Hasil untuk "Commercial geography. Economic geography"

MUNTEANU VERONICA, MIHAILA SVETLANA

This article examines the practice of accounting expertise from the perspective of methodological quality and proposes measures to increase the reliability and comparability of the conclusions drawn by experts. The research methodology combines a systematic documentary analysis of the regulatory and professional framework, a comparative analysis of regulations and practices in the Republic of Moldova and Romania, and a case study of an accounting expertise report used to map the standard report structure. The results highlight the main operational challenges: imprecise definition of the accounting expert's tasks, incomplete or delayed documentation, inconsistency of procedures between experts, absence of a materiality threshold adapted to the specific nature of the expertise, risks to independence, lack of technical review, and limited use of digital solutions. To solve these problems, the paper suggests standardizing the accounting expertise process, setting a materiality threshold, and, where it makes sense, making technical reviews a formal part of quality assurance, without limiting professional judgment. Complementary to this, it is recommended that the process be digitized through electronic registers of expert reports, secure data exchange mechanisms, and activity logging, measures designed to reduce subjective variability, increase traceability, and align accounting expertise with European quality standards. In this way, accounting expertise consolidates its evidentiary role in an increasingly complex judicial and fiscal environment.

Pranjal Rawat

This survey (re)introduces reinforcement learning methods to economists. The curse of dimensionality limits how far exact dynamic programming can be effectively applied, forcing us to rely on suitably "small" problems or our ability to convert "big" problems into smaller ones. While this reduction has been sufficient for many classical applications, a growing class of economic models resists such reduction. Reinforcement learning algorithms offer a natural, sample-based extension of dynamic programming, extending tractability to problems with high-dimensional states, continuous actions, and strategic interactions. I review the theory connecting classical planning to modern learning algorithms and demonstrate their mechanics through simulated examples in pricing, inventory control, strategic games, and preference elicitation. I also examine the practical vulnerabilities of these algorithms, noting their brittleness, sample inefficiency, sensitivity to hyperparameters, and the absence of global convergence guarantees outside of tabular settings. The successes of reinforcement learning remain strictly bounded by these constraints, as well as a reliance on accurate simulators. When guided by economic structure, reinforcement learning provides a remarkably flexible framework. It stands as an imperfect, but promising, addition to the computational economist's toolkit. A companion survey (Rust and Rawat, 2026b) covers the inverse problem of inferring preferences from observed behavior. All simulation code is publicly available.

Giampiero M. Gallo, Demetrio Lacava, Edoardo Otranto

This paper examines the dynamic relationship between electoral polls and indicators of economic and financial uncertainty during the last two U.S. presidential elections (2020 and 2024). Using daily polling data on Donald Trump and measures such as the Aruoba-Diebold-Scotti Business Conditions Index, the 5-year Breakeven Inflation Rate, the Trade Policy Uncertainty index, and the VIX, we estimate conditional correlation models to capture time-varying interactions. The analysis reveals that in 2020, correlations between polls and uncertainty measures were highly dynamic and event-driven, reflecting the influence of exogenous shocks (COVID-19, oil price collapse) and political milestones (primaries, debates). In contrast, during the 2024 campaign, correlations remained close to zero, stable, and largely unresponsive to shocks, suggesting that entrenched polarization and non-economic events (e.g., assassination attempt, candidate changes) muted the economic channel. The study highlights how the interplay between voter sentiment, financial markets, and uncertainty varies across electoral contexts, offering a methodological contribution through the application of Dynamic Conditional Correlation models to political data and policy-relevant insights on the conditions under which economic fundamentals influence electoral dynamics.

Gustavo Bergantiños, Juan D. Moreno-Ternero

Sports are one of the most significant products of the entertainment industry, accounting for a large portion of all television (and even platform) viewing. Consequently, the sale of broadcasting and media rights is the most important source of revenue for professional sports clubs. We survey the economic literature dealing with this issue, with a special emphasis on the crucial problem that arises with the allocation of revenues when they are raised from the collective sale of broadcasting rights.

