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.

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.

As the underlying institutional design for enterprise risk management, it is important to study whether and how internal control affects enterprise technological innovation in the current context of increased economic policy uncertainty. Based on the principal-agent theory and information asymmetry theory, this paper explores the impact of internal control on corporate technological innovation using the listed company data from 2009-2019 CSMAR database, and obtains the following research conclusions: (1) the quality of internal control positively affects the corporate technological innovation. (2) The operational risk plays an intermediary role in the relationship between the internal control affecting the corporate technological innovation. (3) The degree of market competition positively moderates the impact of internal control on enterprise technological innovation. (4) Relative to non-stateowned enterprises and non-high-tech enterprises, the quality of internal control of state-owned enterprises and high-tech enterprises has a more significant impact on enterprise technological innovation. This paper enriches the consequences of the study of internal control on the one hand, and on the other hand, it provides a new perspective for improving technological innovation, and it provides certain guidance for further improving the level of corporate governance.

The integration of digital technology and traditional finance has further achieved the goal of low-cost and sustainable financial services and contributed to high-quality economic development. Based on the data from 31 provinces in China from 2011 to 2021, this paper empirically analyzes the impact of digital finance on the urban-rural income gap. The results show that digital finance can significantly narrow the income gap between urban and rural residents. The quantile test shows that the greater the urban-rural income gap, the stronger the role of digital finance in narrowing the urban-rural income gap. The threshold effect test shows that the narrowing effect of digital finance on the urban-rural income gap will first increase and then weaken with the continuous improvement of the development level. Heterogeneity analysis shows that the secondary indicators of digital finance can significantly narrow the urban-rural income gap, and the effect of depth of usage is the most obvious. Moreover, digital finance has a significantly stronger effect on narrowing the urban-rural income gap in the eastern region than in other regions. Therefore, we should vigorously promote the development of digital finance and give full play to its effectiveness in narrowing the urban-rural income gap.

Based on the panel data of western central-cities from 2002 to 2021, this paper uses the intermediary effect model to analyze the relationship between urban internationalization, innovation ability and urban competitiveness. It is found that: (1) The internationalization of western central-cities has a positive and direct impact on the promotion of urban competitiveness, which has a significant impact on economic strength and infrastructure, and a weak impact on government efficiency and market efficiency The marginal growth effect of internationalization on urban scale will gradually decrease with the improvement of urban development level. (2) The continuous promotion of city internationalization can promote the innovation ability to enhance the competitiveness of the city. Compared with the internationalization of cities, the influence of innovation ability on the competitiveness of cities is small, and the innovation ability of western central-cities and each city is weak. The scale effect has become the main motivation to promote the competitiveness of cities. (3) In the process of internationalization of western central-cities, innovation ability has a part of intermediary effect on the improvement of urban competitiveness, and the effect on urban “hard environment” is significantly greater than that on urban “Soft-environment”. The results of direct and indirect impact tests show that the impact of the process of internationalization of western central-cities on urban competitiveness is exogenous.

Following the seminal paper on Optimum Currency Areas (OCA) by Mundell (1961) a wealth of literature has been published on the business cycles synchronisation and its main determinants. This work provides a systematic review of this research field both at country-level synchronization and regional level. The paper aims to evaluate the contribution of the related literature’s methodologies to the measurement of the business cycle and the estimation of the level of synchronization. The discussion of the collected papers is expected to substantially assist researchers, practitioners, and policy makers keen to employ themselves in the area of business cycles synchronisation.

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.

Hundreds of articles have been written that include empirical estimates of the dis-employment effects of minimum wages; however, many of these articles find statistically insignificant effects, some find significant negative effects, and a few find significant positive effects. Most of these studies use multivariate analyses which can be criticized for omitting key variables. The omitted variables problem ruins all statistics and estimates. This paper uses reiterative truncated projected least squares (RTPLS), a solution to the omitted variables problem, to estimate the percentage increase in unemployment due to a one percent increase in the real minimum wage using monthly data for the 50 states of the USA from 1987 to 2021. RTPLS produces a separate elasticity for every observation where differences in these estimates are due to omitted variables. We argue that RTPLS solves most of the econometric problems that David Neumark identified in his keynote address at a minimum wage conference in Berlin in 2018. We find that the percentage change in the unemployment rate due to a one percent change in the minimum wage ranges between 1.156 and 3.389, that the elasticities for different states tend to move together over time, and that all these elasticities are statistically significant at a 95 percent confidence level.

