This paper investigates the effects of an unprecedented education policy called ‘double reduction’ (DR) on students’ performance, with evidence from China. One of the significant changes brought about by this policy is the transition of examination scores from a numerical system to a grading system. We found a junior middle school in a small town in Guangzhou that met the requirements for a natural experiment, and we used such unique data to examine three hypotheses. After the implementation of DR, the ‘rat race’ reflected on the students’ grade is reduced. To examine heterogeneity, we apply quantile regression showing that the high-ability students exert less efforts on the ‘rat race’ than low-ability students. Meanwhile, we also provide possible explanations in terms of this phenomenon. Overall, this study not only does it assess the grading transformation that DR policy induced but also fills the gap in the field of DR from an economic perspective.

Exiting literature and central banks have recently raised concerns about the ominous overlooked threat of growing global debt, and its fundamental role in financial crises and economic instability. This paper develops and models an alternative economic theory that calls for a paradigm shift from the prevailing debt-based financial economy. Through a structured literature review and conceptual analysis, the paper explores the relationship between the traditional interest-based financial economy and economic injustice and instability. The paper then through an econometric analysis of data collected from 189 countries tests its conceptualised theory advocating a gradual transition from the deb-based financial economy. The results provide three important contributions. Firstly, the results contribute to the field of economics and finance by providing a conceptual and theoretical framework for examining the interaction between the financial economy and economic equity. The paper introduces a conceptualisation of what it coins as the Equitable Optimality Economic Theory, which replaces interest rate with profit rate. Secondly, the paper extends the debate in the literature about debt theories and its impact on the financial economy by arguing that a shift towards debt-balanced and eventually debt-free financial economy provides a more equitable and stable economic system. Thirdly, the econometric modelling demonstrates that such a change would lead to improved economic growth, reduced debt levels, enhanced financial stability, and the inherent nature of profit rates provides a natural buffer against economic downturns. The study concludes by proposing a practical roadmap for this economic transition in gradual and steady approach.

Forecasting stock prices remains a fundamental yet complex challenge in financial economics due to the nonlinearity, volatility, and exogenous shocks characterizing market behavior. This paper proposes a hybrid deep learning framework that integrates Long Short-Term Memory (LSTM) networks for time-series modeling with Transformer-based architectures for textual sentiment extraction from financial news. The goal is to enhance predictive accuracy by combining structured historical data with unstructured semantic signals. Using three years of daily data from Apple Inc. (AAPL), the model captures endogenous price dynamics via LSTM and incorporates contemporaneous market sentiment through FinBERT, a Transformer model pretrained on financial text. Empirical results show that the hybrid model outperforms price-only baselines across multiple evaluation metrics, including mean squared error (MSE) and directional accuracy. The incorporation of sentiment features proves particularly valuable around earnings announcements and event-driven volatility regimes. This study contributes to the literature on machine learning in finance by demonstrating the complementary strengths of multimodal learning, offering a more interpretable and robust framework for stock price prediction. The findings also open avenues for future research in real-time forecasting, reinforcement learning integration, and the application of hybrid models across diverse asset classes.

Fintech has revolutionized financial service models, but can it effectively address enterprise financing challenges and provide critical support for technological innovation—a cornerstone of new quality productive forces? Leveraging data from A-share listed companies (2011–2023), this study constructs a regional fintech development index based on the distribution of fintech firms across China and evaluates its impact on enterprise-level productive forces. The findings demonstrate that fintech significantly fosters new productive forces, a conclusion robust to instrumental variables, Heckman two-stage analysis, and alternative variable tests. Mechanism analysis reveals three key channels: mitigating information asymmetry, alleviating collateral constraints, and enhancing market competition. Heterogeneity analysis further indicates that fintech’s impact is more pronounced among private enterprises, high-tech industries, and firms in eastern regions. As China prioritizes the development of new productive forces, sustained fintech advancement, deeper "data empowerment," and targeted policy measures are essential to ensure finance effectively serves the real economy and underpins high-quality economic growth.

In this study, I analyze the impact of the aggregate, income, corporate and social security tax revenues on both the U.S. output and the stock market return in a structural vector autoregression (SVAR) framework between 1960:Q1 and 2015:Q4. Unlike some of the other studies, I use aggregate and disaggregated tax revenue variables to examine the impact of scal policy. Results show that an increase in aggregate tax revenue reduces output and market return. In addition, an increase in income, corporate, and social security tax revenues reduces output and the market return signi cantly at varying degrees.