Edward Ajayi

We introduce AfriEconQA, a specialized benchmark dataset for African economic analysis grounded in a comprehensive corpus of 236 World Bank reports. The task of AfriEconQA is to answer complex economic queries that require high-precision numerical reasoning and temporal disambiguation from specialized institutional documents. The dataset consists of 8,937 curated QA instances, rigorously filtered from a pool of 10018 synthetic questions to ensure high-quality evidence-answer alignment. Each instance is composed of: (1) a question requiring reasoning over economic indicators, (2) the corresponding evidence retrieved from the corpus, (3) a verified ground-truth answer, and (4) source metadata (e.g., URL and publication date) to ensure temporal provenance. AfriEconQA is the first benchmark focused specifically on African economic analysis, providing a unique challenge for Information Retrieval (IR) systems, as the data is largely absent from the pretraining corpora of current Large Language Models (LLMs). We operationalize this dataset through an 11-experiment matrix, benchmarking a zero-shot baseline (GPT-5 Mini) against RAG configurations using GPT-4o and Qwen 32B across five distinct embedding and ranking strategies. Our results demonstrate a severe parametric knowledge gap, where zero-shot models fail to answer over 90 percent of queries, and even state-of-the-art RAG pipelines struggle to achieve high precision. This confirms AfriEconQA as a robust and challenging benchmark for the next generation of domain-specific IR and RAG systems. The AfriEconQA dataset and code will be made publicly available upon publication.

Leonardo Puehler, André Felipe Danelon

Este estudo investiga disparidades salariais para 6,34 milhões de trabalhadores da cidade de São Paulo (Brasil) durante os anos de 2018 a 2020, utilizando métodos de Fronteira Estocástica para estimar mudanças nos níveis de sub-remuneração em diferentes dimensões demográficas. Os resultados indicam que reduzir a lacuna de sub remuneração tem o potencial de resultar em um aumento salarial médio de 1,12 vezes, com uma variação de 1,02 a 5,65 vezes dentro da amostra. Além disso, os resultados destacam níveis mais baixos de sub-remuneração entre trabalhadores brancos e do sexo masculino, enquanto trabalhadores migrantes e aqueles com deficiências relatadas tendem a vivenciar níveis mais altos de sub-remuneração. Além disso, o estudo revela uma tendência de diminuição dos salários potenciais ao longo do tempo, marcada por uma redução acentuada de -19,34% em 2020, principalmente atribuída ao impacto do surto de COVID-19. Simultaneamente, há uma redução notável na média de sub-remuneração, com uma diminuição substancial ocorrendo em 2019. Em conclusão, esta pesquisa avança a literatura existente ao utilizar modelos de Fronteira Estocástica para avaliar a sub-remuneração e fornece insights valiosos sobre as dinâmicas intricadas das disparidades salariais - uma questão crítica que desempenha um papel fundamental no desenvolvimento socioeconômico.

Tariq Eldakruri, Edip Senyurek

This paper investigates the economic feasibility of replacing human labor with robotics and automation in Qatar's manufacturing and service sectors. By analyzing labor costs, productivity gains, and implementation expenses, the study assesses the potential financial impact and return on investment of robotic integration. Results indicate the sectors where automation is economically viable and identify challenges related to workforce adaptation, policy, and infrastructure. These insights provide guidance for policymakers and industry stakeholders considering automation strategies in Qatar.

Kaisei Higeta, Masakatsu Ogawa, Tomoki Murakami et al.

With the advent of the 6G era, Integrated Sensing and Communications (ISAC) has attracted increasing attention. One representative of use cases is crowd flow estimation on outdoor streets. However, most existing studies have focused on indoor environments or vehicles, and demonstrations of outdoor crowd flow estimation using commercial LTE base station remain limited. This study addresses this use case and proposes an analysis of a crowd flow estimation method using Reference Signal Received Power (RSRP) obtained from a commercial LTE base station. Specifically, pedestrian counts derived from a camera-based object recognition algorithm were associated with the variance of RSRP. The features obtained from the variance were quantitatively evaluated by combining a CatBoost regression model with SHapley Additive exPlanations (SHAP) analysis. Through this investigation, we clarified that an optimal variance window size for RSRP is 0.1 to 0.2 seconds and that enlarging the counting area increased the features obtained from the variance of RSRP, for machine learning. Consequently, this study is the first to quantitatively demonstrate the effectiveness of outdoor crowd flow estimation using commercial LTE, while also revealing the characteristic behavior of variance window size and counting area size in feature design.

