a relatively short time. This study examines the time varying reactions of deposit dollarization for the period between 2013 and 2022 based on rolling window maximum entropy bootstrap estimates. Our findings reveal that all the model variables had significant and sometimes asymmetric effects on deposit dollarization during the different stages of this turbulent period. In particular, we observe diminishing effects of real exchange rate, policy credibility and consumer confidence over time. The findings provide ample new evidence regarding the dynamics of deposit dollarization in general, and how it is affected by loss of monetary policy credibility.

The low-carbon city pilot is an important policy for promoting sustainable development and addressing climate change. The level of green technological innovation is a significant driving force for China's transition to high-quality development and the realization of carbon peaking and carbon neutrality goals. This essay takes the panel data of 30 provinces in China from 2008 to 2020 as the research sample, regards the number of urban green invention patent applications as a quantifiable indicator of the level of urban green technological innovation, and takes the policy of low-carbon city pilot as a quasi-natural experiment, and uses the difference-in-differences model to test the impact of low-carbon city pilot on the level of urban green technological innovation, and to investigate the mechanism of action within. The main conclusions are as follows: (1) The policy of low-carbon city pilot has an outstanding promotion effect on the level of urban technological innovation. (2) Using the mediating effect to analyze, it is found that the low-carbon city pilot policy can improve the level of urban green technological innovation through the development of green finance.

With the continuous advancement of technological revolution and industrial transformation, digital finance supported by big data and artificial intelligence has become an important engine for promoting carbon neutrality. We measured the industrial structure transformation index (ISU) of 30 provinces in China, and discussed the spatial spillover effect and transmission mechanism between digital finance and ISU using the spatial Durbin model. The research results demonstrate that the digital finance development can significantly improve the local ISU. Interestingly, the impact of digital finance on the ISU of adjacent areas has a significantly negative spatial spillover effect, which still exists under the different spatial weight matrix. Digital finance can improve ISU by improving green technology innovation, upgrading industrial structure, and alleviating capital allocation. We also found that the higher degree of marketization and environmental regulation can increase the positive influences of digital finance on ISU. This research proves the effectiveness of the digital finance in improving energy efficiency, and it encourages policymakers around the world to rely on digital finance to promote ecological governance and achieve high-quality economic development.

Based on the provincial panel data of China from 2011 to 2019 for a total of 9 years, this paper uses the entropy method to construct a comprehensive measure of the digital economy and common prosperity. We theoretically analyzed the impact mechanism of the digital economy on common prosperity using the econometric model. The study found that the digital economy can significantly promote the realization of common prosperity. The digital economy has a significant impact on the development and sustainability of common prosperity. The results of the impact mechanism show that the digital economy promotes common prosperity by increasing entrepreneurial activity. Heterogeneity research shows that the role of digital economy in promoting common prosperity changes with different regions. Specifically, the digital economy in the central and western regions can significantly promote the realization of common prosperity, but this impact is not significant in the eastern region. From the regression results of the digital economy, we can see that industrial digitalization and digital infrastructure play a greater role in promoting common prosperity. Economic openness affects the promotion of digital economy to common prosperity. The conclusion of this work provides an important research reference for the development of digital economy and the realization of common prosperity.

The signing of RCEP has created new opportunities for the development of China's export trade, and international logistics capacity is an important factor affecting the conduct of international trade. This paper qualitatively describes and analyzes the LPI index and its sub-indicators of RCEP member countries, and empirically investigates the impact of logistics performance level on China's export trade through an extended gravity model. The study shows that there are large differences in logistics performance levels among RCEP member countries, especially in South Asia, where the quality of logistics infrastructure and customs efficiency are the most lagging behind, which are the key factors pulling down the logistics performance index; the signing of RCEP has effectively promoted China's foreign trade level, but there are great differences in export amounts to different countries, and the logistics performance levels of importing countries and China's export trade are significant positive correlation, the improvement of logistics performance is conducive to promoting China's export trade to RCEP member countries, and from the viewpoint of logistics links, the role of logistics infrastructure quality is the strongest, and the influence of cargo transportation timeliness is the weakest.

Bank capital requirements would entail large social costs if they made resource allocation suboptimal and banking services costly by unduly limiting the banks’ ability to lend. This paper considers three main factors that may make capital requirements relevant, namely, deposit insurance subsidies, stock valuation errors, and tax shields derived from debt financing. The theoretical model analyzes the combined effects of the three factors on the banks’ incentives to make fairly priced loans, which should also be socially optimal loans. A key finding is that the long-term cost of capital requirements is likely to be very small when deposit insurance is underpriced. Increased funding costs resulting from higher capital requirements are absorbed by shareholders of banks, rather than passed on to borrowers. Under some reasonable assumptions, higher capital requirements improve resource allocation by countervailing distortionary effects of deposit insurance subsidies. Short-term adjustment costs can still be large, but it should be relatively easy to mitigate the short-term effects.

