Hasil untuk "Economics"

Wensu Li, Atin Aboutorabi, Harry Lyu et al.

This paper develops a unified framework for evaluating the optimal degree of task automation. Moving beyond binary automate-or-not assessments, we model automation intensity as a continuous choice in which firms minimize costs by selecting an AI accuracy level, from no automation through partial human-AI collaboration to full automation. On the supply side, we estimate an AI production function via scaling-law experiments linking performance to data, compute, and model size. Because AI systems exhibit predictable but diminishing returns to these inputs, the cost of higher accuracy is convex: good performance may be inexpensive, but near-perfect accuracy is disproportionately costly. Full automation is therefore often not cost-minimizing; partial automation, where firms retain human workers for residual tasks, frequently emerges as the equilibrium. On the demand side, we introduce an entropy-based measure of task complexity that maps model accuracy into a labor substitution ratio, quantifying human labor displacement at each accuracy level. We calibrate the framework with O*NET task data, a survey of 3,778 domain experts, and GPT-4o-derived task decompositions, implementing it in computer vision. Task complexity shapes substitution: low-complexity tasks see high substitution, while high-complexity tasks favor limited partial automation. Scale of deployment is a key determinant: AI-as-a-Service and AI agents spread fixed costs across users, sharply expanding economically viable tasks. At the firm level, cost-effective automation captures approximately 11% of computer-vision-exposed labor compensation; under economy-wide deployment, this share rises sharply. Since other AI systems exhibit similar scaling-law economics, our mechanisms extend beyond computer vision, reinforcing that partial automation is often the economically rational long-run outcome, not merely a transitional phase.

Lisa Capretti, Lorenzo Tonni

The empirical literature on the relationship between income inequality and economic growth has produced highly heterogeneous and often conflicting results. This paper investigates the sources of this heterogeneity using a meta-analytic approach that systematically combines and analyzes evidence from relevant studies published between 1994 and 2025. We find an economically small but statistically significant negative average effect of income inequality on subsequent economic growth, together with strong evidence of substantial heterogeneity and selective publication based on statistical significance, but no evidence of systematic directional bias. To explain the observed heterogeneity, we estimate a meta-regression. The results indicate that both real-world characteristics and research design choices shape reported effect sizes. In particular, inequality measured net of taxes and transfers is associated with more negative growth effects, and the adverse impact of inequality is weaker - or even reversed - in high-income economies relative to developing countries. Methodological choices also matter: cross-sectional studies tend to report more negative estimates, while fixed-effects, instrumental-variable, and GMM estimators are associated with more positive estimates in panel settings.

B.S. Prashanth, Manoj Kumar, Ariful Hoque et al.

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal.We utilize a supervised learning pipeline that incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives.Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

Agustín García-García, Pablo Hidalgo, Julio E. Sandubete

Explainable Artificial Intelligence (XAI) is increasingly required in computational economics, where machine-learning forecasters can outperform classical econometric models but remain difficult to audit and use for policy. This survey reviews and organizes the growing literature on XAI for economic time series, where autocorrelation, non-stationarity, seasonality, mixed frequencies, and regime shifts can make standard explanation techniques unreliable or economically implausible. We propose a taxonomy that classifies methods by (i) explanation mechanism: propagation-based approaches (e.g., Integrated Gradients, Layer-wise Relevance Propagation), perturbation and game-theoretic attribution (e.g., permutation importance, LIME, SHAP), and function-based global tools (e.g., Accumulated Local Effects); (ii) time-series compatibility, including preservation of temporal dependence, stability over time, and respect for data-generating constraints. We synthesize time-series-specific adaptations such as vector- and window-based formulations (e.g., Vector SHAP, WindowSHAP) that reduce lag fragmentation and computational cost while improving interpretability. We also connect explainability to causal inference and policy analysis through interventional attributions (Causal Shapley values) and constrained counterfactual reasoning. Finally, we discuss intrinsically interpretable architectures (notably attention-based transformers) and provide guidance for decision-grade applications such as nowcasting, stress testing, and regime monitoring, emphasizing attribution uncertainty and explanation dynamics as indicators of structural change.

