Part of the present inflation is caused by the breakdown of globalization, in particular supply chains, part is caused by the Corona Pandemic, in particular lockdowns, part is caused by the Ukrainian War, part is caused by European sanctions, and part – and not the smallest one – is caused by the European Central Bank’s printing money by hook or by crook in the past and in the presence. This paper attributes inflation decisively to the overwhelming money creation by the European Central Bank.

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 study delves into the intricate interconnections among the financial sector, macroeconomic dynamics, and the advancement of sustainable energy production in South Asia. Utilizing data from 1987 to 2022 in Bangladesh, India, Pakistan, and Sri Lanka, the research employs the Structural Vector Autoregressive (SVAR) method to analyze financial market responses. The findings reveal nuanced insights into the complex relationship between economic and environmental factors, fostering a comprehensive understanding of the region's intricate nexus between financial markets and sustainable energy initiatives. In the Economic Growth model, the negative coefficient for CO2 emissions underscores environmental concerns, indicating an inverse relationship with economic growth. Conversely, positive coefficients for non-fossil energy production, Gross Fixed Capital Formation, stock exchange value, and domestic credit emphasize their positive impact on economic growth. A positive GDP coefficient in the CO2 emission model signifies heightened carbon emissions with economic growth, while a negative coefficient for non-fossil energy production suggests potential environmental benefits. The nearly unitary positive coefficient for Gross Fixed Capital Formation highlights its significant role in driving carbon emissions. The Non-Fossil Energy Production model reveals an inverse relationship between GDP and clean energy, suggesting a potential reliance on fossil fuels during economic growth. Positive coefficients for CO2 emissions, Gross Fixed Capital Formation, stock exchange value, and domestic credit highlight factors influencing non-fossil energy production. Long-run stable coefficients affirm variable significance, while a negative error correction term suggests convergence in developing countries. These models offer nuanced insights into the intricate relationships among economic, environmental, and energy variables, guiding sustainable development policies in South Asia.

Bank deposit is closely related to systemic risks. In addition, considering that resident deposits in China have significant seasonal characteristics, this paper focuses on which component of deposits drives the systemic risk volatility, that is, it can supplement the existing forecast information. We use X-13ARIMA-SEATS to decompose deposit into three subsequences. The research findings show that the forecast effect of subsequence models is better than that of benchmark series. Most importantly, the model with trend component has the best forecast performance.

In reality firms most often face negatively sloped demand curves. Then, for a given level of consumers’ surplus, levies on prices yield higher fiscal revenues than specific duties. Therefore, according to the prevailing view, the switch from unit to ad valorem taxation is supposed to generate more welfare; some even speak of an associated Pareto-improvement. However, this is not true because taxing prices merely transfers profits to the Treasury, while total rent remains unaffected. Since excise duties diminish the welfare gain in comparison with untaxed trade, an appropriately designed income tax allows all parties to benefit. Sales should be taxed only exceptionally.

This paper provides an overview of the existing literature on the use of artificial intelligence (AI) in various fields, including economics, finance, mining, manufacturing, and innovation. The paper identifies the drivers and effects of AI deployment in the context of innovation and highlights the challenges and opportunities that arise from the use of AI. The studies reviewed in this paper cover various topics related to forecasting, including the impact of AI on professional skills, hybrid forecasting techniques for predicting commodity prices, and novel deep reinforcement learning algorithms for crude oil price forecasting. The paper’s contribution lies in its systematic and comprehensive approach to reviewing the literature, which allows for a better understanding of the impact of AI on various fields and the identification of strategies to address the challenges that arise from its deployment.

Previous literature shows that the price-discovery ability of options market varies substantially over time. Using data of Shanghai Stock Exchange 50 exchange-traded fund options, this paper shows that options prices contribute relatively less to price discovery during low-sentiment periods, but the price-discovery ability of options market remains unchanged during high-sentiment periods. These results suggest that change in aggregate investor sentiment is an important source of the time variation in options’ price discovery ability. Moreover, the options market experiences greater bid-ask spreads when investor sentiment is lower, supporting a “transaction costs mechanism.” This paper fulfills related literature on the time variation in options’ price-discovery ability.

Under the background of digital economy, global tax governance has become one of the important topics in today's world development and change. In recent years, new challenges, such as tax base erosion and profit transfer, and the imbalance of cross-border tax source division, have become prominent, forcing the reform of international tax rules and the reshaping of international tax order. Based on this, the article first deeply digs into the scale and cross-border characteristics of the spatial-temporal aggregation of the digital economy, and comprehensively evaluates its impact on the global tax governance pattern. After that, the article combs out the realistic picture and challenges of the current digital tax reform from the aspects of theoretical basis, basic principles and governance mechanism. Finally, based on China's basic national conditions, the article puts forward some institutional ideas and policy suggestions, with a view to improving China's participation, voice and influence in global tax governance, and also enabling governments, enterprises and residents of various countries to enjoy the fruits of world economic development fairly and promote the construction of Community of Shared Future for Mankind.