This research investigates the determinants of women's empowerment within households in a developing country. The investigation specifically focuses on the impact of employment and fertility, considering constraints imposed by family rules that play a pivotal role in shaping women's empowerment. The theoretical framework outlined in this study posits a simplistic model demonstrating that, within the prevailing family structure and household composition, earned income and fertility may bear significant relevance. Utilizing data extracted from the Pakistan Social and Living Standards Measurement Survey, we engage in estimating the level of female empowerment, operationalized as the extent of female authority over household expenditures. Notably, our empirical approach accounts for the endogeneity of employment and fertility through a two-stage estimation process. This involves leveraging information on family rules—representative of a family's cultural norms—pertaining to work and childbearing. Our empirical findings affirm the viability of family-specific rules as instruments to estimate and subsequently predict employment and fertility. Importantly, the evidence gleaned underscores the critical role of selecting valid threat options in theoretical analyses of women's empowerment.

Electricity price volatility in developing economies, particularly Nigeria, presents a significant challenge to sustainable development. In this paper, we examine the macroeconomic drivers of electricity prices (ELP) in Nigeria based on annual data spanning 1980-2022 and the autoregressive distributed lag (ARDL) procedure. Our model estimates the long-run and short-run impacts of population growth (PG), economic growth (GDP), crude oil price (COPR), and electricity consumption (ELC) on electricity prices (ELP). The empirical findings reveal a positive and long-run effect of PG on ELP, indicating that rising demand from a growing population increases electricity prices. In the short-run, ELC surprisingly co-moved with ELP, which may be attributable to price-sensitive demand within specific consumer segments or periods. Furthermore, GDP and COPR exert positive effects on ELP, indicating that economic growth drives energy consumption and prices, and the cost-driven impact of fossil fuel dependence in electricity generation. These findings shed light on the complex interplay of demographic, economic, and energy market-related forces driving electricity prices in Nigeria. Therefore, the paper proposes some policy suggestions based on the empirical findings.

This paper examines the information content of selected US industries focusing on the dynamic linkages among these industries, the stock market and a number of fundamental variables. The period of investigation spans from January 1960 to December 2021. The empirical strategy includes several methodologies such as regressions, vector autoregressions and volatility models. The idea is to investigate the dynamic linkages among these series at both the mean and the volatility levels. The results point to significant industry returns’ explanatory power for many predictors of economic activity including the stock market. Further, time-varying analysis of the linkages among the industries and the stock market’s returns reveal that certain industries such as Oil and Financials provide consistent information leadership over other industries and across decades. Further, upon assessing the industry–market return volatility spillovers, it was found that a market risk–return profile may not always be economically significant and timely for investors. Finally, crises, financial or otherwise, affect industries but to differing degrees.

The digital transformation provides an opportunity for the development of a green and low-carbon economy. This study used panel data collected from 30 Chinese provinces between 2011 and 2018, and assessed the impact of digitization (Dig) on environmental efficiency (EE). Quantile regression is employed to scrutinize the evolution of the marginal effect. From the perspectives of population and institutional factors, this study empirically investigates nonlinear relationships and potential mechanisms using Hansen threshold and mediation models. The findings reveal several key insights. Overall, levels of digitization and environmental efficiency (EE) are increasing with regional dispersion expansion, indicating a “polarization” characteristic. The impact of digitization on EE exhibits noticeable stage and regional heterogeneity. Analysis of population factors reveals that population structure, population size, and human capital trigger a sharp “marginal increase” of positive effects with single thresholds of 0.8155, 7.2284, and 11.0497, respectively. Analysis of institutional factors highlights the significance of fiscal policy quality (tax proportion), education expenditure, and tax system structure as important intermediaries. Finally, this paper presents corresponding policy implications.

The use of cloud computing by firms can be expected to go hand in hand with higher productivity, more innovations, and lower costs, and, therefore, should be positively related to export activities. Empirical evidence on the link between cloud computing and exports, however, is missing. This paper uses firm level data for manufacturing enterprises from the 27 member countries of the European Union taken from the Flash Eurobarometer 486 survey conducted in February – May 2020 to investigate this link. Applying standard parametric econometric models and a new machine-learning estimator, Kernel-Regularized Least Squares (KRLS), we find that firms which use cloud computing do more often export, do more often export to various destinations all over the world, and do export to more different destinations. The estimated cloud computing premium for extensive margins of exports is statistically highly significant after controlling for firm size, firm age, patents, and country. Furthermore, the size of this premium can be considered to be large. Extensive margins of exports and the use of cloud computing are positively related.

