Hasil untuk "Commercial geography. Economic geography"

Aditya Humnabadkar

Network analysis of inter-industry payment flows reveals structural economic relationships invisible to traditional bilateral measurement approaches, with significant implications for real-time economic monitoring. Analysing 532,346 UK payment records (2017--2024) across 89 industry sectors, we demonstrate that graph-theoretic features which include centrality measures and clustering coefficients improve payment flow forecasting by 8.8 percentage points beyond traditional time-series methods. Critically, network features prove most valuable during economic disruptions: during the COVID-19 pandemic, when traditional forecasting accuracy collapsed (R2} falling from 0.38 to 0.19), network-enhanced models maintained substantially better performance, with network contributions reaching +13.8 percentage points. The analysis identifies Financial Services, Wholesale Trade, and Professional Services as structurally central industries whose network positions indicate systemic importance beyond their transaction volumes. Network density increased 12.5\% over the sample period, with visible disruption during 2020 followed by recovery exceeding pre-pandemic integration levels. These findings suggest payment network monitoring could enhance official statistics production by providing leading indicators of structural economic change and improving nowcasting accuracy during periods when traditional temporal patterns prove unreliable.

Oleg V. Pavlov, Natalia V. Smirnova, Elena V. Smirnova

A literacy-targeted approach to economic instruction draws on insights from cognitive science. It highlights that students process complex economic information by constructing and modifying schemas that represent economic material. Following this approach, we developed a set of instructional activities centered around causal diagrams that promote a deeper understanding of economic topics beyond the traditional lecture-based methods. Our results show that structural debriefing activities can be used effectively to introduce students to the causal diagrams that explain key economic relationships in the national income model, government-purchases multiplier and tax multiplier.

Yingche Liu, Mengyang Li

The massive digital footprints generated by bike-sharing systems in megacities like Shanghai offer a novel perspective on the urban socio-economic fabric. This study investigates whether these daily mobility patterns can quantitatively map the city's underlying social stratification. To overcome the persistent challenge of acquiring fine-grained socio-economic data, we constructed a multi-layered analytical dataset. We annotated 2,000 raw bike trips with local economic attributes, derived from a novel data enrichment methodology that employs a Large Language Model (LLM), and integrated contextual features of the built environment. A Random Forest model was then utilized as an interpretable framework to determine the key factors governing the relationship between mobility behavior and local economic status. The analysis reveals a compelling and unambiguous finding: a neighborhood's economic level, proxied by housing prices, is the single most dominant predictor of its bike-sharing patterns, substantially outweighing other geographic or temporal factors. This economic determinism manifests in three distinct ways: (1) a spatial clustering of resources, a phenomenon we term the \textit{club effect}, which concentrates mobility infrastructure and usage in affluent areas; (2) a functional dichotomy between necessity-driven, utilitarian usage in lower-income zones and flexible, recreational usage in wealthier ones; and (3) a nuanced inverted U-shaped adoption curve that identifies the urban middle class as the system's primary user base.

Ali Zeytoon-Nejad

This paper presents a novel quantitative approach for comparative economic studies, addressing limitations in current classification methods. Conventional approaches in comparative economics often rely on ad hoc and categorical classifications, leading to subjective judgments and disregarding the continuous nature of the spectrum of economic systems. These can result in subjectivity and significant information loss, particularly for countries with systems near categorical borders. To overcome these shortcomings, the present paper proposes distance-based indices for objective categorization, considering economic foundations and using hard data. Accordingly, the paper introduces institutional similarity indices--Capitalism Similarity Index (CapSI), Communism Similarity Index (ComSI), and Socialism Similarity Index (SocSI)-which reflect countries' positions along the economic system continuum. These indices adhere to mathematical rigor and are grounded in the mathematical fields of real analysis, metric spaces, and distance functions. By classifying 135 countries and creating GIS maps, the practical applicability of the proposed approach is demonstrated. Results show a high explanatory power of the introduced indices, suggesting their beneficial usage in comparative economic studies. The paper advocates for their adoption due to their objectivity and ability to capture structural and institutional nuances without subjective judgments while also considering the continuous nature of the spectrum of economic systems.

