Hasil untuk "Public finance"

Patricia O’Campo

This chapter reviews differences with respect to health outcomes within and between countries, and the role of the ‘social determinants of health’—family, household, community, and societal conditions—in shaping these outcomes. The pathways from before birth, through the early childhood years, to adolescence and adult health are explored, in particular looking at relationships between health and wealth, as manifested through social, economic, and political factors. National wealth is associated with better child health where adequate resources are channelled into public and social goods, such as clean water and sanitation, education, basic health care, social protection. National, and increasingly global, influences on a country’s ability to raise domestic revenue and finance public services are considered, including aid, trade, and the international and multilateral systems. In a rapidly globalising world, more effort is required to elucidate the channels between global, national and local actors in the generation and equitable distribution of resources.

Charles M. Tiebout

R. C. Merton

A. Downs

IN SPITE of the tremendous importance of government decisions in every phase of economic life, economic theorists have never successfully integrated government with private decision-makers in a single general equilibrium theory. Instead they have treated government action as an exogenous variable, determined by political considerations that lie outside the purview of economics. This view is really a carry-over from the classical premise that the private sector is a self-regulating mechanism and that any government action beyond maintenance of law and order is "interference" with it rather than an intrinsic part of it.2 However, in at least two fields of economic theory, the centrality of government action has forced economists to formulate rules that indicate how government "should" make decisions. Thus in the field of public finance, Hugh Dalton states:

T. Besley

R. Boyer

Darrin Grimsey, M. Lewis

M. Ayyagari, A. Demirguc-Kunt, V. Maksimovic

Huwei Wen, Rui Cao, Xuan-Hoa Nghiem et al.

Abstract The maturity mismatch paradox of investment and financing has led to the inefficient upgrading of carbon-intensive industries, and fintech in the banking system has the potential to solve this problem. By utilizing the loan information of Chinese listed companies and banks’ digital transformation index to construct a firm-level banking fintech index, this study aims to investigate the effect of banking fintech on the innovation of firms in carbon-intensive industries. Empirical results show that banking fintech can significantly boost corporate innovation and help carbon-intensive industries address the financing challenges of transformation and innovation. The results remain robust after the use of instrumental variables, dynamic panel models, and quasi-natural experimental methods to account for potential endogeneity. The enabling effect of banking fintech operates through dual pathways: in the quantitative dimension, it helps enhance corporate financial resilience and alleviate financing constraints; in the qualitative dimension, it facilitates breaking down information barriers between banks and enterprises while directing capital flows toward high-potential innovation projects. Additionally, the impact of banking fintech in promoting innovation is greater for large enterprises and state-owned enterprises. Implications and recommendations for financial policies and fintech policies for carbon-intensive industries are presented.

Wenfeng Kang, Jia Zhang, Xiaoyan Wang et al.

Exposure to fine particulate matter (PM2.5) components and ozone (O3) is associated with reduced lung function. This study aimed to examine the interaction effects of PM2.5 components and O3 on lung function in young adults. A cohort study involving 1697 participants was conducted in Shandong Province, China from September 2019 to November 2020. Pollutant data were obtained from the China High Air Pollutants (CHAP) dataset and the Tracking Air Pollution in China (TAP) dataset. Forced Vital Capacity (FVC), first-second forceful expiratory volume (FEV1.0), peak expiratory flow rate (PEF) and 50 % forceful expiratory flow rate (FEF50 %) were used as lung function indices. A linear mixed-effects model was employed to evaluate the impact of PM2.5 components and its interaction effects with O3 on lung function. Each 1 μg/m³ increase in black carbon (BC) concentration was significantly associated with 0.4027 L/s decrease in PEF (95 % confidence interval (CI): 0.2420 L/s, 0.5634 L/s). Increases in other PM2.5 components were also associated with various reduced lung function indices. Notably, the interaction term for BC and O3 was significantly associated with reduced PEF (-0.0243, 95 % CI: −0.0472, −0.0014). Synergistic effects between PM2.5 components [organic matter (OM), nitrate (NO3-)] and O3 adversely impacted lung function. A more proactive policy should be adopted to address the synergistic effects of air pollution.

R. Sanders‐DeMott, L. R. Hutyra, M. D. Hurteau et al.

Abstract Forests have substantial potential to help mitigate climate change. Private finance channeled through carbon credits is one way to fund that mitigation, but market‐based approaches to forest carbon projects have been fraught to date. Public skepticism of forest carbon markets signals a need to closely scrutinize the system for certifying carbon credits. We rigorously reviewed and scored new and existing protocols for the voluntary and North American compliance carbon markets. We included protocols for forest projects engaging in improved forest management, afforestation/reforestation, and avoided planned forest conversion. Most protocols score poorly overall, and none were assessed as robust. Only one new protocol that had yet to issue credits at the time of our evaluation was assessed as satisfactory, owing to improvements in the approach to additionality demonstration. We conclude that existing protocols do not ensure carbon credits are consistently real, high‐quality, and accurately represent 1 tonne of avoided, reduced, or removed emissions. We offer recommendations for how protocols can be strengthened using existing data and new tools to promote reliably high‐quality credits. Continuing to rely on the status quo without such investments is a serious risk to climate change mitigation, and in our estimation, these proposed improvements would increase the likelihood that forests carbon projects can deliver their promised climate mitigation benefits.