The study explores online consumers’ impulse buying intentions and behaviors on live streaming platforms. Unlike traditional shopping modes, the development of real-time video streaming provides online consumers with a distinct approach to interacting with live streamers and browsing online products in real-time, potentially causing their impulse buying intentions. To understand online consumers’ impulse buying intentions and behaviors, the paper establishes the research model based on the theory of planned behavior (TPB) model and analyses influencing factors from attitude, subject norm and perceived control aspects. Through the data analysis based on the partial least squares path modelling and variance-based structural equation modelling (PLS-SEM), the research results show that, three factors positively affect online consumers’ impulse buying intentions and lead to their final behaviors. Meanwhile, control variables, including gender, age, and income level, demonstrate insignificant effects across the model. Unlike existing literature, the current study displays the distinct features of live streaming platforms and discovers online consumers’ impulse buying intention based on the TPB model. The results are helpful for related scholars and departments to pay more attention to the live shopping environment and understand online consumers’ impulse buying issues.

Major economic and financial contractions usually go hand-in-hand with muted inflation. This has been true for the Great Depression, the Global Financial Crisis, as well as the Covid-19 crisis. In this paper, we theoretically highlight and discuss the evolution of instruments and approaches monetary-policy decision makers at the Federal Reserve have in lifting inflation to desired levels in times of the zero-lower bound, paying particular focus on more rigorous developments like asymmetric average inflation and temporary price-level targeting.

China's carbon emission trading market has been formally established, but few studies have been conducted to analyze the impact of this policy on the regional urbanization level. Therefore, this paper evaluates whether the carbon trading pilot policy can enhance the regional urbanization level in China through the difference-in-differences method and analyzes the mediating role of industrial structure upgrading in this process. The results prove that the carbon trading market policy can accelerate the transformation and upgrading of industrial structure in the region so that it promotes the development of regional urbanization. Moreover, the effects of the policy are concentratedly manifested in the eastern region of China.

The manufacturing sector must incorporate digitalization and greening as crucial strategies to attain high level of development in the context of the digital economy, given its significant role in quality of environment. Most of existing studies explore net effect of a single or several factors on the digitalization or greening in the manufacturing industry. Limited investigation has been done to integrate various factors impacting the connection and synchronized advancement of digitalization and greening within the manufacturing. Factors affecting the synergy of digitalization and green change within manufacturing are recognized in this article, and a theoretical framework is established to analyze. FsQCA tool is also put into use to survey configurational cases, which contains synergy degree of green digitalization from 30 provinces’ manufacturing in China. Results show that: (1) Between 2007 and 2020, the development level of digital-green integration in the manufacturing industry shows an upward trend. There are three types of collaborative modes, namely, "industrial robot driven" "market development driven" and "market development driven - industrial robot driven", with each mode consisting of at least one to four equivalent pathways. (2) The marketization level and application of industrial robots are the important external and internal pulling forces that drive the synergy degree of digitalization and green change within manufacturing industry, respectively.(3) The influencing factors of the collaborative mode choice of digital-green development within manufacturing industry are complementary, among which, the degree of marketization is similar to the role of industrial robots in the configuration, which are capable to weaken the negative impact from insufficient government R&D investment support under specific conditions; (4) The non-high collaborative state of digital green in manufacturing industry is mainly due to the low level of marketization, openness, and economic development, as well as the low application rate of industrial robots and software products. This study enriches the theoretical understanding on digitalization and green change of manufacturing industry. Meanwhile, it also provides insights to advance the integrated and synergistic advancement of digital green transformation in manufacturing practice.

