Hasil untuk "Public finance"

J. Keynes

Joshua Tan, Nicholas Vincent, Katherine Elkins et al.

Open source projects have made incredible progress in producing transparent and widely usable machine learning models and systems, but open source alone will face challenges in fully democratizing access to AI. Unlike software, AI models require substantial resources for activation -- compute, post-training, deployment, and oversight -- which only a few actors can currently provide. This paper argues that open source AI must be complemented by public AI: infrastructure and institutions that ensure models are accessible, sustainable, and governed in the public interest. To achieve the full promise of AI models as prosocial public goods, we need to build public infrastructure to power and deliver open source software and models.

Marc S. Montalvo, Hamed Yaghoobian

Recent advances in large language models (LLMs) are transforming data-intensive domains, with finance representing a high-stakes environment where transparent and reproducible analysis of heterogeneous signals is essential. Traditional quantitative methods remain vulnerable to survivorship bias, while many AI-driven approaches struggle with signal integration, reproducibility, and computational efficiency. We introduce MASFIN, a modular multi-agent framework that integrates LLMs with structured financial metrics and unstructured news, while embedding explicit bias-mitigation protocols. The system leverages GPT-4.1-nano for reproducability and cost-efficient inference and generates weekly portfolios of 15-30 equities with allocation weights optimized for short-term performance. In an eight-week evaluation, MASFIN delivered a 7.33% cumulative return, outperforming the S&P 500, NASDAQ-100, and Dow Jones benchmarks in six of eight weeks, albeit with higher volatility. These findings demonstrate the promise of bias-aware, generative AI frameworks for financial forecasting and highlight opportunities for modular multi-agent design to advance practical, transparent, and reproducible approaches in quantitative finance.

Georgy Lukyanov, Samuel Safaryan

We study public persuasion when a sender communicates with a large audience that can fact-check at heterogeneous costs. The sender commits to a public information policy before the state is realized, but any verifiable claim she makes after observing the state must be truthful (an ex-post implementability constraint). Receivers observe the public message and then decide whether to verify; this selective verification feeds back into the sender's objective and turns the design problem into a constrained version of Bayesian persuasion. Our main result is a reverse comparative static: when fact-checking becomes cheaper in the population, the sender optimally supplies a strictly less informative public signal. Intuitively, cheaper verification makes bold claims invite scrutiny, so the sender coarsens information to dampen the incentive to verify. We also endogenize two ex-post instruments - continuous falsification and fixed-cost repression - and characterize threshold substitutions from persuasion to manipulation and, ultimately, to repression as monitoring improves. The framework provides testable predictions for how transparency, manipulation, and repression co-move with changes in verification technology.

Zbigniew Tetłak

The implementation of public tasks for which public funds are spent should be recognized by striving to achieve the set goals and due diligence in managing funds. In the Public Finance Act (Article 44), the legislator linked the effectiveness of spending public funds with the effectiveness and optimization of the selection of methods for achieving set goals and the timely implementation of incurred obligations. Public tasks defined in European Union law and transferred to Polish legislation most often have precisely defined objectives, for example in the form of unambiguous indicators. The problem is that the indicators are increasing year by year and the threat of high penalties for failing to achieve them. Therefore, the responsibility for the effective spending of public funds for the implementation of the indicated tasks becomes fundamental. An illustration of the above-mentioned problem are tasks in the field of municipal waste management, the goals for which are specified in the Act on maintaining cleanliness and order in municipalities, and municipal the source of their financing are funds from the garbage fee, the amount of which is determined by the council.

Fithria Dyah Ayu Suryanegara, Deni Iskandar, Ericko Ekaputra et al.