Pierre Bardier

We provide a formal framework accounting for a widespread idea in the theory of economic design: analytically established incompatibilities between given axioms should be qualified by the likelihood of their violation. We define the degree to which rules satisfy an axiom, as well as several axioms, on the basis of a probability measure over the inputs of the rules. Armed with this notion of degree, we propose and characterize i) a criterion to evaluate and compare rules given a set of axioms, allowing the importance of each combination of axioms to differ, and ii) a criterion to measure the compatibility between given axioms, building on a analogy with cooperative game theory.

Abdul Galib, Nurwahyuni Syahrir, Hasnidar Hasnidar

Main Purpose -  This study aims to reveal the role of awareness of sustainable practices, culture, and perceived behavioral control in improving tax compliance as an effort to maintain sustainable business practices for MSMEs in West Sulawesi by internalizing the Pappasang Kalindaqdaq Mandar. Method -  The research method used in this study is a mixed method with a concurrent model to analyze quantitatively and qualitatively simultaneously. Main Findings -  The results confirmed the theory of planned behavior, whereby awareness of sustainable practices and culture has a significant influence on increasing MSMEs' intention to behave in a compliant manner towards taxation, but perceived behavioral control did not have a significant influence. These findings indicate that aspects of awareness of sustainable practices and internalization of Pappasang Kalindaqdaq Mandar culture have a strong dominance in explaining MSME tax compliance in West Sulawesi. Theory and Practical Implications - The strong dominance of tax awareness and culture, but not accompanied by a significant influence on perceived behavioral control, requires further investigation. Further in-depth interviews are needed to obtain more in-depth information from tax authorities and MSMEs to uncover the actual role of control.  Novelty -  This research explores non-economic aspects from various perspectives such as awareness of sustainable practices (internal), culture (external), and perceived behavioral control (control belief) in improving tax compliance (external).

Faiza Bouzemlal, Ali Nabil Belouard

Exchange rate volatility can have socioeconomic challenges and a significant impact on export diversification of major global economies. The main objective of this article is to assess the symmetric and asymmetric effect of exchange rate volatility on Algerian export diversification. For this purpose, the autoregressive linear and nonlinear distributed lag (ARDL) model and annual data for the period 1990-2023 were used.The empirical findings using the estimation for time series data reveal that the volatility of the exchange rate has a symmetric effect on export diversification. The results revealed the presence of cointegration between the variables. The relationship between exchange rate volatility and export diversification in Algeria is positive and symmetric, which is contrary to conventional wisdom, as both currency depreciation and appreciation were found to boost diversification. Economic openness and GDP per capita significantly promote diversification, while investment and infrastructure surprisingly hinder it. Inflation also has an unexpected positive effect. The model adjusts quickly to equilibrium, though short-run results show mixed results. Diagnostic tests confirm robustness, except for serial correlation, corrected via Newey-West standard errors. This article suggests that policy makers should adopt different policies to address socio-economic challenges and keep the exchange rate stable in order to promote export diversification.

Ignasius Radix Astadi Praptono Jati, Adrianus Rulianto Utomo, Erni Setijawaty et al.

Abstract The aims of this research are to investigate the effects of different concentrations of butterfly pea flower extract infusion as an active agent on the properties of durian fruit albedo and eggshell-based smart edible films. The butterfly pea flower was extracted using water with the ratios of 1:50 (T1), 1:100 (T2), 1:150 (T3), 1:200 (T4), 1:250 (T5), and 1:300 (T6) (w/v). The film was formulated using durian albedo, eggshell, sorbitol, and cornstarch, which was mixed with butterfly pea flower extract and mold using the casting method. The analysis performed included anthocyanin and phenolic content, antioxidant activity, tensile strength, elongation, water vapor transmission rate, scanning electron microscopy, Fourier transform infrared spectroscopy, and smart indicator examination using fresh milk model system. Different concentrations of butterfly pea extract affect the physicochemical properties of smart edible film. The increase in extract concentration increased anthocyanin and phenolic contents, which align with the increase in antioxidant activity. Meanwhile, the presence of bioactive compounds in the formulation reduced the tensile strength of the film and increased its elongation, as confirmed by SEM and FTIR results. Smart edible film can act as an indicator in the fresh milk model by changing color according to the change in pH due to milk spoilage.