The answer to how to stop economic growth must be political rather than economic. Economic growth made by men has an impetus which can only be stopped by a deliberate choice. It takes national and international decisions to stop it. In order to organize a moratorium on economic growth (which eventually must lead to a stop on growth) I suggest to create blocks of nations in the world. The European Union can function as a model for such a block. The E.U. is on its way of becoming a supra-national government which was created to ensure a level playing field for its members. Now it is meant to create economic growth for its members, in the future its administration should be used to stop it. Blocks should agree on the targets to reach a durable economy. Globally or between certain more advanced blocks a date should be set to stop economic growth after that date. Each block should implement the goals to reach a durable economy and prevent products reaching its market that violate the goals of durability. Blocks in developing countries cannot be expected to stop economic growth. They need that growth for two reasons. Eventually they must reach the level of existence in the more advanced block and secondly they must have the time to improve the standard for durability of their products. They need the aid of the developed countries to reach a higher but also more durable level of production, but this aid comes with a quid pro quo, In the meantime they must do their utmost to stop wars in their regions and conduct a responsible population-policy to reduce the growth of their population and let it reduce on the long term.

The No.1 document of the Central Government of China has been on the theme of "agriculture, rural areas and farmers" for nearly ten years, placing the solution of "agriculture, rural areas and farmers" at the forefront of the Chinese government's work. Located in the Central Plains, Henan Province in China is one of the major agricultural provinces in China and plays an extremely important role in the field of agricultural production in China. This paper uses an empirical approach and the DEA-Malquist model to study the efficiency of rural public financial inputs to rural revitalisation in Henan Province. The results show that due to different levels of regional economic development, the efficiency of rural public finance investment in Henan Province in promoting rural revitalisation varies, and there is a phenomenon of unreasonable allocation of funds. At the same time, the region's agricultural base also has a greater impact on the efficiency of financial investment. Therefore, Henan Province should adjust measures according to the different regional economic levels and agricultural bases to improve the efficiency of the use of fiscal funds.

The goal of this paper is to provide an overall presentation of corruption as occupational fraud building on the findings of an empirical study conducted in Greece. As uncertainty leads to increased levels of corruption, Greece was chosen due to the prolonged period of uncertainty the country faces, caused by the 2010 financial crisis and followed by the Covid pandemic crisis. 400 questionnaires reviewing corruption perception were gathered and analyzed by gender, age, marital status, education level, position, occupation, and monthly income, as well as urban vs rural area of residence. The findings of the study were compared with and critically evaluated against those of the previous studies for Greece. Respondents in age group 36-49 believe that if appropriate measures are taken it is possible to tackle corruption in the public sector, in contrast to people aged 18-35 who disagree completely. Residents of rural areas do not consider corruption to be a serious problem and are more willing to whistleblowing (in contrast to residents of urban areas). The study findings have important practical implications in the context of designing effective corruption reduction policies.

Population, as a fundamental element in urban development, often reflects a city's economic development pattern through its spatial distribution and dynamic changes. Studying population spatial distribution is pivotal for bolstering the economic activity capacity in urban agglomerations and guiding regional economic health. Using the Changsha-Zhuzhou-Xiangtan urban agglomeration as a case study, this paper analyzes its overall spatial structure and the spatiotemporal evolution of population at the district and county levels. This analysis utilizes population density, population redistribution index, and population geographic concentration as key indices. Additionally, a spatial econometric model is constructed to assess the impact of economic, social, and environmental factors on population spatial patterns. Findings reveal several key points: (1) Furong District serves as the primary central area, boasting a population geographic concentration of 25.1% in 2021. Tianxin District, Kaifu District, Yuhua District, Shifeng District, Yuelu District, and Hetang District constitute the secondary central areas, while Yutang District, Tianyuan District, Lusong District, Yuhu District, Wangcheng District, and Changsha County form the tertiary level areas. (2) Population density within the Changsha-Zhuzhou-Xiangtan urban agglomeration gradually decreases from Furong District outward. The first central area and sub-central areas experience increasing population density, highlighting a polarization trend in the population distribution. (3) The overall Moran's index for the spatial distribution of population in the Changsha-Zhuzhou-Xiangtan urban agglomeration is significantly positive, indicating a strong spatial autocorrelation and a deepening spatial agglomeration of population distribution. (4) Per capita disposable income, financial expenditure, and education level positively influence the geographical concentration of population in the urban agglomeration, while GDP per capita, road area per capita, and environmental quality exert a negative impact. Notably, the most influential factors shaping population spatial distribution are GDP per capita, disposable income per capita, and air quality.