Abstract Background Breast cancer is the most common cancer in Indonesia, and radiotherapy plays an essential role in its treatment. However, since 2016, the INA-CBGs (Indonesian Case-Based Groups) tariffs for radiotherapy have remained unchanged. This study aimed to assess the disparity between tariffs and actual costs of outpatient radiotherapy in breast cancer, using real-world data from two Indonesian hospitals. Methods We conducted a retrospective cohort study in a national public referral hospital and a private hospital. Breast cancer claims data were collected from 2017 to 2022 from the Department of Accounting/Finance with INA-CBGs tariff code of C-3-10-0 (radiotherapy procedures for outpatients). We estimated total actual costs, actual costs per patient and visit, and the cost-tariffs ratio. Differences between the actual costs and tariffs were analyzed using Mann-Whitney test. Results A total of 3,890 breast cancer patients were included in the study, of which 74.4% were from the national public referral hospital. In the national public referral hospital and private hospital, the total actual costs of outpatient radiotherapy in breast cancer were USD 19,028,791.17 and USD 5,279,980.74, with median costs per patient of USD 6,560.00 [3,679.81;7,518.46] and USD 5,110.00 [839.15;7,552.34], and median costs per visit of USD 272.00 [253.16;274.47] and USD 272.00 [211.31;305.50], respectively. Over the study period, the cost-tariffs ratio was 86.85% and 59.07% in the national public referral hospital and private hospital, respectively. The differences between the tariffs and total actual costs were statistically significant in both hospitals and increased throughout the years. Conclusions For both hospitals, the INA-CBGs tariffs for outpatient radiotherapy services for breast cancer were insufficient to fully cover the actual costs during the review period. Furthermore, the difference between the tariffs and the actual costs increased over the years, emphasizing the need for revision of the C-3-10-0 tariffs. It is crucial to ensure coverage of all actual costs to ensure the sustainability, accessibility, and availability of radiotherapy treatment for breast cancer patients in Indonesia.

Anton Andriienko

The article addresses the issue of "Ghost Park" syndrome, which occurs when industrial parks and special economic zones are legally established yet fail to evolve into fully functioning industrial ecosystems. The subject of the present study is the viability of industrial ecosystems in fragile and high-risk environments, with a particular focus on how policy makers, development finance institutions, and investors can distinguish viable parks from those that are likely to become Ghost Parks. The objective of the research is to develop an integrated assessment framework that captures not only planned inputs, but also real operational performance, resilience and social outcomes. The overarching aim is to support evidence-based industrial policy and investment decisions. The methodology combines conceptual systematisation of existing approaches to zone evaluation with a hybrid Multi-Criteria Decision-Making model. The study develops the OPERRA framework, which comprises the following six dimensions: Operational Performance, Plug-and-Play Readiness, Economic Vibrancy, Risk and Security Management, Resource and Climate Resilience, and Social and Environmental, Social and Governance (ESG) Adaptation. Hierarchical decomposition is then applied to translate these dimensions into measurable criteria and indicators. The Analytic Hierarchy Process is used to identify and combine stakeholder preferences regarding these criteria. TOPSIS is then employed to benchmark and rank industrial parks according to how close they are to an ideal operational and resilience profile, and how far they are from a Ghost Park profile. This methodological design enables the incorporation of factors such as actual occupancy, job density, infrastructure readiness, security, governance, and disaster risk management into a single, transparent assessment tool. The study concludes that the outcomes of Ghost Parks are rarely accidental. This usually occurs when the authorities rely on optimistic planning assumptions, focus on land designation and tax incentives, and underestimate the importance of credible infrastructure commitments, security, climate resilience, and social integration. In contrast, parks that perform well under the OPERRA framework demonstrate a combination of credible sunk-cost signals, robust institutional arrangements, reliable plug-and-play services and strong links to labour markets and local communities. The article demonstrates that the long-term viability of industrial ecosystems depends less on formal status and projected investment volumes, and more on verifiable performance and resilience. The main conclusion is that the OPERRA framework can be used as a practical tool for the ex-ante screening and ex-post monitoring of industrial ecosystems, helping to prevent the creation of new ghost parks and prioritise scarce public resources.