Chi-Sheng Chen, Aidan Hung-Wen Tsai

This study presents a comprehensive empirical comparison between quantum machine learning (QML) and classical machine learning (CML) approaches in Automated Market Makers (AMM) and Decentralized Finance (DeFi) trading strategies through extensive backtesting on 10 models across multiple cryptocurrency assets. Our analysis encompasses classical ML models (Random Forest, Gradient Boosting, Logistic Regression), pure quantum models (VQE Classifier, QNN, QSVM), hybrid quantum-classical models (QASA Hybrid, QASA Sequence, QuantumRWKV), and transformer models. The results demonstrate that hybrid quantum models achieve superior overall performance with 11.2\% average return and 1.42 average Sharpe ratio, while classical ML models show 9.8\% average return and 1.47 average Sharpe ratio. The QASA Sequence hybrid model achieves the highest individual return of 13.99\% with the best Sharpe ratio of 1.76, demonstrating the potential of quantum-classical hybrid approaches in AMM and DeFi trading strategies.

Yuke Zhang

This study introduces an interpretable machine learning (ML) framework to extract macroeconomic alpha from global news sentiment. We process the Global Database of Events, Language, and Tone (GDELT) Project's worldwide news feed using FinBERT -- a Bidirectional Encoder Representations from Transformers (BERT) based model pretrained on finance-specific language -- to construct daily sentiment indices incorporating mean tone, dispersion, and event impact. These indices drive an XGBoost classifier, benchmarked against logistic regression, to predict next-day returns for EUR/USD, USD/JPY, and 10-year U.S. Treasury futures (ZN). Rigorous out-of-sample (OOS) backtesting (5-fold expanding-window cross-validation, OOS period: c. 2017-April 2025) demonstrates exceptional, cost-adjusted performance for the XGBoost strategy: Sharpe ratios achieve 5.87 (EUR/USD), 4.65 (USD/JPY), and 4.65 (Treasuries), with respective compound annual growth rates (CAGRs) exceeding 50% in Foreign Exchange (FX) and 22% in bonds. Shapley Additive Explanations (SHAP) affirm that sentiment dispersion and article impact are key predictive features. Our findings establish that integrating domain-specific Natural Language Processing (NLP) with interpretable ML offers a potent and explainable source of macro alpha.

OWENVBIUGIE VINCENT OVENSERI

This study investigates the pervasive impact of corruption on Internally Generated Revenue (IGR) in Nigeria, focusing specifically on the collection and administration of taxes and property rates. Utilizing a quantitative research design, data were collected through a structured questionnaire administered to 162 respondents in Edo State, representing various demographics and socioeconomic backgrounds. The analysis reveals a significant negative correlation between corruption and tax collection efficiency, evidenced by a correlation coefficient of -0.65. The findings indicate that high levels of bribery, embezzlement, and misappropriation of funds among tax and revenue officials substantially hinder effective revenue generation, thereby limiting the financial resources available for public services and infrastructural development. Moreover, the study highlights that corruption not only constrains revenue collection but also erodes public trust in government institutions, leading to a diminished perception of the efficacy and reliability of public services. This erosion of trust has far-reaching implications for governance and accountability in Nigeria, as citizens become increasingly disillusioned with their government’s ability to manage resources effectively. To address these challenges, the study emphasizes the urgent need for enhanced transparency, robust accountability mechanisms, and the strengthening of institutional frameworks to combat corruption effectively. Policymakers are urged to implement reforms aimed at improving revenue administration and ensuring that collected taxes and rates are utilized for the public good. By contributing valuable insights to the existing literature on public finance management, this research serves as a critical resource for policymakers and stakeholders seeking to enhance revenue generation while curbing corrupt practices in Nigeria.

Wenya Sun, Yichen Luo, Siu-Ming Yiu et al.