As the COVID-19 epidemic swept the world in early 2020, it is worth watching how it affects the total factor productivity of enterprises. Based on the theory of information asymmetry and the data of A-share listed companies from 2014 to 2021, this paper conducts an empirical study on the relationship between COVID-19 and the total factor productivity of enterprises. The results show that the impact of the COVID-19 epidemic can significantly reduce the total factor productivity of enterprises, and the conclusion is still true after a series of robustness tests. Taking the financing constraint as an intervening variable, it clearly reveals a transmission mechanism, that is to say, the impact of the COVID-19 epidemic exacerbates the financing constraint of the enterprise, thus it makes the total factor productivity of the enterprise reduce. It is further found that the above effects are more obvious for small and medium-sized enterprises and non-intelligent enterprises. Based on this, the government should implement some active economic development policies to create a free, relaxed and orderly market environment for enterprise financing. At the same time, enterprises should not only pay attention to digital and intelligent development, and transform into technology-intensive enterprises, but also strengthen the awareness of worry. Enterprises should store "more grain" for preparing for the cold winter.

Educational equity is the foundation of social equity, and education equalization is one of the serious issues to be solved by the government. This work considers one education equalization reform conducted by Suzhou Industrial Park of China, namely, the comprehensive "five to nine" (CFTN) housing purchase admission qualification adjustment policy, as an example and uses the resold housing transaction data to identify the effect of the equalization policy by the difference-in-differences (DID) method. Based on this natural experiment, the quantitative assessment results show that (1) the CFTN policy has a significantly positive effect on housing prices in nine-year consistent (NYC) school districts that belong to high-quality school districts; (2) the CFTN policy has no significant effect on housing prices in non-NYC consistent school districts that belong to ordinary school districts. These results uncover that the one-size-fits-all CFTN policy can only obtain significant housing price equilibrium effects in school districts above a certain level of education quality. For regions with backward education levels, the policy is ineffective. For these regions, continuously increasing high-quality educational resources and increasing educational service levels are essential solutions to reduce educational inequality.

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.

Based on provincial panel data from 1998-2018, this paper estimates research and development (R&D) factors, and a stochastic frontier analysis (SFA) model is constructed to examine the effects of R&D factors on regional total factor productivity (TFP). The results show that both R&D capital stock and R&D personnel can significantly promote regional TFP, but the productivity-enhancing effect of R&D factors is different between regions. Specifically, R&D capital and R&D personnel can promote TFP in eastern and central provinces, and the promotion effect is not significant in western provinces. In addition, compared with investment-driven regions, innovation-driven regions are more likely to enhance TFP by R&D factors.

This study investigates the impact of inventor CEOs on firm performance using a manually collected database of firms' inventors. Our findings reveal that, on average, firms with inventor CEOs experience a one- and two-percentage-point increase in ROA and ROE, respectively, compared to firms with noninventor CEOs. To address potential endogeneity issues, we employ turnover analysis, an instrumental variable approach, and propensity score matching. The estimation results suggest that inventor CEOs significantly enhance firm performance by fostering innovation and total factor productivity. This research contributes novel evidence on the relationship between inventor CEOs and firm performance.

In developing countries, the dependence on traditional biomass for domestic energy consumption is one of the major causes of deforestation and environmental poverty. This paper investigates the impact of women’s education on the probability of using Unsustainable Biomass Cooking Technologies (UBCT) as the household main fuel of cooking instead of clean energy. Combining data from four rounds of Nigeria DHS conducted between 2003 and 2018, we take advantage of a natural experiment, the implementation of Uni-versal Primary Education policies in 1970, to obtain an exogenous variation in women’s education levels. Using an instrumental variable approach to control for the endogeneity issues, we find that women with higher levels of education are less likely to use UBCT as their primary source of cooking. This result implies that environmental policies in developing countries should rely not only on income transfers policies as suggested in the literature but also on increasing women's human capital to reduce women’s dependence on unsustainable biomass cooking practices.

This paper investigates the degree of inflation persistence across regions in Portugal by analyzing the Consumer Price Index (CPI) growth rates for NUTS II regions. The study employs the Augmented Dickey-Fuller (ADF) test to determine whether the CPI data for Portugal is stationary or non-stationary. The results of the ADF test reveal that the IPC data for Portugal is non-stationary, indicating that inflation exhibits persistence in the long run. The study further assesses the persistence of inflation by estimating an autoregressive integrated moving average (ARIMA) model for each region. The Ljung-Box test is used to test for autocorrelation in the time series data, and the Hurst exponent is calculated to evaluate the presence of long-term memory in the time series data. The study finds that there is significant autocorrelation in the time series data for all regions, supporting the presence of persistence in inflation at the regional level in Portugal. The Hurst exponent also shows that the time series data for each region exhibits a high degree of persistence in inflation. Finally, the study applies the ARIMA model to each CPI division's data and uses the Ljung-Box test to test for autocorrelation in the time series data. The results show that some CPI divisions exhibited higher levels of persistence compared to others. For example, the "Housing, water, electricity, gas and other fuels" division exhibited high persistence, while the "Communication" division exhibited low persistence. This study contributes to the existing literature by exploring regional inflation persistence in Portugal and its implications for policymaking. The results provide insights into the inflation persistence patterns across regional levels in Portugal, by emphasizing the need to consider regional differences in inflation dynamics when formulating effective policy interventions. Understanding the persistence of inflation is crucial for policymakers to ensure price stability and sustainable economic growth.