We theoretically examine whether a US-Japan trade agreement would be effective as a buffer against the US-China trade war. There are two ways to mitigate the negative impacts of US protectionism: a “domestic pressure effect to suppress external pressure” and a “free trade agreement (FTA) effect. The former is an effect arising from Japanese protectionist political pressure to suppress the negative impacts of US tariff increases on Japan's political support function. The latter is an effect of the Japan-US free trade agreement. In the absence of a US–Japan trade agreement, both Japan and China’s political support functions decrease when the US raises tariffs. By contrast, when an FTA is agreed between Japan and the US, an increase in US tariffs causes the Chinese political support function to decrease, while the Japanese political support function increases. The US–Japan trade agreement thus plays an effective role as a buffer in mitigating the negative impacts of the US–China trade war on Japan.

In response to the twin environmental crises of climate change and resource depletion, the circular economy has emerged as a key strategy to reduce waste, minimize the extraction of virgin materials, and promote reuse, repair, and recycling. However, the circular economy, as currently conceived, does not inherently support continuous economic growth in the conventional sense. At the same time, modern societies — grounded in ideals of progress, innovation, and rising living standards — rely on economic growth to sustain social stability, employment, and investment. In this paper, Focardi and Fabozzi argue that integrating qualitative economic growth with circularity offers a viable pathway to decoupling economic progress from the depletion of natural resources. Qualitative growth refers to economic progress driven by increased quality, functionality, and complexity of products, services, infrastructure, and institutions rather than the sheer output volume. To illustrate how qualitative growth and circularity can complement each other in practice, the paper highlights initiatives in Ljubljana, Slovenia, Vitoria-Gasteiz, Spain, and Amsterdam, Netherlands, where circular economy strategies have been combined with urban greening, infrastructure regeneration, and resource-efficient policies to enhance both environmental sustainability and quality of life.

This research paper investigates the policy impact of tariff, interest rate, and political stability on balance of payments among three income groups classified by the World Bank. The global panel data is taken from the World Bank from 2002 to 2022 and divided into three groups based on the classification on per capita income into High Income, Upper middle income, and Lower middle-income countries. This study explores how these variables impact balance of payments in different economic conditions and if their impact is different. The study employed in this paper is the Panel Least Squares, Fixed Effects Regression, GMM and Dumitrescu panel granger causality tests. The findings do confirm that these variables indeed have unlike impacts in the selected groups and the importance of tariff and interest rate as a policy tool cannot be sidelined. This research contributes to a deeper understanding of the complex relationship between macroeconomic variables within each income group also highlighting the importance of political stability for economic outlook.

The goal of this paper is to work out a poststratification method for estimating in-time indicators for international end-to-end delivery processes when

• collected data cannot cover all the strata making up the logistics universe to be surveyed,
• the true weights of the strata - needed to correct biases in representativeness caused by disproportionate sampling and incomplete coverage - are unknown but can be inferred from marginal subtotals related to stratification criteria considered separately, rather than jointly, and conditional on each end of the delivery journey: outbound- versus inbound-specific. Within this perspective, poststratification is used here to mean a statistical correction of measurements derived from incomplete stratified samples, an ex-post calibration aimed at yielding more accurate estimates based on an analysis of the data. Thus, we tackle instances where ex-ante assignment to strata is not a problem, but when surveying all strata is out of the question.

For that purpose, an econometric model is designed

• to link the discrete transport lead times, counted in days, of tested items to the specifics of their material characteristics (e.g. size/weight), as well as those of the routes they take through the distribution network (e.g. origin and destination zones),
• and provide performance predictions for each of the strata, covered as well as non-covered.

Benchmarking the multinomial cumulative logit regression against the negative binomial one reveals that delivery time had better be treated as an ordinal categorical system’s response, rather than as a ratio-scaled count.

The model-based fitted and extrapolated estimates are then used as inputs to the ex-post weighting stage, which produces robust point- and interval-estimates of aggregate key performance indicators (KPIs) through bootstrapping. Simple linear programs provide two extreme weighting sets, one per country-to-country path: the first minimizes the KPIs’ values, while the second maximizes them. 

Probabilities of delivery within deadlines summarize distributions of delivery times better than their means and standard deviations, because logistical efforts to cut transit by one day must be enhanced more and more as it gets shortened. Three types of graphs are proposed to help visualize this exponential increase in the service quality required. The applicability of the methodology developed is demonstrated on the 2023 database of the International Post Corporation. In this case, the imprecision of the KPI estimates depends much more on the uncertainty caused by disturbances occurring during the first- and last-miles, than on the imperfection of the information about the real weights of the strata. 

This paper presents a model of bank runs and evaluates relevant policy tools. The model is founded on the historical pattern of banking panics, involving an economic boom, an adverse shock, prominent bank failures, and runs on both insolvent and solvent banks. The model analyzes various ways in which solvency information affects the likelihood of systemwide bank runs. An interesting result is that partial bank-specific information can be worse than no bank-specific information. The model can also explain runs driven by liquidity concern based on incomplete solvency information. The main policy implication derived from the model and the evaluation of policy tools is that policy actions to contain a financial crisis should incorporate weeding out insolvent institutions and assuring the solvency of remaining institutions.