Steven Dermawan, Afifah Trista Ayunda

In January 2025, Indonesia’s Ministry of Finance launched Coretax to replace DJP Online. However, the launch triggered widespread dissatisfaction among users, reflecting negative public sentiment. This study aims to analyze public perception of Coretax and evaluate the performance of machine learning models in sentiment classification. A total of 6.036 Indonesian language tweets related to Coretax, posted between January and April 2025, were collected using Tweet Harvest. The dataset consists of 0,83% positive, 51,05% negative, and 48,11% neutral sentiments. The research methodology involved several stages: data crawling, manual labeling, preprocessing (cleaning, case folding, stopword removal, tokenization, normalization, stemming, and specifically for LSTM: conversion of tokens into numerical indices, padding, and embedding), feature representation using TF-IDF for classical models and word embedding for deep learning, data balancing with SMOTE, model implementation (Naive Bayes, Support Vector Machine with various kernels, and LSTM), model evaluation and comparison, and visualization through word clouds. The application of SMOTE succeeded in improving the performance of all algorithms. After applying SMOTE, the SVM with the RBF kernel achieved the best performance with 90,70% accuracy, 91% precision, 90,66% recall, and 90,66% F1-score. Keyword analysis revealed that terms such as “data” and “mudah” dominated positive sentiment, “silakan” and “kakak” were prevalent in neutral sentiment, while “sistem” and “error” frequently appeared in negative sentiment. The findings highlight the urgent need for system infrastructure improvements, user-centered features, responsive technical support, taxpayer training, and continuous updates to enhance Coretax and restore public trust.

Andi Mehmeti, Endrit Elezi, Armila Xhebraj et al.

Hydrogen and fuel cell technologies are increasingly recognized as strategic tools for decarbonizing the aviation sector, offering not only environmental benefits but also operational and economic value. However, their effective integration within complex airport ecosystems remains underexplored, particularly concerning investment planning and infrastructure management. This study evaluates the technical, environmental, and economic feasibility of deploying hydrogen technologies at Tirana International Airport (TIA), focusing on both stationary and mobility applications powered by rooftop photovoltaic (PV) systems. Scenario-based modeling shows that allocating 25% of TIA’s PV output could produce approximately 5,750 kg of green hydrogen annually—sufficient to supply 96 MWh of clean electricity or support 63,889 km of hydrogen-powered vehicle travel. The environmental assessment reveals a net annual CO₂ reduction of 58,576 kg, primarily from mobility uses, which alone account for 70,181 kg of avoided emissions. Economic analysis estimates the Levelized Cost of Hydrogen (LCOH) at €6.91/kg under current conditions, with potential to decline to €4.45/kg in a lower-CAPEX scenario. The corresponding cost for hydrogen mobility ranges from €1.16/km to €0.85/km, depending on technology and investment assumptions. These results highlight the importance of capital planning, utilization rates, and cost optimization strategies for real-world deployment. A SWOT analysis is used to identify strategic enablers and barriers, revealing key opportunities such as access to EU funding, public-private partnerships (PPPs), employment creation, and the potential to position TIA as a green infrastructure leader in the Western Balkans. The study concludes that hydrogen integration, if paired with targeted investment, institutional support, and coordinated stakeholder engagement, can significantly enhance airport resilience, energy autonomy, and economic competitiveness, aligning with both national policy priorities and broader European sustainability goals.

رنا محمد عبدالله حسين, اية سعيد حنفي

Policy lapses and cancellations pose significant challenges to the stability and profitability of the Egyptian insurance market. This study evaluates the effectiveness of machine learning (ML) models—Logistic Regression, Random Forest, Support Vector Machines (SVM), and Artificial Neural Networks (ANN)—to predict lapses based on policyholder data and behavioral trends. Key predictors such as income, age, premium amount, payment frequency, and policy type were analyzed to identify actionable insights. The findings highlight the transformative potential of AI in improving customer retention, optimizing operational efficiency, and addressing critical market challenges.The findings demonstrate that AI-based predictive models offer significant improvements in forecasting policy lapses and cancellations compared to traditional methods. These models effectively capture complex, non-linear relationships within the data, resulting in more accurate and reliable predictions. The study emphasizes the importance of using advanced data preprocessing, feature engineering, and model evaluation techniques to ensure optimal model performance.Furthermore, the research underscores the importance of predictive analytics in reducing the financial impact of policy lapses and cancellations. By identifying at-risk policies in advance, insurance companies can take proactive measures, such as targeted marketing and personalized interventions, to retain valuable customers and improve overall profitability.