Yiqin Wang, Chong Han

The phenomenon that multi-path components (MPCs) arrive in clusters has been verified by channel measurements, and is widely adopted by cluster-based channel models. As a crucial intermediate processing step, MPC clustering bridges raw data in channel measurement and cluster characteristics for channel modeling. In this paper, a physical-interpretable and self-adaptive MPC clustering algorithm is proposed, which can locate both single-point and wide-spread scatterers without prior knowledge. Inspired by the concept in geography, a novel metaphor that interprets features of MPC attributes in the power-delay-angle profile (PDAP) as topographic concepts is developed. In light of the interpretation, the proposed algorithm disassembles the PDAP by constructing contour lines and identifying characteristic points that indicate the skeleton of MPC clusters, which are fitted by analytical models that associate MPCs with physical scatterer locations. Besides, a new clustering performance index, the power gradient consistency index, is proposed. Calculated as the weighted Spearman correlation coefficient between the power and the distance to the center, the index captures the intrinsic property of MPC clusters that the dominant high-power path is surrounded by lower-power paths. The performance of the proposed algorithm is analyzed and compared with the counterparts of conventional clustering algorithms based on the channel measurement conducted in an outdoor scenario. The proposed algorithm performs better in average Silhouette index and weighted Spearman correlation coefficient, and the average root mean square error (RMSE) of the estimated scatterer location is 0.1 m.

Fabiana Rocha, Paula Pereda, Maria Dolores Montoya Diaz et al.

The leaky pipeline remains a persistent challenge to achieving gender diversity in the economics career. In this study, we contribute to the existing literature by investigating gender differences in academic performance in economics in Brazil in two distinct stages: undergraduate studies and graduate admission exams. We use individual-level data from the national admission exam for economics graduate programs (ANPEC exam) and undergraduate records from the University of São Paulo. Women are less likely to rank among the top 100 ANPEC applicants and perform worse than men in all exam subjects. Meanwhile, we find consistent evidence that female students perform similarly to their male counterparts in undergraduate courses with comparable content to those evaluated on the ANPEC exam. Since the students taking the ANPEC exam were exposed to the same higher education program, after controlling for observable characteristics, we can relate the differences in performance to the exam itself rather than to differences in learning abilities. While we cannot identify the source of the performance gap, as the ANPEC exam and undergraduate grading system vary in terms of stakes, grading scheme, risk, and competitiveness (all of which can potentially affect women and men differently), we argue that our evidence suggests the need to reconsider admission exam designs to address the leaky pipeline in economics.

Christian Skafte Beck Clausen, Bo Nørregaard Jørgensen, Zheng Grace Ma

Facing economic challenges due to the diverse objectives of businesses, and consumers, commercial greenhouses strive to minimize energy costs while addressing CO2 emissions. This scenario is intensified by rising energy costs and the global imperative to curtail CO2 emissions. To address these dynamic economic challenges, this paper proposes an architectural design for an energy economic dispatch testbed for commercial greenhouses. Utilizing the Attribute-Driven De-sign method, core architectural components of a software-in-the-loop testbed are proposed which emphasizes modularity and careful consideration of the multi-objective optimization problem. This approach extends prior research by implementing a modular multi-objective optimization framework in Java. The results demonstrate the successful integration of the CO2 reduction objective within the modular architecture with minimal effort. The multi-objective optimization output can also be employed to examine cost and CO2 objectives, ultimately serving as a valuable decision-support tool. The novel testbed architecture and a modular approach can tackle the multi-objective optimization problem and enable commercial greenhouses to navigate the intricate landscape of energy cost and CO2 emissions management.

Manuel Tonneau, Diyi Liu, Samuel Fraiberger et al.