Shahbaz Hussain

The economic dispatch of generators is a major concern in thermal power plants that governs the share of each generating unit with an objective of minimizing fuel cost by fulfilling load demand. This problem is not as simple as it looks because of system constraints that cannot be neglected practically. Moreover, increased awareness of clean technology imposes another important limit on the emission of pollutants obtained from burning of fossil fuels. Classical optimization methods lack the ability of solving such a complex and multi-objective problem. Hence, various modern artificial intelligence (AI) techniques based on evolution and social behaviour of organisms are being used to solve such problems because they are easier to implement, give accurate results and take less computational time. In this work, a study is done on most of the contemporary basic AI techniques being used in literature for power systems in general and combined economic emission dispatch (CEED) in particular. The dispatch problem is implemented on IEEE 30-bus benchmarked system in MATLAB for different load demands considering all gases (COX, NOX and SOX) using particle swarm optimization (PSO) and genetic algorithm (GA) and their results are compared with each other.

Minghao Han, Xunyuan Yin

Shipboard carbon capture is a promising solution to help reduce carbon emissions in international shipping. In this work, we propose a data-driven dynamic modeling and economic predictive control approach within the Koopman framework. This integrated modeling and control approach is used to achieve safe and energy-efficient process operation of shipboard post-combustion carbon capture plants. Specifically, we propose a deep neural Koopman operator modeling approach, based on which a Koopman model with time-varying model parameters is established. This Koopman model predicts the overall economic operational cost and key system outputs, based on accessible partial state measurements. By leveraging this learned model, a constrained economic predictive control scheme is developed. Despite time-varying parameters involved in the formulated model, the formulated optimization problem associated with the economic predictive control design is convex, and it can be solved efficiently during online control implementations. Extensive tests are conducted on a high-fidelity simulation environment for shipboard post-combustion carbon capture processes. Four ship operational conditions are taken into account. The results show that the proposed method significantly improves the overall economic operational performance and carbon capture rate. Additionally, the proposed method guarantees safe operation by ensuring that hard constraints on the system outputs are satisfied.

Ethan Hartley

This study evaluates large language models as estimable classifiers and clarifies how modeling choices shape downstream measurement error. Revisiting the Economic Policy Uncertainty index, we show that contemporary classifiers substantially outperform dictionary rules, better track human audit assessments, and extend naturally to noisy historical and multilingual news. We use these tools to construct a new nineteenth-century U.S. index from more than 360 million newspaper articles and exploratory cross-country indices with a single multilingual model. Taken together, our results show that LLMs can systematically improve text-derived measures and should be integrated as explicit measurement tools in empirical economics.

Ravi Jagadeesan, Alexander Teytelboym

This paper develops a theory of competitive equilibrium with indivisible goods based entirely on economic conditions on demand. The key idea is to analyze complementarity and substitutability between bundles of goods, rather than merely between goods themselves. This approach allows us to formulate sufficient, and essentially necessary, conditions for equilibrium existence, which unify settings with complements and settings with substitutes. Our analysis has implications for auction design.

Salah Eddine NEBBACHE, Abdelkrim MOKRANI

The paper considers that the internal auditing role in family firms is unique owing to the overlap and ambiguity of roles between the family and the firm. Besides, family firms’ characteristics seem to influence the internal audit role. In this context, internal auditing must effectively deal with the factors leading to conflict in family firms. The study aims to emphasize the interpretation of the internal audit role in family businesses, and the article suggests a specific role for the internal audit that is the trade-off between economic aspirations and socioemotional wealth dimensions. In order to achieve this objective, a questionnaire was prepared and distributed to internal auditors, chief financial officers, and certified public accountants of Algerian family businesses. The results reveal that the cognitive role had the highest mean score (3.04), followed by the trade-off between economic and non-economic goals (2.79) and the disciplinary role (2.64).

Dhruba Lal Pandey, Surendra Kumar Uprety, Nischal Risal

Abstract The focus of this study is to analyze the impact of big five personality traits (proxied by agreeableness, conscientiousness, extraversion, emotional stability, and openness and social support) on social entrepreneurship intention of the students of Tribhuvan University, with the objective to examine the effect of these five personality traits and social support on social entrepreneurship intention as also the moderating effect of gender. Most of the studies focused on the impact of personality traits on social entrepreneurial intention, but ignored the situational factors proxied here by the social support. There are contradictory and contractionary findings while examining impact of big five personality traits on SEI. Most of the studies (Nga & Shamuganathan in Journal of Business Ethics, 95(2), 259–282, 2010; Yusuf & Kamil in Global Journal of Research in Social Sciences, 2(1), 65–73, 2015; Hsu & Wang in Innovations in Education and Teaching International, 56(3), 385–395, 2018; Bernardino et al. in International Journal of Gender and Entrepreneurship, 10(1), 61–82, 2018; and Seyoum et al. in Journal of Small Business and Enterprise Development, 28(3), 337–359, 2021). Similarly, studies on these issues are almost ignored in Nepalese academics and therefore the researchers attempted to assess the impact of big five personality traits on SEI which is new in the Nepalese context. The sample size was determined using Cochran’s (John Wiley & Sons Incorporated, 1977) formula. The data were collected based on five-point Likert scale questionnaire administered personally and online on 385 samples and were analyzed using SMART PLS software. Structure equation modeling was used to examine the impact of the big five personality traits and social support on social entrepreneurship intention and bootstrap multi-group analysis to check the moderating effect of gender. Cronbach Alpha and composite reliability (CR) were used to check reliability, variance inflation factor (VIF) to check multicollinearity, K-S and Shapiro–Wilk test to check the normality of the data, and Fornell and Larcker criterion and HTMT ratio to check the discriminant validity. The study found that all the proxies of big five personality traits and social support positively and significantly impact on social entrepreneurship intention, but gender does not moderate the relationship. The big five personality traits remain one of the major determinants in creating entrepreneurial intention among students. The reason why, university can adopt programs to educate big five personality traits in order to develop entrepreneurial intention among graduate level students. Similarly, social support helps generate entrepreneurial intentions. The study findings confirm the effect of social support in creating entrepreneurial intention and create the scope to use TPB theory in creating entrepreneurial intention. As well, it helps university to develop programs and courses for the creating entrepreneurial intention among graduate level students.

Philip Metzger

Economic parameters are identified for an in-space industry where the capital is made on one planet, it is transported to and teleoperated on a second planet, and the product is transported off the second planet for consumption. This framework is used to model the long-run cost of lunar propellant production to help answer whether it is commercially competitive against propellant launched from Earth. The prior techno-economic analyses (TEAs) of lunar propellant production had disagreed over this. The "gear ratio on cost" for capital transport, G, and the production mass ratio of the capital, phi, are identified as the most important factors determining competitiveness. The prior TEAs are examined for how they handled these two metrics. This identifies crucial mistakes in some of the TEAs: choosing transportation architectures with high G, and neglecting to make choices for the capital that could achieve adequate phi. The tent sublimation technology has a value of phi that is an order of magnitude better than the threshold for competitiveness even in low Earth orbit (LEO). The strip mining technology is closer to the threshold, but technological improvements plus several years of operating experience will improve its competitiveness, according to the model. Objections from members of the aerospace community are discussed, especially the question whether the technology can attain adequate reliability in the lunar environment. The results suggest that lunar propellant production will be commercially viable and that it should lower the cost of doing everything else in space.

Aykut Örs, Cennet OĞUZ, Alexsander SEMIN et al.

The most remarkable technology brought to dairy farms by the digital transformation in agriculture is undoubtedly robotic milking systems (RMS). Knowing the economic impact of this technology is essential for farmers to adopt. For this purpose, in the study; a simulation model was created that gives possible economic analysis results as a result of the use of RMS by using the current economic analysis results of dairy farms. For the economic analysis of dairy farms, data obtained from face-to-face surveys from 148 dairy farms were used. Assumptions used in the simulation model for comparing RMS and conventional milking systems (CMS) were 8.66% increase in milk yield, 58.46% increase in investment costs, 36.66% increase in energy consumption, 1.33% increase in feed costs and 27.84% decrease in labor input. The economic analysis of the dairy farms was made again with these new input and output values obtained. While the simulation results show that the use of RMS is a preferable investment that increases profitability for 10-60 head and 121 + head groups; it shows that it will be an investment that negatively affects profitability for the 61-120 head group. The simulation model was used by taking the average values of the data belonging to the dairy farm groups. A dairy farmer considering an RMS investment can be able to obtain a result specific to his farm if he combines the simulation model with his own economic analysis results.

SIMONA COȘEREA

The events of december 1989 also meant for our country the return to educational pluralism, giving up the exclusive monopoly of the state in the field of education by implementing educational alternatives and establishing private education. Educational alternatives are ways of school organization, which propose forms and methods of organization and functioning of the educational instructive activity, other than the forms specific to an era or which appear in a certain social context. An educational alternative has the role of correcting certain imperfections of the official system, of substituting some forms of education through different or complementary teaching methodologies and of restructuring the organizational and functioning framework of the school institution.

Nadjia BOUKEZZATA, Wahiba KHALFI

تهدف هذه الدراسة إلى معرفة مدى إمكانية تسمية توصيات لجنة بازل بالتشريعات الاحترازية  الدولية مع دراسة حالة الأنظمة الاحترازية في الجزائر، حيث تم التوصل إلى أنه يمكن اعتبار توصيات لجنة بازل تشريعات احترازية دولية، كما يمكن اعتبار الأنظمة الاحترازية الصادرة عن بنك الجزائر تشريعات احترازية، نظرا لاستفاء كل منهما لشروط وأركان صحة التشريع This study aims to know the extent to which the recommendations of the Basel Committee can be called international precautionary legislation which a study of the case of precautionary systems in Algeria, where it was concluded that the recommendations of the Basel committe can be considered international  precautionary legislation, and the precautionary regulations issued by the Bank of Algeria can be considered precautionary legislation, given the fulfillment of each of them to the conditions and pillars of the validity of  the legislation.

Gabriel Cepaluni, M. Dorsch, Daniel Kovarek

Objective: This paper quantitatively explores determinants of governments’ non-pharmaceutical policy responses to the COVID-19 pandemic. Our focus is on the extent to which geographic mobility affected the stringency of governmental policy responses. Methods: Using cross-country, daily frequency data on geographic mobility and COVID-19 policy stringency during 2020, we investigate some of the determinants of policy responses to COVID-19. In order to causally identify the effect of geographic mobility on policy stringency, we pursue an instrumental variable strategy that exploits climate data to identify arguably exogenous variation in geographic mobility. Results: We find that societies that are more geographically mobile have governmental policy responses that are less stringent. Examining disaggregated mobility data, we show that the negative relation between geographic mobility and policy stringency is the stronger for commercially-oriented movements than for geographic movements that relate to civil society. Conclusion: The results suggest that policy-makers are more willing to trade-off public health for economic concerns relative to other civil concerns.

Michael G. Suarez, Leomarich F. Casinillo

This study was conducted in Doos Sur Elementary School, Hindang, Leyte, Philippines within the school year 2017-2018 to assess the effectiveness of Strategic Intervention Material (SIM) on academic performance in science among grade VI students. It utilized the pretest-posttest quasi-experimental design. The SIM used as a treatment of the study covered one of the least mastered skills in the Science VI, that is, describing the appearance and uses of homogeneous and heterogeneous mixtures. The study employed 20 students for control group and 20 students for experimental group enrolled in Science VI during the first quarter. Control group was taught with conventional teaching method and the experimental group was taught with SIM. Through hypothesis testing, this study determined the significant effect of SIM to students’ academic performance. Results of the study showed that the use of SIM is effective in terms of improving students’ performance particularly on the topic pertaining to the least mastered skills in Science VI. This implies that SIM can be utilized as instructional materials during learning process as effective teaching tool.