Abstract The informativeness of environmental, social, and governance (ESG) scores and their actual impact on firms remains understudied. To address this gap in the literature, we make theoretical predictions and conduct empirical research revealing that a high ESG score is associated with a lower probability of ESG scandals and lower stock returns during a scandal event. Our results suggest that ESG scores are heterogeneous but informative, and that a strong ESG reputation may have both positive and negative consequences for firms. Drawing on our findings, we develop a model and showcase that firms face an optimization problem when determining optimal ESG investment levels. Two equilibria may exist based on the trade-off between ESG scandal losses and ESG adjustment costs. Our model explains why certain firms make heterogeneous ESG decisions

Masanori Hirano, Kentaro Imajo

Large language models (LLMs) are now widely used in various fields, including finance. However, Japanese financial-specific LLMs have not been proposed yet. Hence, this study aims to construct a Japanese financial-specific LLM through continual pre-training. Before tuning, we constructed Japanese financial-focused datasets for continual pre-training. As a base model, we employed a Japanese LLM that achieved state-of-the-art performance on Japanese financial benchmarks among the 10-billion-class parameter models. After continual pre-training using the datasets and the base model, the tuned model performed better than the original model on the Japanese financial benchmarks. Moreover, the outputs comparison results reveal that the tuned model's outputs tend to be better than the original model's outputs in terms of the quality and length of the answers. These findings indicate that domain-specific continual pre-training is also effective for LLMs. The tuned model is publicly available on Hugging Face.

Qing Guo, Siyu Chen, Xiangquan Zeng

The proliferation of internet technology has catalyzed the rapid development of digital finance, significantly impacting the optimization of resource allocation in China and exerting a substantial and enduring influence on the structure of employment and income distribution. This research utilizes data sourced from the Chinese General Social Survey and the Digital Financial Inclusion Index to scrutinize the influence of digital finance on the gender wage disparity in China. The findings reveal that digital finance reduces the gender wage gap, and this conclusion remains robust after addressing endogeneity problem using instrumental variable methods. Further analysis of the underlying mechanisms indicates that digital finance facilitates female entrepreneurship by lowering financing barriers, thereby promoting employment opportunities for women and also empowering them to negotiate higher wages. Specially, digital finance enhances women's bargaining power within domestic settings, therefore exerts a positive influence on the wages of women. Sub-sample regressions demonstrate that women from economically disadvantaged backgrounds, with lower human capital, benefit more from digital finance, underscoring its inclusive nature. This study provides policy evidence for empowering vulnerable groups to increase their wages and addressing the persistent issue of gender income disparity in the labor market.

Natkamon Tovanich, Célestin Coquidé, Rémy Cazabet

Decentralized Finance (DeFi) is increasingly studied and adopted for its potential to provide accessible and transparent financial services. Analyzing how investors use DeFi is important for reaching a better understanding of their usage and for regulation purposes. However, analyzing DeFi transactions is challenging due to often incomplete or inaccurate labeled data. This paper presents a method to extract ego network motifs from the token transfer network, capturing the transfer of tokens between users and smart contracts. Our results demonstrate that smart contract methods performing specific DeFi operations can be efficiently identified by analyzing these motifs while providing insights into account activities.

Xi-Ning Zhuang, Zhao-Yun Chen, Cheng Xue et al.

Quantum machine learning has demonstrated significant potential in solving practical problems, particularly in statistics-focused areas such as data science and finance. However, challenges remain in preparing and learning statistical models on a quantum processor due to issues with trainability and interpretability. In this letter, we utilize the maximum entropy principle to design a statistics-informed parameterized quantum circuit (SI-PQC) for efficiently preparing and training of quantum computational statistical models, including arbitrary distributions and their weighted mixtures. The SI-PQC features a static structure with trainable parameters, enabling in-depth optimized circuit compilation, exponential reductions in resource and time consumption, and improved trainability and interpretability for learning quantum states and classical model parameters simultaneously. As an efficient subroutine for preparing and learning in various quantum algorithms, the SI-PQC addresses the input bottleneck and facilitates the injection of prior knowledge.

سارة الشاذلي

The aim of this research is to investigate the impact of social media antecedents on brand equity of fast fashion brands, which leads to purchase intention. Based on literature, fast fashion brands rely on e-commerce social media platforms in order to meet the needs and wants of consumers. So, in order to create effective social media presences, the fashion brand should rely on: media interaction, social media sharing, social media credibility, social media relationship, social media customization and social media presence. In this study, the researcher took each dimension and hypothesized them as having a positive significant impact on brand equity, which impacts consumers purchase intentions of fast fashion. This study is quantitative research. It used administrated questionnaires to collect data that is to be analyzed through the SPSS program. The researcher chooses online survey because of the current crisis (covid-19 pandemic). and also chooses the population is Egyptian Middle to upper class youth and young adults (age 16-34) who use social media on a daily basis. The research took place January 2020, following a cross sectional study. The study used the SPSS program to analyze the collected data. The researcher used regular linear multiple regression, correlation and descriptive statistics to get statistical results. The researcher reaches that social media content interaction and social media credibility have an impact on brand equity. The analysis shows that brand equity leads to purchase intentions of fashion.