Юлія Терещенко

Стаття присвячена аналізу глобального та українського ринку розваг. Виокремлено ключові тренди, що впливають на розвиток бізнес-екосистем індустрії розваг. Підкреслено важливість інновацій, адаптивності та інтеграції цифрових рішень у функціонуванні сучасних бізнес-екосистем. Досліджено архітектоніку екосистемної бізнес-моделі в індустрії розваг з урахуванням динаміки цифрової трансформації. Запропоновано багаторівневу бізнес-модель з урахуванням соціально-технічної архітектури, що відображає взаємозв’язки між акторами ринку, технологічною базою та механізмами створення цінності. Отримані результати дозволяють сформувати інструментарій системного оцінювання екосистемних бізнес-моделей в умовах цифрової економіки та швидкоплинних ринкових змін. Запропонований підхід має практичну цінність для компаній, що прагнуть до сталого розвитку та підвищення конкурентоспроможності в індустрії розваг.

Luis Guijarro, Maurizio Naldi, Vicent Pla et al.

In Beyond5G and 6G networks, a common theme is that sensing will play a more significant role than ever before. Over this trend, Integrated Sensing and Communications (ISAC) is focused on unifying the sensing functionalities and the communications ones and to pursue direct tradeoffs between them as well as mutual performance gains. We frame the resource tradeoff between the SAC functionalities within an economic setting. We model a service provision by one operator to the users, the utility of which is derived from both SAC functionalities. The tradeoff between the resources that the operator assigns to the SAC functionalities is analyzed from the point of view of the service prices, quantities and profits. We demonstrate that equilibrium quantities and prices exist. And we provide relevant recommendations for enforcing regulatory limits of both power and bandwidth.

Elia Moretti, Michael Benzaquen

Biodiversity loss driven by agricultural intensification is a pressing global issue, with significant implications for ecosystem stability and human well-being. Existing policy instruments have so far proven insufficient in halting this decline, which raises the need to explore the possible feedback loops that are pivotal to ecosystem degradation. We design a minimal integrated bio-economic agent-based model to qualitatively explore macro-level biodiversity trends, as influenced by individual farmer behavior within simple decision-making processes. Our model predicts further biodiversity decline under a business-as-usual scenario, primarily due to intensified land consolidation. We evaluate two policy options: reducing pesticide use and subsidizing small farmers. While pesticide reduction rapidly benefits biodiversity in the beginning, it eventually leads to increased land consolidation and further biodiversity loss. In contrast, subsidizing small farmers by reallocating a small fraction of existing subsidies, stabilizes farm sizes and enhances biodiversity in the long run. The most effective strategy results from combining both policies, leveraging pesticide reduction alongside targeted subsidies to balance economic pressures and consistently improve biodiversity.

Ge Zhu

Abstract Background With the global aging population, attention to the health and medical issues of older adults is increasing. By analyzing the relationship between older people's participation in outdoor activities and medical expenditure, this study aims to provide a scientific basis for improving their quality of life and reducing the medical burden. Methods Data on outdoor activity participation, medical expenditures, and relevant variables were collected through questionnaires and databases. A multi-chain mediation effect model was established to analyze the impact of outdoor activities on the medical expenditure of older people, considering mediation effects and heterogeneity. Results Results revealed that increased participation in outdoor activities among older adults correlated with lower medical expenditures. Outdoor activities positively influenced their health by improving mental health, cognition, eating habits, and activities of daily living, resulting in reduced medical expenditures. Robustness tests confirmed the consistent effect of outdoor activities on older people's medical expenditure. Conclusion These findings contribute to understanding the relationship between outdoor activities, health, and medical expenditure in older people, guiding policy formulation and interventions. Encouraging and supporting older adults in outdoor activities can enhance their quality of life and alleviate medical resource strain. The study's conclusions can also inform health promotion measures for other populations and serve as a basis for future research in this area.

Abderrazak Khediri, Ayoub Yahiaoui, Mohamed Ridda Laouar et al.