Based on provincial panel data in China, this study is the first to investigate whether industry-university-research collaborative innovation (IURCI) can help to improve factor misallocation. It is found that IURCI can significantly improve capital misallocation and labor misallocation, and the effect has regional differences, which shows that the improvement effect is obvious in areas with factor under-allocation, such as the central and western regions, but not obvious in areas with factor over-allocation, which conforms to the rule of diminishing marginal returns. A regulatory effect model is built to explore the impact of regional heterogeneity, through which we find that after considering three external environmental conditions, including economic development level, academic research level, and marketization degree, the improvement effect of IURCI on factor misallocation undergoes significant changes. The research results show that to deepen the marketization reform of factor allocation, we can start with IURCI. The government should form a sustainable and normalized industry-university-research collaborative innovation ecological mode through pilot cases and adopt measures according to local conditions to ensure the efficient use and reasonable distribution of capital and human resources of enterprises, universities, and scientific research institutions.

Parkinson’s Disease (PD) is a prevalent progressive neurodegenerative condition affecting millions globally. Research has found that individuals with PD have a reduced risk of certain cancers, such as colon, lung, and rectal cancers, but an increased risk of brain cancer. Therefore, there is an urgent need for the development of advanced PD diagnostic methods and for investigating the relationships between risk factors, such as lifestyle due to handedness associated with various types of cancers. Recent ad- vancements in magnetic resonance imaging have enhanced PD diagnosis, reducing misdiagnosis and facilitating more accurate disease progression monitoring. Nevertheless, challenges exist, particularly in the distinction of PD between left-handed and right-handed patients over time. This survey provides an overview of contemporary deep learning-based imag- ing analysis methodologies, encompassing both non-longitudinal and lon- gitudinal contexts. We also explore existing limitations and prospects for refinement to gain deeper insights. These insights are poised to inform the development of personalized treatment strategies for PD patients while elucidating the current disparities between deep learning models and their efficacious implementation in clinical practice.

This paper explores the transformative journey from traditional e-commerce to the emerging realm of virtual commerce (v-commerce) within the metaverse, a collective virtual shared space created by the convergence of virtually enhanced physical reality and physically persistent virtual reality. The metaverse, with its immersive and interactive capabilities, is reshaping the landscape of economic activities, creating a paradigm shift in the way consumers shop, sell, and buy goods and services. The study delves into the impact of virtual reality (VR) technologies on consumer behavior, business models, and the overall economy. It investigates how VR is revolutionizing the shopping experience by offering immersive, personalized, and interactive platforms, thereby influencing con-sumer decision-making processes. The paper further examines the implications of these changes for businesses, particularly in terms of strategy, operations, and customer engagement. Moreover, the paper discusses the challenges and opportunities presented by the metaverse economy, including issues related to privacy, security, digital divide, and regulatory frameworks. It also highlights the potential of v-commerce to drive economic growth, innovation, and sustainability in the digital age. The paper concludes with a discussion on the future directions of v-commerce and the potential strategies for businesses to thrive in this new economic landscape.

The Public Welfare Foundation of a listed company is a non-profit organization with independent legal personality. It is established and funded by a listed company. The purpose of establishing a foundation for a listed company is generally to carry out charitable activities and make important contributions to charitable causes. However, due to the special power separation model, the autonomy of the foundation is poor, and there may be serious agency conflicts, ultimately leading to listed companies becoming beneficiaries of the foundation's charitable activities. We have found from the perspectives of commercial credit, sales, and cooperative relationships that social responsibility initiating organizations (listed companies) can obtain potential benefits from social responsibility fulfilling organizations (listed company public welfare foundations) in supply chain commercial relationships, that is, listed companies that establish public welfare foundations have good supply chain commercial relationships. When a listed company and a public welfare foundation have a close relationship in terms of fund donation, decision-making, business, and naming, the public welfare foundation has a strategic preference in selecting project names, project service areas, project beneficiaries, and project service areas. It prioritizes the interests of the listed company, enabling the listed company to obtain better supply chain business relationships.