Roger Lee Mendoza

Rate-making — a vital component of insurance and risk management — can be creative and at times contentious because pricing is its central goal. In the United States, the Affordable Care Act (ACA) permits insurers in the non-group and small group markets to vary health insurance premiums only on the basis of age, geographic location, family coverage, and tobacco use. Tobacco use as a rating factor in setting insurance premiums cannot exceed 50 percent of the non-smoker rate. Academic literature is limited and focuses on questions of insurance accessibility and equity, and smoking cessation impact. There is yet no study or evidence available on the price or financial burden that may be imposed by rate-making on (different variants of) the smoker or tobacco surcharge. In approaching the surcharge from a payer — rather than enrollee — standpoint, this study conceptually investigates how insurers might devise tobacco surcharges to manage objective risk and their premium price implications while meeting both regulatory and business imperatives. The federal standard age curve of the CMS (Centers for Medicare & Medicaid Services) and sampled insurer rating schedules were used in this study to develop illustrations of multiplicative and additive rate structures of the tobacco surcharge. Findings suggest that insurers have latitude to apply tobacco surcharges within and perhaps beyond the substantive intent of healthcare reform, provided the state where they operate does not prohibit it or only reduces the federal cap. ACA implementing and state regulations do not appear to restrict or diminish such flexibility. The risk-related and public policy consequences of modified community rating for smokers are addressed by way of conclusion.

Tianbao Zhou, Zhixin Liu, Yingying Xu

Based on the quarterly data from 26 advanced economies (AEs) and 18 emerging market economies (EMs) over the past two decades, this paper estimates the short- and medium-term impacts of financial cycles on the duration and amplitude of public debt cycles. The results indicate that public debt expansions are larger than their contractions in duration and amplitude, aligning with the "deficit bias hypothesis" and being more pronounced in EMs than in AEs. The impacts of various financial cycles are different. Specifically, credit cycles in EMs significantly impact the duration and amplitude of public debt cycles. Notably, short- and medium-term credit booms in EMs shorten the duration of public debt contractions and reduce the amplitude. Fast credit growth in AEs prolongs the duration of public debt expansions and increases the amplitude. However, credit cycles in AEs show no significant impact. For house price cycles, the overall impact is stronger in EMs than in AEs, differing between short- and medium-term cycles. Finally, the impact of equity price cycles is significant in the short term, but not in the medium term. Equity price busts are more likely to prolong the expansion of public debt in EMs while increasing the amplitude of public debt contractions in AEs. Uncovering the impacts of multiple financial cycles on public debt cycles provides implications for better debt policies under different financial conditions.

Xiangpeng Wan, Haicheng Deng, Kai Zou et al.

Structured finance, which involves restructuring diverse assets into securities like MBS, ABS, and CDOs, enhances capital market efficiency but presents significant due diligence challenges. This study explores the integration of artificial intelligence (AI) with traditional asset review processes to improve efficiency and accuracy in structured finance. Using both open-sourced and close-sourced large language models (LLMs), we demonstrate that AI can automate the verification of information between loan applications and bank statements effectively. While close-sourced models such as GPT-4 show superior performance, open-sourced models like LLAMA3 offer a cost-effective alternative. Dual-agent systems further increase accuracy, though this comes with higher operational costs. This research highlights AI's potential to minimize manual errors and streamline due diligence, suggesting a broader application of AI in financial document analysis and risk management.

Sara Fish, Yannai A. Gonczarowski, Sergiu Hart

We study a matching problem between agents and public goods, in settings without monetary transfers. Since goods are public, they have no capacity constraints. There is no exogenously defined budget of goods to be provided. Rather, each provided good must justify its cost by being utilized by sufficiently many agents, leading to strong complementarities in the "preferences" of goods. Furthermore, goods that are in high demand given other already-provided goods must also be provided. The question of the existence of a stable solution (a menu of public goods to be provided) exhibits a rich combinatorial structure. We uncover sufficient conditions and necessary conditions for guaranteeing the existence of a stable solution, and derive both positive and negative results for strategyproof stable matching.