Machine learning is playing an increasingly important role in the healthcare industry by transforming the way cancer is diagnosed and treated. By analyzing patient data, genomic data, and imaging data, machine learning algorithms can identify molecular signatures that distinguish cancer patients from healthy patients. Biomarkers that can accurately detect and diagnose cancer can be identified through analysis of these data sources. Additionally, personalized cancer therapies can be developed by identifying the most effective treatments based on individual patient characteristics and cancer type. Some of the machine learning techniques used for cancer biomarker discovery include deep learning and support vector machines, which can respectively identify complex patterns in data and classify data to identify relevant biomarkers. The benefits of using machine learning for cancer biomarker discovery are significant, including more precise and personalized treatments, improved patient outcomes, and the potential to transform cancer diagnosis and treatment. However, there are also challenges associated with using machine learning for cancer biomarker discovery, such as data collection and privacy issues, as well as the need for more powerful computational resources. This article explores the potential of machine learning in cancer biomarker discovery and argues that ongoing research in this field has the potential to revolutionize cancer diagnosis and treatment. Future research directions should focus on further developing machine learning algorithms and effective data collection and privacy protocols.

Traditionally, conditions of sustainability of the public debt have long been related quite exclusively to fiscal policy and to budgetary parameters. However, the interaction between fiscal and monetary policies regarding the fixation of the interest rate is fundamental. Indeed, a simple analytical modelling shows that if the nominal interest rate increases exponentially with the public debt, because of a default (credit) risk premium, if the confidence of investors is fundamental, conditions of sustainability of the public debt could be much more difficult to comply with. Indeed, if the interest rate is risk-free, values for which the public debt can be sustainable are less constraining if the long-term GDP growth rate is high, or if the long-term risk-free nominal interest rate is small. They are also less constraining if the country decides to turn to a non-negligible primary budget surplus in case of a high public debt. However, if the interest rate exponentially increases with the public debt level, in case of a significant importance of the default (credit) risk premium, these parameters have very limited consequences on sustainable and equilibrium public debt levels. The sustainable public debt that a government should target is then much smaller than in absence of this risk premium.

Digitalization is nowadays one of the fastest developing processes. The adoption of digital technologies can provide innumerous opportunities for the organizations to evolve and gain competitive advantage by leveraging of technologies to respond to dynamic expectations and demands. Information about the country digitalization level is essential to decision makers in both public and private organizations. It can present insights on which areas need the most investment, and furthermore to gain feedback on the outcomes of these investments. For companies, it can create a certain predictability on which products and services will be required the most. To assess the evolution of this process in different countries, various indexes were proposed and employed by different corporations. The Digital Economy and Society Index (DESI) and ICT Development Index (IDI) are used by public organizations. Digitalization Index (DiGiX) and Cisco Digital Readiness Index (CISCO) are used by private companies. Comparing two sectors highlights the most common factors of digital evaluation. Indexes are analyzed thoroughly by their structure, coverage, weights, methodology and ranking. The result of the practical work is a equivalence table which shows the percentage of their similarity. Additionally, a new digitalization index is proposed, based on the result of the previous comparison, which can be applied to analyze both public and private sector of the country’s digitalization level.

The study investigates the impact of COVID-19 on African exchange rates, employing a panel dataset over the period 2016-2022 from 13 African economies and employing pooled OLS estimator. The findings reveal a noteworthy positive correlation between COVID-19 levels and exchange rates within the region. However, this association necessitates deeper exploration due to its multifaceted implications, including economic disruptions, government interventions, global market interconnections, regional stability perceptions, and investor behavior. Economic indicators, such as GDP, inflation rate, real interest rate, and unemployment rate, significantly influence exchange rates. While GDP positively impacts exchange rates through economic growth, investor confidence, and trade strength, controlled inflation fosters investor confidence and export competitiveness, positively affecting exchange rates. Moreover, a pronounced positive impact of the real interest rate indicates economic stability, attracting investment and contributing to currency appreciation. Conversely, high unemployment negatively affects exchange rates, signaling economic weaknesses and eroding investor confidence. These findings emphasize nuanced policy responses for managing health crisis-induced economic disruptions and suggest future research directions focusing on specific country-level analyses and multifaceted factors influencing currency valuations within African economies.