Credit scoring remains one of the most important subjects in financial risk management. Although the methods in this field have grown in sophistication, further improvements are necessary. These advances could translate in major gains for financial institutions and other companies that extend credit by diminishing the potential for losses in this process. This research seeks to compare statistical and artificial intelligence (AI) predictors in a credit risk analysis setting, namely the discriminant analysis, the logistic regression (LR), the artificial neural networks (ANNs), and the random forests. In order to perform this comparison, these methods are used to predict the default risk for a sample of companies that engage in trade credit. Pre-processing procedures are established, namely in the form of a proper sampling technique to assure the balance of the sample. Additionally, multicollinearity in the dataset is assessed via an analysis of the variance inflation factors (VIFs), and the presence of multivariate outliers is investigated with an algorithm based on robust Mahalanobis distances (MDs). After seeking the most beneficial architectures and/or settings for each predictor category, the final models are then compared in terms of several relevant key performance indicators (KPIs). The benchmarking analysis revealed that the artificial intelligence methods outperformed the statistical approaches.

In the past, investing in housing has served as an engine of growth for many economies as it is widely recognized that poor housing conditions can have significant negative impacts on human health, education, and economic opportunities. To assess the housing-related quality of life, indicators such as housing quality, housing environment, and cost burdens can be applied. However, recent studies indicate that materials used for construction are critical. Permanent materials, as opposed to temporary materials, typically offer a range of benefits in terms of durability, low maintenance, improved energy efficiency, increased property value, and better safety. The ultimate aim is to identify the key drivers of housing conditions in Cambodia, with a particular focus on materials and water quality, and the legal status of housing. To find the empirical relationship between economic, socio-economic, and demographic variables on the one hand, and variables measuring housing and living conditions in Cambodia on the other, the analysis employs Ordinary Least Squares and Methods-of-Moments regression modeling. Results indicate that high employment rates and entrepreneurship increase home ownership, and improve the quality of drinking water available. Furthermore, in addition to employment and entrepreneurship, the higher-performing construction materials can also be empirically explained by a bigger labor force and variables capturing the wider macroeconomic environment.

A large body of research has been dedicated to the study of relationships between social or cultural capital and educational outcomes in Western countries. However, few studies have examined these associations in a Chinese context, and even fewer have examined the effects of both forms of capital on educational outcomes simultaneously within a familial context in China. This study offers a reformulation of the associations between family social capital and family cultural capital on the educational outcomes of adolescents in both rural and urban China. Using the structural equation modelling approach and the China Education Panel Survey, this study sheds some new insights – the presence of significant compositional differences in both family social capital and family cultural capital between rural and urban Chinese adolescents, and differential effects of both forms of capital on educational outcomes were found. Family social capital presented larger positive effects on the academic effort and educational aspiration of rural adolescents while having no positive effects in facilitating the academic achievement of both rural and urban adolescents. Meanwhile, family cultural capital presented larger positive effects for urban adolescents on all educational outcomes as compared to their rural counterparts.

Influenced by traditional notions of solidarity, when a province’s planning can be highly aligned with that of the central government, the province is perceived to be more collective and thus able to receive financial or resource favors from the central government. This consistency, as is often the case, reflected in doing the same thing as the central government. This situation may lead governors to ignore local economic performance and thus reduce regional innovation efficiency, as in the case of China’s Great Leap Forward. Likewise, it is possible to get better resources (energy or capital) by demonstrating managerial submissiveness, thus improving the regional innovation efficiency. Therefore, to verify the relationship between imitative governance and regional innovation efficiency, we collected relevant data from 31 major provincial administrative units in China, calculated the degree of imitative governance between provincial government work reports and central government work reports through text similarity, as well as utilized the SBM-DEA model to evaluate regional innovation efficiency. Meanwhile, we provide a new explanation of the phenomenon from the perspective of resource inclination. Finally, the empirical results show that imitative governance promotes local innovation efficiency and is moderated by resource inclination.

Public diplomacy is a significant factor in promoting policy communication, people-to-people bonds, and the protection of foreign assets between two nations, but there is little research on the economic worth of public diplomacy. Python was used to crawl the daily search frequency of "China", "African countries", and related terms to construct the heat index of China-Africa public diplomacy during 2011-2019, and a two-way fixed effect model was developed to examine the impact of public diplomacy on China’s outward foreign direct investment (OFDI) in Africa. The empirical results show that public diplomacy has a considerable favorable effect on China’s OFDI in Africa, and after a series of robustness tests, the basic conclusion still holds. Heterogeneity tests show the investment promotion effect of public diplomacy is more pronounced in sub-Saharan Africa and that the promotion mechanism exhibits the characteristics of poverty alleviation and risk aversion. In addition, the promotion effect of public diplomacy on China’s OFDI becomes greater as time goes on. In terms of impact mechanisms, public diplomacy may promote OFDI by enhancing financing capacity and improving national governance. The research conclusion has policy implications for promoting China-Africa public diplomacy and investment cooperation in the new era.