Information leakage in the stock market has been widely proven. Information disclosure is sometimes uneven, and there is significant information asymmetry between ordinary investors and professional institutional investors. In this paper, Regression Discontinuity design (RDD) model is first employed to analyze the information leakage issues. Based on the daily closing stock prices of 15 capital service listed companies, we analyze the difference between the market reaction time and the disclosure time of two stamp tax policies. We found that the sample policies information may leaked to the market about two days earlier. This paper provides a new method analyzing information leakage.

A high inflation period of seven years (1978-1985) in Israel, which turned into a hyperinflation, puzzled Israeli economists, who tried to understand its causes and mechanisms. As a result, they provided fourteen different explanations. Although all of the explanations were based on the same data, the researchers’ conclusions were either different or contradictory. This situation provides a virtual laboratory for the study of the ways and methods of economic thought; it is a classical Ceteris patribus situation. This laboratory further raises other subjects and questions. Are there principal differences between the economists' explanations? Is it possible to classify these differences? How do economic explanations for inflation differ from non-economic ones? In this article, I classified thirteen economic studies and one non-economic study into five clusters. This was the methodological tool used to inquire into the economic thought in this virtual laboratory.

This research delves into the temporal dynamics of a nation's pursuit of a targeted GDP per capita level, employing five different survival machine learning models, remarkably Deep Learning algorithm (DeepSurv) and Survival Random Forest. This nuanced perspective moves beyond static evaluations, providing a comprehensive understanding of the developmental processes shaping economic trajectories over time. The economic implications underscore the intricate balance required between calculated risk-taking and strategic vulnerability mitigation. These findings guide policymakers in formulating resilient economic strategies for sustained development and growth amid the complexities inherent in contemporary economic landscapes.

The purpose of this paper is to examine and elucidate the effect of the announcement of winning a US federal contract by pharmaceutical companies, traded on the NASDAQ stock exchange on the share prices. The analysis is based on a sample of 327 event days from 12 pharmaceutical companies listed on the NASDAQ market for a given period (2010 – 2021). The sample is analysed by using the event study methodology and Capital Asset Pricing Model (CAPM) in order to examine the abnormal returns and cumulative abnormal returns during an event window of 10 days before the event and 10 days after the event. While prior researchers have identified significant positive abnormal returns following different types of contract announcements, this study aims to contribute to the existing literature by exploring the specific impact of the public contract in the pharmaceutical market on market shares. By investigating this unique aspect, the research fills a gap in previous studies and enhances our understanding of the relationship between public contract announcements and market performance. The data collection utilises secondary data from the official open data source of US federal spending information and daily stock prices. The collected data is analysed quantitatively using event study methodology, allowing for statistical examination of abnormal returns, such as ANOVA analysis, t-statistic, beta index, time series analysis and linear regression. The research findings indicate a significant impact of public contract-winning announcements on cumulative abnormal returns, suggesting a positive reaction from the market. However, average abnormal returns within the event window were positive but not statistically significant. These results imply that winning a government contract has a limited impact on the stock market. The implications of this research highlight the importance of public contract announcements in the pharmaceutical industry and their influence on share prices. The findings provide valuable insights for investors and stakeholders, aiding decision-making processes related to investment opportunities and market performance.

This paper attempts to introduce an overlapping generations structure into Paul Krugman's "The world's smallest macroeconomic model" (Krugman (1999)) to examine the implications of fiscal policy, particularly fiscal deficits, in a framework suitable for policy analysis. In that paper, Krugman argued that under the price rigidity assumption, a shortage in the money supply leads to underemployment and recession, so increasing the money supply would eliminate underemployment and restore full employment. But how can the money supply be increased? I show that in order to restore full employment out of a recession, a fiscal deficit is needed to increase the money supply. I also show that in a growing economy, fiscal deficits are necessary to maintain full employment at constant prices or inflation. Fiscal deficits are not only effective in pulling the economy out of recession, they are even necessary for growth to continue without recession or inflation. The fiscal deficit in this paper represents the difference between government spending and government revenues. If this difference is positive, we say that the government is in deficit. Krugman's original model is a one-period static model. I intend to extend this model to a dynamic overlapping generations model.