Sharif Mozumder, M. Kabir Hassan, M. Humayun Kabir

Abstract This study investigates the simplicity and adequacy of tail-based risk measures—value-at-risk (VaR) and expected shortfall (ES)—when applied to tail targeting of the extreme value (EV) model. We implement Lévy–VaR and ES risk measures as full density-based alternatives to the generalized Pareto VaR and the generalized Pareto ES of the tail-targeting EV model. Using data on futures contracts of S&P500, FTSE100, DAX, Hang Seng, and Nikkei 225 during the Global Financial Crisis of 2007–2008, we find that the simplicity of tail-based risk management with a tail-targeting EV model is more attractive. However, the performance of EV risk estimates is not necessarily superior to that of full density-based relatively complex Lévy risk estimates, which may not always give us more robust VaR and ES results, making the model inadequate from a practical perspective. There is randomness in the estimation performances under both approaches for different data ranges and coverage levels. Such mixed results imply that banks, financial institutions, and policymakers should find a way to compromise or trade-off between “simplicity” and user-defined “adequacy”.

Mahmut Baydaş, Mustafa Yılmaz, Željko Jović et al.

Abstract The approach of evaluating the final scores of multi-criteria decision-making (MCDM) methods according to the strength of association with real-life rankings is interesting for comparing MCDM methods. This approach has recently been applied mostly to financial data. In these studies, where it is emphasized that some methods show more stable success, it would be useful to see the results that will emerge by testing the approach on different data structures more comprehensively. Moreover, not only the final MCDM results but also the performance of normalization techniques and data types (fuzzy or crisp), which are components of MCDM, can be compared using the same approach. These components also have the potential to affect MCDM results directly. In this direction, in our study, the economic performances of G-20 (Group of 20) countries, which have different data structures, were calculated over ten different periodic decision matrices. Ten different crisp-based MCDM methods (COPRAS, CODAS, MOORA, TOPSIS, MABAC, VIKOR (S, R, Q), FUCA, and ELECTRE III) with different capabilities were used to better visualize the big picture. The relationships between two different real-life reference anchors and MCDM methods were used as a basis for comparison. The CODAS method develops a high correlation with both anchors in most periods. The most appropriate normalization technique for CODAS was identified using these two anchors. Interestingly, the maximum normalization technique was the most successful among the alternatives (max, min–max, vector, sum, and alternative ranking-based). Moreover, we compared the two main data types by comparing the correlation results of crisp-based and fuzzy-based CODAS. The results were very consistent, and the “Maximum normalization-based fuzzy integrated CODAS procedure” was proposed to decision-makers to measure the economic performance of the countries.

Naohiro Takanashi, Masaaki Hiramatsu, Shio Kawagoe et al.

We report on the results of a survey we conducted on the Japanese public's attitudes toward astronomy. This survey was conducted via an online questionnaire, with 2,000 responses received. Based on this data, we present what kind of interest the general public in Japan has in astronomy. We also conducted a questionnaire survey of those involved in astronomy communication to examine how they differ from the general public. The results suggest that while there are clear differences between them in terms of their engagement in astronomy, there is also continuity between them by looking at their attributes in more detail. The data presented in this paper could help us to promote communicating astronomy to the public.

Xifeng Wu, Yue Shen, Jin Chen et al.

Abstract This study proposes a social-financial approach (SFA) to fill the methodological research gap in strategic policy design for managing financial transitions during social changes. The SFA seeks to characterize inclusive transitions in response to innovation and analyze financial management in social changes. Using a multilevel perspective, we combine evolutionary finance and inclusive growth analytics into this framework. We contend that the interaction between the different levels can be summarized as spontaneous adjustments and the alignment of financial elements with the indicators. Actors who attempt to achieve their goals based on past performance evaluations and other forms of bounded rationality strive to cope with adjustments and further trigger a reorientation of the existing regime. We also developed a new configuration tool called the three-axis description to describe the evolution of financial transitions at different stages. These methods allow us to analyze the evolution of financial transition and efficiency, and we argue that market efficiency evolves in stages with the financial transition. Finally, to demonstrate the capability of SFA to identify diverse financial transition pathways, we examined an example case: the establishment of the Bretton Woods System.

