Micro-finance supports mainly informal activities that often have low market demand. It may be thus hypothesized that the aggregate poverty impact of micro-finance in an economy with low economic growth is modest or nonexistent. The observed borrower-level poverty impact is then a result of income redistribution or short-run income generation. The author addresses these questions using household level panel data from Bangladesh. The findings confirm that micro-finance benefits the poorest and has sustained impact in reducing poverty among program participants. It also has positive spillover impact, reducing poverty at the village level. But the effect is more pronounced in reducing extreme rather than moderate poverty.
Japanese finance combines agglutinative, head-final linguistic structure, mixed writing systems, and high-context communication norms that rely on indirect expression and implicit commitment, posing a substantial challenge for LLMs. We introduce Ebisu, a benchmark for native Japanese financial language understanding, comprising two linguistically and culturally grounded, expert-annotated tasks: JF-ICR, which evaluates implicit commitment and refusal recognition in investor-facing Q&A, and JF-TE, which assesses hierarchical extraction and ranking of nested financial terminology from professional disclosures. We evaluate a diverse set of open-source and proprietary LLMs spanning general-purpose, Japanese-adapted, and financial models. Results show that even state-of-the-art systems struggle on both tasks. While increased model scale yields limited improvements, language- and domain-specific adaptation does not reliably improve performance, leaving substantial gaps unresolved. Ebisu provides a focused benchmark for advancing linguistically and culturally grounded financial NLP. All datasets and evaluation scripts are publicly released.
Enhancing the subjective well-being of poor households is crucial for the world’s sustainable development. Using a comprehensive household-level dataset from the China Household Finance Survey (CHFS) spanning 2011 to 2019, this study employed a multi-period difference-in-differences (DID) approach to systematically identify the causal effect and underlying mechanisms of the Targeted Poverty Alleviation (TPA) program on the subjective well-being of poverty-stricken households. Then it explored the heterogeneous effects of different assistance measures on their subjective well-being. We found that the TPA program significantly improves the subjective well-being of rural poor households after a series of robustness checks. The analysis indicated that the TPA program improves the happiness of poor households by reducing their relative poverty and promoting their labor participation to eliminate poverty. We found that providing basic public services, means of agricultural production, and communication infrastructure all enhance the positive impact of TPA on happiness, while the housing relocation program, transportation infrastructure investment, and agricultural technical support do not. The conclusions of this study have important policy implications for ensuring equitable access to basic public services, consolidating the effective link between poverty alleviation achievements and rural revitalization in the post-poverty era, thereby promoting the common prosperity of rural households.
Hallucination remains a critical challenge for deploying Large Language Models (LLMs) in finance. Accurate extraction and precise calculation from tabular data are essential for reliable financial analysis, since even minor numerical errors can undermine decision-making and regulatory compliance. Financial applications have unique requirements, often relying on context-dependent, numerical, and proprietary tabular data that existing hallucination benchmarks rarely capture. In this study, we develop a rigorous and scalable framework for evaluating intrinsic hallucinations in financial LLMs, conceptualized as a context-aware masked span prediction task over real-world financial documents. Our main contributions are: (1) a novel, automated dataset creation paradigm using a masking strategy; (2) a new hallucination evaluation dataset derived from S&P 500 annual reports; and (3) a comprehensive evaluation of intrinsic hallucination patterns in state-of-the-art LLMs on financial tabular data. Our work provides a robust methodology for in-house LLM evaluation and serves as a critical step toward building more trustworthy and reliable financial Generative AI systems.
Cultural tourism is important for preserving cultural history and giving visitors immersive experiences, but tailoring it to each visitor's needs is still a major problem. It offers a distinct method of improving cultural tourism by combining Virtual Reality (VR), Genetic Algorithm (GA), and individual customization. Premature convergence and inadequate population variety are addressed by the Dynamic variety-Enhanced Genetic Algorithm (DDE-GA), a variation of the conventional GA. DDE-GA improves the investigation of possible solutions by dynamically modifying selection pressure according to population diversity, it makes it particularly useful for tackling optimization problems that are complicated, multi-modal, and highly dimensional. Creating an immersive environment that enables visitors to experience cultural heritage in a manner that is entirely tailored to their preferences, interests, and schedules is the objective of virtual reality technology. By adjusting to these individual parameters, the algorithm cleverly optimizes tourist itineraries. The DDE-GA-powered VR system works better than current methods, according to experimental data, with improvements in reaction time (1.1 s), accuracy (98 %), precision (97 %), and modeling error (0.10). When compared to convolutional algorithms, the suggested approach specifically enhances accuracy and drastically lowers error. This invention assists not only in satisfying tourists with individualized experiences but also in popularizing and preserving cultural traditions via the use of modern technology. The research concludes that integrating DDE-GA with VR technology substantially enhances personalized cultural tourism by optimizing routes based on user-specific preferences. This approach yields notable improvements in accuracy, precision, and response time while minimizing modeling errors. Furthermore, it contributes to both enriching tourist experiences and advancing cultural heritage conservation through innovative technological applications.
Information technology, Electronic computers. Computer science
The global acceleration of population urbanization has transformed cities into primary spatial hubs of human activity. As urban populations continue to expand, identifying the socioeconomic drivers of urbanization and elucidating their underlying mechanisms are essential for achieving Sustainable Development Goal 11, established by the United Nations. This study leverages machine learning and big data to investigate the determinants of population urbanization in China over the period 1991–2023. Utilizing the XGBoost algorithm combined with SHAP (Shapley Additive Explanations), the analysis reveals a tripartite structure of key drivers encompassing industrial support, employment orientation, and infrastructure accessibility. Regional assessments indicate distinct urbanization patterns: Eastern coastal areas are predominantly driven by finance and service industries; central inland regions follow an investment-led trajectory anchored in infrastructure development and real estate expansion, while the western interior relies mainly on employment-centered strategies. Partial Dependence Plots (PDPs) highlighted spatial variations in the effects of sensitive factors, with interaction analyses revealing synergistic effects between tertiary sector shares and the working-age share in eastern coastlands, structural amplification by real estate investment with appropriate working-age population shares in the central inlands, and balancing interactions between GDP growth rates and tertiary sector shares in the western interior. These findings contribute to a more nuanced understanding of the socioeconomic forces shaping urbanization and offer evidence-based recommendations for policymakers in other developing countries seeking to foster sustainable urban growth.