Blackout events in smart grids can have significant impacts on individuals, communities and businesses, as they can disrupt the power supply and cause damage to the grid. In this paper, a new proactive approach to an early warning system for predicting blackout events in smart grids is presented. The system is based on deep learning models: convolutional neural networks (CNN) and deep self-organizing maps (DSOM), and is designed to analyse data from various sources, such as power demand, generation, transmission, distribution and weather forecasts. The system performance is evaluated using a dataset of time windows and labels, where the labels indicate whether a blackout event occurred within a given time window. It is found that the system is able to achieve an accuracy of 98.71% and a precision of 98.65% in predicting blackout events. The results suggest that the early warning system presented in this paper is a promising tool for improving the resilience and reliability of electrical grids and for mitigating the impacts of blackout events on communities and businesses.

Mengbing Du, Jianhui Ruan, Zhe Zhang et al.

This study investigates the impact of economic globalization on China's climate using city-level data from 2005 to 2019. This study employs the Spatial Durbin Model (SDM) to reveal an inverted U-shaped relationship between globalization and climate impact, with carbon emissions initially increasing due to scale and composition effects. Surprisingly, the technology effect has limited effectiveness, particularly in second and third-tier cities. Another significant finding of this study is the observed positive correlation between carbon emissions in the previous and current periods. However, this trend leads to lower carbon emissions in neighbouring regions. The results of this study call for technology-focused policies and international collaborations to promote sustainable development in Chinese cities.

Agata Angelika Rzoska, Aleksandra Drozd-Rzoska

The report discusses the emergence of the Socio-Economic Soft Matter (SE-SM) as the result of interactions between physics and economy. First, demographic changes since the Industrial Revolution onset are tested using Soft Matter science tools. Notable in the support of innovative derivative-based and distortions-sensitive analytic tools. It revealed the Weibull type powered exponential increase, with a notably lesser rising rate since the crossover detected near the year 1970. Subsequently, demographic (SE-SM) patterns are tested for Rapa Nui (Easter) Island model case and for four large 'hallmark cities' where the rise and decay phases have occurred. They are Detroit and Cleveland in the USA and Lodz (former textile industry center) and Bytom (former coal mining center) in Poland. The analysis explicitly revealed scaling patterns for demographic changes, influenced by the historical and socio-economic backgrounds and the long-lasting determinism in population changes. Universalistic features of demographic changes are discussed within the Socio-Economic Soft Matter concept.

Alberto Baccini, Cristina Re

This study investigates the role of editorial board members as gatekeepers in science, creating and utilizing a database of 1,516 active economics journals in 2019, which includes more than 44,000 scholars from over 6,000 institutions and 142 countries. The composition of these editorial boards is explored in terms of geographic affiliation, institutional affiliation, and gender. Results highlight that the academic publishing environment is primarily governed by men affiliated with elite universities in the United States. The study further explores social similarities among journals using a network analysis perspective based on interlocking editorship. Comparison of networks generated by all scholars, editorial leaders, and non-editorial leaders reveals significant structural similarities and associations among clusters of journals. These results indicate that links between pairs of journals tend to be redundant, and this can be interpreted in terms of social and intellectual homophily within each board, and between boards of journals belonging to the same cluster. Finally, the analysis of the most central journals and scholars in the networks suggests that journals probably adopt 'strategic decisions' in the selection of the editorial board members. The documented high concentration of editorial power poses a serious risk to innovative research in economics.

Victor Olkhov

This paper studies the links between the descriptions of macroeconomic variables and statistical moments of market trade, price, and return. The randomness of market trade values and volumes during the averaging interval Δ results in the random properties of price and return. We describe how averages and volatilities of price and return depend on the averages, volatilities, and correlations of market trade values and volumes. The averages, volatilities, and correlations of market trade, price, and return can behave randomly during the long interval Δ2>>Δ. To describe their statistical properties during the long interval Δ2, we introduce the secondary averaging procedure of trade, price, and return. We explain why, in the coming years, predictions of market-based probabilities of price and return will be limited by Gaussian distributions. We discuss the roots of the internal weakness of the commonly used hedging tool, Value-at-Risk, that cannot be solved and remains the source of additional risks and losses. One should consider theoretical economics as a set of successive approximations, each of which describes the next array of the n-th statistical moments of market trades, price, return, and macroeconomic variables, which are repeatedly averaged during the sequence of increasing time intervals.