Perceptions of hate can vary greatly across cultural contexts. Hate speech (HS) datasets, however, have traditionally been developed by language. This hides potential cultural biases, as one language may be spoken in different countries home to different cultures. In this work, we evaluate cultural bias in HS datasets by leveraging two interrelated cultural proxies: language and geography. We conduct a systematic survey of HS datasets in eight languages and confirm past findings on their English-language bias, but also show that this bias has been steadily decreasing in the past few years. For three geographically-widespread languages -- English, Arabic and Spanish -- we then leverage geographical metadata from tweets to approximate geo-cultural contexts by pairing language and country information. We find that HS datasets for these languages exhibit a strong geo-cultural bias, largely overrepresenting a handful of countries (e.g., US and UK for English) relative to their prominence in both the broader social media population and the general population speaking these languages. Based on these findings, we formulate recommendations for the creation of future HS datasets.

Clement E. Bohr, Marti Mestieri, Frederic Robert-Nicoud

As countries develop, the relative importance of agriculture declines and economic activity becomes spatially concentrated. We develop a model integrating structural change and regional disparities to jointly capture these phenomena. A key modeling innovation ensuring analytical tractability is the introduction of non-homothetic Cobb-Douglas preferences, which are characterized by constant unitary elasticity of substitution and non-constant income elasticity. As labor productivity increases over time, economic well-being rises, leading to a declining expenditure share on agricultural goods. Labor reallocates away from agriculture, and industry concentrates spatially, further increasing aggregate productivity: structural change and regional disparities are two mutually reinforcing outcomes and propagators of the growth process.

DĂNĂCICĂ DANIELA-EMANUELA

The aim of this paper is to present an empirical analysis of unemployment spells and exit states of the Roma population living in Romania. A dataset of unemployment spells of Roma individuals with ISCED4, ISCED5 and ISCED7 level of education, registered as unemployed at the National Agency of Employment Romania, during January 1 st 2014 until 31st December 2017 is analysed. Personal characteristics such as age, gender, education, urban/rural area of living, county and region, previous work experience, reason for leaving unemployment are analysed in association with unemployment duration and exit destinations. The obtained results can be useful for policy makers.

COJOCARU ANDREI VALENTIN, NACHE CIMPOERU MARIA, CĂLIN OANA ALEXANDRA

The ongoing evolution and rapid transformations occurring in the labor market, influenced by economic progress, are leading to the emergence of novel job roles and substantial modifications in conventional occupations. These changes underscore the importance of adapting educational systems to anticipate the future demands of the labor market, ensuring seamless transitions for individuals from educational institutions to the workforce. Education and employment are interdependent. On one hand, the European Union (EU) and its member states require an effective education system that equips individuals with skills aligned with labor market demands. However, it is also imperative to provide a proficient and inventive workforce market that amplifies efficiency while fostering individual growth and societal integration within the population. The key dimensions frequently analyzed to characterize the state of the education market and evaluate its implications on the labor market include the demand for education (participation rates in education), characteristics of the education supply (financial aid for tertiary education), and outcomes of the educational system (early school leavers, tertiary graduates, youth not in employment, education, or training). To gather relevant information, the statistical database Eurostat, Tempo-online (National Institute of Statistics), as well as data provided by the European Commission, the Council of the Union, and the European Parliament were utilized.

Farizka Shafa Nabila, Mahendra Fakhri, Mahir Pradana et al.

Abstract People in Indonesia, particularly members of Generation Z, frequently struggle to manage their financial situation both now and in the future. The problem is brought on by a lack of understanding of financial investments. The purpose of this study is to ascertain the financial standing of Generation Z. A questionnaire with 100 respondents was employed in this investigation. In this study, financial attitudes serve as the independent variable, financial management serves as the intervention variable, and financial satisfaction serves as the dependent variable. A Likert scale was utilized as the measurement in the quantitative research technique. In this work, structural equation modeling (SEM) and SmartPLS software were utilized to process the data. The financial attitude variable has a positive and significant impact on financial happiness that is mediated by financial management. We also offer some recommendations and future research directions related to this topic.