Katarzyna Owsiak

Theoretical background: The contemporary approach to public debt is multi-faceted. Debt incurred to finance current expenses is assessed differently than debt financing capital expenditure. This distinction is also important from the point of view of local authorities as part of their budgetary policy. Each decision related to incurring a debt has not only financial consequences, but is also made in the political and image context. It is worth noting that the current research on debt insufficiently emphasizes its direct impact on the socio-economic development of a given local government unit, devoting much more attention to the impact of debt on the condition of public finances. In the case of the first stream of research, significant discrepancies should be noted with regard to the selection of optimal indicators for measuring the impact of debt on local development. Therefore, one should agree that local development is conditioned by a whole group of factors dependent and independent of local authorities. The article attempts to verify several indicators of the development of infrastructure partially financed with debt. Purpose of the article: Against this background, the subject of this study is to identify the policy of incurring debt by local government units in Poland in the longer term, to determine the degree of diversification of this policy, as well as, and perhaps above all, to link this policy with development processes. In order to avoid a superficial approach to such outlined issues, the scope of observation was limited to the communes of the Małopolska Voivodeship. Such an approach also made it possible to take into account a longer period of observation of the surveyed communes (2010–2020). Research methods: Out of the total number of 179 communes existing in Małopolska, 11 urban communes (the whole group due to their limited number), 20 rural communes and 20 urban-rural communes were randomly selected for the study. The basic criterion for assessing the policy of municipalities in relation to local debt was the ratio of total debt per capita to total revenue per capita. This indicator allows to determine the level of debt burden on communes’ revenue. Next, the total impact of variables characterizing the budgetary policy of the commune was examined, such as: a) own revenue per capita, b) investment expenditure per capita, c) non-recoverable property expenditure per capita (mainly from the European Union), d) debt per capita, on selected indicators of the development of municipal infrastructure of the commune, affecting the quality of life of the commune’s inhabitants. Progress in the development of infrastructure improves the conditions for conducting business activity of private enterprises, including the location of new entities, which may result in an increase in the commune’s own revenue. The study used a multiple correlation coefficient, the value of which was calculated in each distinguished cluster of communes. The obtained results allowed to assess how the level of indebtedness of communes influenced the socio-economic development of these units. Main findings: The conducted analysis made it possible to determine the degree of restrictiveness carried out by the authorities of the analyzed municipalities of Małopolska. The level of the multiple correlation coefficient calculated for municipalities between the explanatory variables characterizing the budgetary policy of municipalities (investment expenditure per capita, debt per capita, non-refundable property expenditure per capita and the dependent variable own revenue per capita) proves a fairly strong relationship between the analyzed variables. On the other hand, in rural communes with a low restrictive budget policy (with a high debt ratio), the total impact of the indicated variables on the level of infrastructure development is generally stronger than in communes with a more disciplined budget policy (with a lower debt level). In the case of urban-rural communes with a low-restrictive budget policy (high debt ratio), the total impact of the analyzed variables on the level of infrastructure development in a commune is generally stronger than in communes with a more disciplined budget policy (with a lower level of debt).

Kiryl Kascian

On 18 January 2022, the parliament of Lithuania adopted the law on writing personal names in the official documents. Having substantially liberalised the existing practice, the law, however, offered only a partial solution because it did not sanction the use of non-Lithuanian diacritical characters. Based on the relevant legal, linguistic and historical contexts, relevant Lithuanian legislation and case law, this study analyses the evolution of Lithuania’s approach towards the writing of its citizens’ personal names in official documents. The text shows that the significant changes that took place in Lithuanian society during the last 30 years have resulted in a partial liberalisation of these practices. The recent case law suggests that the state is on the verge of accepting full liberalisation, particularly if relevant interwar legislation and a broader understanding of historical traditions of the Lithuanian language will be taken into account. However, the Constitutional Court plays a key role in determining the contents and directions of this process.