Ali Lashgari

Forecasting financial time series (FTS) is an essential field in finance and economics that anticipates market movements in financial markets. This paper investigates the accuracy of text mining and technical analyses in forecasting financial time series. It focuses on the S&P500 stock market index during the pandemic, which tracks the performance of the largest publicly traded companies in the US. The study compares two methods of forecasting the future price of the S&P500: text mining, which uses NLP techniques to extract meaningful insights from financial news, and technical analysis, which uses historical price and volume data to make predictions. The study examines the advantages and limitations of both methods and analyze their performance in predicting the S&P500. The FinBERT model outperforms other models in terms of S&P500 price prediction, as evidenced by its lower RMSE value, and has the potential to revolutionize financial analysis and prediction using financial news data. Keywords: ARIMA, BERT, FinBERT, Forecasting Financial Time Series, GARCH, LSTM, Technical Analysis, Text Mining JEL classifications: G4, C8

Leland Bybee

I introduce a survey of economic expectations formed by querying a large language model (LLM)'s expectations of various financial and macroeconomic variables based on a sample of news articles from the Wall Street Journal between 1984 and 2021. I find the resulting expectations closely match existing surveys including the Survey of Professional Forecasters (SPF), the American Association of Individual Investors, and the Duke CFO Survey. Importantly, I document that LLM based expectations match many of the deviations from full-information rational expectations exhibited in these existing survey series. The LLM's macroeconomic expectations exhibit under-reaction commonly found in consensus SPF forecasts. Additionally, its return expectations are extrapolative, disconnected from objective measures of expected returns, and negatively correlated with future realized returns. Finally, using a sample of articles outside of the LLM's training period I find that the correlation with existing survey measures persists -- indicating these results do not reflect memorization but generalization on the part of the LLM. My results provide evidence for the potential of LLMs to help us better understand human beliefs and navigate possible models of nonrational expectations.

Tania Masha, Astika Widy Utomo, Martha Ardiaria et al.

ABSTRACT Background: Diabetes mellitus is often associated with the occurrence of complications. Haemostatic factors, especially hyperfibrinogenaemia, is a common cause of the complication. Goat kefir and vitamin D3 may act as an antioxidant and anti-inflammation agent which can repair pancreatic beta cells. Objectives: This study aimed to analyse the effect of vitamin D3-fortified goat milk and plasma fibrinogen levels in diabetic rats. Materials and Methods: This study was an experimental study with pre-post only group design. The samples were 21 male rats divided into four groups; negative control (K-), positive control (K +), treated with unfortified goat kefir (P1), and treated with vitamin D3-fortified goat kefir (P2). The 35-day intervention was conducted, the goat kefir dose was 2 ml/200 g BW/day and the vitamin D dose 600 IU. Fasting blood glucose and plasma fibrinogen were assessed pre- and post-intervention. Blood glucose level was evaluated by GOD-PAP method, while plasma fibrinogen was assessed by Enzyme-Linked Immunosorbent Assay (ELISA) method. The data were analysed with paired t-test and One-Way ANOVA. Results: There were not significant difference levels of fibrinogen between groups. The intervention groups both showed an insignificant decrease of plasma fibrinogen. The plasma fibrinogen of group treated with vitamin D3-fortified goat kefir went down to 13.47 mg/dl from 16.49 mg/dl (p = 0.49). Meanwhile, the group treated with unfortified goat kefir showed a decrease from 26.81 mg/dl to 24.94 mg/dl (p=0.83). On the other hand, there was a significant decrease in fasting blood glucose in the group treated with vitamin D3-fortified goat kefir from 181.75 mg/dl to 116.25 mg/dl (p=0.03). Conclusion Our results demonstrate that administration of vitamin D3-fortified goat kefir can decrease fasting blood glucose but not in plasma fibrinogen.  Keywords : Diabetes Mellitus; Fastin blood glucose; Fibrinogen; Goat kefir; Vitamin D3 Fortification