Laura Gustafson, Megan Richards, Melissa Hall et al.

Despite impressive advances in object-recognition, deep learning systems' performance degrades significantly across geographies and lower income levels raising pressing concerns of inequity. Addressing such performance gaps remains a challenge, as little is understood about why performance degrades across incomes or geographies. We take a step in this direction by annotating images from Dollar Street, a popular benchmark of geographically and economically diverse images, labeling each image with factors such as color, shape, and background. These annotations unlock a new granular view into how objects differ across incomes and regions. We then use these object differences to pinpoint model vulnerabilities across incomes and regions. We study a range of modern vision models, finding that performance disparities are most associated with differences in texture, occlusion, and images with darker lighting. We illustrate how insights from our factor labels can surface mitigations to improve models' performance disparities. As an example, we show that mitigating a model's vulnerability to texture can improve performance on the lower income level. We release all the factor annotations along with an interactive dashboard to facilitate research into more equitable vision systems.

DOBRE ALEXANDRU CRISTIAN

The global trend of business is to undergo changes that help the environment and society as a whole. With the current state of affairs, CSR has been widely implemented in different types of entrepreneurial activities. The CSR strategies have been important for stakeholders and employees alike. Operations management has also gone through changes in the last decade and is also implementing CSR strategies. Governments slowly implement new strategies in rapport to country development. Through ethical and well thought decisions, CSR can be an impactful strategy for sustainable development and innovation management in a greener environment.

Houssame BENFRIHA

تهدف هذه الدراسة إلى تحديد الفرص االستثمارية المتاحة في بورصة ماليزيا من خالل اختبار كفاءة السوق المالي اإلسالمي والتقليدي عند مستوى ضعيف، ومن خالل دراسةدرجةالتكامل فيما بينها  للفترة الممتدة من 7102 إلى غاية .7102 حيث خلصت الدراسة إلى كفاءة المؤشرات المدروسة عند المستوى الضعيف وأنه اليمكن للمستثمرين تحقيق مكاسب غير عاديةباالستفادة من البيانات التاريخية، كما أظهرت النتائج أن جميع المؤشرات المدروسة متكاملة فقط على المدى القصير و بالتالي وجود فرص للتنويع االستثماري علىالمدى الطويل This study aims to identify investment opportunities in Bursa Malaysia through testing the weak form efficiency of Islamic and conventional financial markets, and through testing the integration between them using Johansen co-integration test and the Granger  causality test for 2017 and 2018. This study demonstrates that indices are efficient, and investors can not realize gains above average  using historical data. Results show also that indices are co-integrated only in the short-term, this leads us to say that there are opportunities for investments diversification in the long-term. 

Larisa Kargina, Mattia Masolletti

The authors of the article analyze the content of the Eurasian integration, from the initial initiative to the modern Eurasian Economic Union, paying attention to the factors that led to the transition from the Customs Union and the Single Economic Space to a stronger integration association. The main method of research is historical and legal analysis.

Matheus E. Leusin, Bjoern Jindra, Daniel S. Hain

This paper draws upon the evolutionary concepts of technological relatedness and knowledge complexity to enhance our understanding of the long-term evolution of Artificial Intelligence (AI). We reveal corresponding patterns in the emergence of AI - globally and in the context of specific geographies of the US, Japan, South Korea, and China. We argue that AI emergence is associated with increasing related variety due to knowledge commonalities as well as increasing complexity. We use patent-based indicators for the period between 1974-2018 to analyse the evolution of AI's global technological space, to identify its technological core as well as changes to its overall relatedness and knowledge complexity. At the national level, we also measure countries' overall specialisations against AI-specific ones. At the global level, we find increasing overall relatedness and complexity of AI. However, for the technological core of AI, which has been stable over time, we find decreasing related variety and increasing complexity. This evidence points out that AI innovations related to core technologies are becoming increasingly distinct from each other. At the country level, we find that the US and Japan have been increasing the overall relatedness of their innovations. The opposite is the case for China and South Korea, which we associate with the fact that these countries are overall less technologically developed than the US and Japan. Finally, we observe a stable increasing overall complexity for all countries apart from China, which we explain by the focus of this country in technologies not strongly linked to AI.

Griffin Mooers, Mike Pritchard, Tom Beucler et al.

We explore the potential of feed-forward deep neural networks (DNNs) for emulating cloud superparameterization in realistic geography, using offline fits to data from the Super Parameterized Community Atmospheric Model. To identify the network architecture of greatest skill, we formally optimize hyperparameters using ~250 trials. Our DNN explains over 70 percent of the temporal variance at the 15-minute sampling scale throughout the mid-to-upper troposphere. Autocorrelation timescale analysis compared against DNN skill suggests the less good fit in the tropical, marine boundary layer is driven by neural network difficulty emulating fast, stochastic signals in convection. However, spectral analysis in the temporal domain indicates skillful emulation of signals on diurnal to synoptic scales. A close look at the diurnal cycle reveals correct emulation of land-sea contrasts and vertical structure in the heating and moistening fields, but some distortion of precipitation. Sensitivity tests targeting precipitation skill reveal complementary effects of adding positive constraints vs. hyperparameter tuning, motivating the use of both in the future. A first attempt to force an offline land model with DNN emulated atmospheric fields produces reassuring results further supporting neural network emulation viability in real-geography settings. Overall, the fit skill is competitive with recent attempts by sophisticated Residual and Convolutional Neural Network architectures trained on added information, including memory of past states. Our results confirm the parameterizability of superparameterized convection with continents through machine learning and we highlight advantages of casting this problem locally in space and time for accurate emulation and hopefully quick implementation of hybrid climate models.

Khaled BENAMOR, Djillali BOURZAMA, Youcef BOUDELLA

تهدف هذه الدراسة إلى الإلمام بالإطار العام للإدارة الإلكترونية وكشف تجارب تطبيقها لدى منظمات الأعمال في الجزائر، كما تهدف أيضا الى إبراز الأثر الميداني لتطبيق أسلوب الإدارة الإلكترونية على عصرنة بيئة منظمات الأعمال الجزائرية وتطوير أدائهم الوظيفي، وقد توصلت هذه الدراسة أن الواقع يعكس تأخرا بفجوة تكنولوجية في تجسيد الإدارة الإلكترونية لدى منظمات الأعمال، كما توصلت أن الإدارة الإلكترونية بإمكانها أن تساهم في عصرنة البيئة الداخلية لمنظمات الأعمال في الجزائر من خلال تطوير كفاءة الموارد البشرية في التعامل الإلكتروني وتعزيز فرص الذكاء الإقتصادي و تشجيع الإبداع لديها بالإضافة إلى تطوير الأداء الإداري لدى منظمات الأعمال في الجزائر كما يمكن أن تساهم أيضا في عصرنة بيئتها الخارجية من خلال عصرنة سبل التواصل بين منظمات الأعمال وزبائنها وتسهيل الإندماج في مشروع الحكومة الإلكترونية في الجزائر.    This study aims at understanding the general framework of e-management and revealing the reality of its application by business organizations in Algeria. It also aims at highlighting the practical impact of e-management method application on modernizing the environment of Algerian business organizations and improving their functional performance. The study concludes that the reality reflects a technological gap delay in the embodiment of e-management in business organizations. It also concludes that e-management can contribute to modernizing the internal environment of business organizations in Algeria by developing the efficiency of human resources in electronic dealing, enhancing economic intelligence opportunities and encouraging their creativity, as well as developing the administrative performance of Algerian business organizations and eventually contributing to their external environment modernization by updating communication channels between business organizations and their customers and facilitating the integration into the e-government project in Algeria

John M. Abowd, Ian M. Schmutte

Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S.\ statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy.