Hasil untuk "Trade associations"

Xiaojuan He, T. Adebayo, D. Kırıkkaleli et al.

Abstract The main aim of this paper is to explore the impact of financial development and globalization on consumption-based carbon emissions in Mexico while controlling growth, trade openness, and energy consumption. This impact has not been comprehensively explored for the case of Mexico using the newly developed dual adjustment approach. The fundamental innovation of the approach is that it offers an alternative to cointegration analysis, which reduces the implicit assumption of the singular adjustment in cointegration analysis. Furthermore, the study employs an autoregressive distributed lag approach to capture both the long-run and short-run association, while frequency domain causality tests are applied to capture causal linkages among the variables in the short run, medium run and long run. The empirical findings of this study reveal that: (a) globalization and financial development improve the quality of the environment; (b) energy consumption and economic growth deteriorate environmental quality; (c) trade openness exerts no significant impact on environmental quality. The findings from the frequency domain causality test reveal that financial development, energy usage, and economic growth can predict consumption-based carbon emissions at different frequencies, whereas trade openness and globalization can predict significant variations in consumption-based carbon emissions in the long and short term. Based on the empirical findings, the study suggests that the government of Mexico should be careful when formulating policies aimed at increasing growth, as they could be detrimental to the quality of the environment.

Muhammad Zahid Rafique, A. M. Nadeem, Wanjun Xia et al.

The current decade has witnessed the rise of empirical research in the domain of ecological footprint which has become a major scholarly area among environmental researchers. However, many key factors determining ecological footprint have been inadequately dealt within the existing body of knowledge. The current research aims to explore the association between economic complexity, human capital, renewable energy generation, urbanization, economic growth, export quality, trade and ecological footprint for the top ten economic complex countries. This study applied panel data estimators, for instance, fully modified ordinary least squares (FMOLS), dynamic ordinary least squares (DOLS) and the system-GMM long-run estimators from 1980 to 2017. The long-run estimates reveal that economic complexity, economic growth, export quality, trade and urbanization increase ecological footprint. Human capital and renewable energy generation help to mitigate ecological footprint. We conclude that investment in more renewable energy generation and its consumption and efficient use of human capital will improve economic complexity, export quality, and environment in developed and developing countries.

G. Uddin, Mohammad A. Salahuddin, Khorshed Alam et al.

Zhi-Wei Su, Muhammad Umar, D. Kırıkkaleli et al.

Romil Rawat, Anand Rajavat

The limits of user visibility have been exceeded by the internet. The “Dark Web” or “Dark Net” refers to certain unknown portions of the internet that cannot be found using standard search methods. A number of computerised techniques are being explored to extract or crawl the concealed data. All users can freely interact on the surface web. Identity identities may be found on the deep web, and the dark web (DW), a hub for anonymous data, is a haven for terrorists and cybercriminals to promote their ideologies and illegal activities. Officials in clandestine surveillance and cyberpolicing are always trying to track down offenders' trails or hints. The search for DW offenders might take five to ten years.The proposed study provides data from a DW mining and online marketplaces situation from a few domains, as well as an overview for investigators to build an automated engine for scraping all dangerous information from related sites.

Nathan L. DeBono, Vanessa Amar, Hardy Hardy et al.

Abstract Background Industry-funded research poses a threat to the validity of scientific inference on carcinogenic hazards. Scientists require tools to better identify and characterize industry sponsored research across bodies of evidence to reduce the possible influence of industry bias in evidence synthesis reviews. We applied a novel large language model (LLM)-based tool named InfluenceMapper to demonstrate and evaluate its performance in identifying relationships to industry in research on the carcinogenicity of benzene, cobalt, and aspartame. Methods All epidemiological, animal cancer, and mechanistic studies included in systematic reviews on the carcinogenicity of the three agents by the IARC Monographs programme. Selected agents were recently evaluated by the Monographs and are of commercial interest by major industries. InfluenceMapper extracted disclosed entities in study publications and classified up to 40 possible disclosed relationship types between each entity and the study and between each entity and author. A human classified entities as ‘industry or industry-funded’ and assessed relationships with industry for potential conflicts of interest. Positive predictive values described the extent of true positive relationships identified by InfluenceMapper compared to human assessment. Results Analyses included 2,046 studies for all three agents. We identified 320 disclosed industry or industry-funded entities from InfluenceMapper output that were involved in 770 distinct study-entity and author-entity relationships. For each agent, between 4 and 8% of studies disclosed funding by industry and 1–4% of studies had at least one author who disclosed receiving industry funding directly. Industry trade associations for all three agents funded 22 studies published in 16 journals over a 37-year span. Aside from funding, the most prevalent disclosed relationships with industry were receiving data, holding employment, paid consulting, and providing expert testimony. Positive predictive values were excellent (≥ 98%) for study-entity relationships but declined for relationships with individual authors. Conclusions LLM-based tools can significantly expedite and bolster the detection of disclosed conflicts of interest from industry sponsored research in cancer prevention. Possible use cases include facilitating the assessment of bias from industry studies in evidence synthesis reviews and alerting scientists to the influence of industry on scientific inference. Persistent challenges in ascertaining conflicts of interest underscore the urgent need for standardized, transparent, and enforceable disclosures in biomedical journals.

Bowen Wang, Zhongshan Yue, Jian Yao

This study examines the associations between migration patterns and consequences of economic and non-economic among China's internal migrants. Through Latent Class Analysis, we identify three migration patterns: Individual Short-Term Labor Migration (ISTL), First-Time Family Migration (FFM), and Multiple-Step Family Migration (MFM). These patterns emerge consistently across both rural-to-urban and urban-to-urban migration flows, yet demonstrate systematically different associations with migrant outcomes. Our analysis reveals that ISTL migration is associated with significantly higher income levels but lower sense of belonging, whereas FFM migration shows the opposite pattern, being associated with reduced income but enhanced sense of belonging. MFM represents an intermediate strategy balancing both objectives. Notably, among rural ISTL migrants, higher income appears to partially offset the negative association between individual migration and sense of belonging, suggesting complex interaction effects. The findings illuminate systematic trade-offs between economic gains and social integration in China's internal migration system, demonstrating that migrants face strategic choices between maximizing immediate economic returns and fostering long-term social attachments. These patterns persist even after controlling for demographic characteristics, hukou status, and destination city factors, indicating that migration strategies themselves play a crucial role in shaping migration outcomes. The research contributes to migration theory by revealing how different migration patterns create different opportunity structures for economic and social advancement, with important implications for understanding migrant adaptation strategies and designing targeted policy interventions that address the diverse needs of China's internal migrants.

Tomasz Grzyb, Sylwia Kulczyk

This research offers an integrated look at people’s attitudes towards urban riverscapes. Intentional interactions with nature are important for urban residents as they influence visitors’ physical and mental well-being through multiple comfort and discomfort agents. Positive effects—usually framed as cultural ecosystem services—are often emphasised. However, it is also important to consider negative aspects, such as disturbing and unpleasant nature-related phenomena (often conceptualised as ecosystem disservices) or man-induced negative effects. This study employs the results of a representative survey to identify interconnections between well-being agents in the context of the Vistula River in Warsaw, Poland. At the citywide scale, respondents recalled what made their visits comfortable or uncomfortable. Then, they chose up to five of the most significant well-being agents, mapped them and enriched with visitation preferences (analysis at the local scale). While factor analysis clearly distinguished comfort and discomfort agents in the citywide scale, local-scale associations are more complex. Four key ways to influence visitors’ well-being revolved around (1) emotional attachment to the river and both benefits and drawbacks of riverine nature; (2) nature-related discomfort and lacks of amenities; (3) pros and cons of social interactions, and (4) sport and creativity opportunities and safety concerns. They are significantly linked to the characteristics of the visited places and time preferences of visits, with broadness of view and landscape homogeneity level as variables impacting all of them. Understanding synergies and/or trade-offs between well-being agents, along with identifying their environmental drivers, may foster the sustainable management of urban natural spaces.

Eid Ibrahim Daud, Mousse Abdi Mohamoud, Jama Mohamed et al.

Alleviating poverty stands as one of the paramount global challenges, particularly in regions like Latin America, where socioeconomic disparities persist. Therefore, this study aims to investigate the relationship between Foreign Direct Investment (FDI) and poverty reduction in 14 Latin American countries from 1992 to 2022. Utilizing panel quantile regression analysis, we explored the nuanced relationships between key economic factors and poverty levels across different income quantiles in the region. Our findings consistently demonstrate that higher FDI levels are associated with lower poverty rates across all income groups, highlighting the potential of FDI as a driver of poverty reduction. We also uncover negative associations between poverty and factors such as trade openness and labor participation, while income inequality emerges as a significant contributor to poverty levels. In light of these findings, we propose several policy implications, including measures to attract FDI through tax incentives and infrastructure development; promote trade openness and labor participation; mitigate income inequality through progressive taxation and social safety nets; and prioritize targeted investment strategies in sectors benefitting lower-income groups, such as education and healthcare.

Xiaoting Kuang, Boken Lin

Large Language Models (LLMs) excel at understanding context and qualitative nuances but struggle with the rigorous and transparent reasoning required in high-stakes quantitative domains such as financial trading. We propose a model-first hybrid architecture for the options "wheel" strategy that combines the strengths of LLMs with the robustness of a Bayesian Network. Rather than using the LLM as a black-box decision-maker, we employ it as an intelligent model builder. For each trade decision, the LLM constructs a context-specific Bayesian network by interpreting current market conditions, including prices, volatility, trends, and news, and hypothesizing relationships among key variables. The LLM also selects relevant historical data from an 18.75-year, 8,919-trade dataset to populate the network's conditional probability tables. This selection focuses on scenarios analogous to the present context. The instantiated Bayesian network then performs transparent probabilistic inference, producing explicit probability distributions and risk metrics to support decision-making. A feedback loop enables the LLM to analyze trade outcomes and iteratively refine subsequent network structures and data selection, learning from both successes and failures. Empirically, our hybrid system demonstrates effective performance on the wheel strategy. Over nearly 19 years of out-of-sample testing, it achieves a 15.3% annualized return with significantly superior risk-adjusted performance (Sharpe ratio 1.08 versus 0.62 for market benchmarks) and dramatically lower drawdown (-8.2% versus -60%) while maintaining a 0% assignment rate through strategic option rolling. Crucially, each trade decision is fully explainable, involving on average 27 recorded decision factors (e.g., volatility level, option premium, risk indicators, market context).

Hayato Kato, Kensuke Suzuki, Motoaki Takahashi

We study how tariffs affect industrial structure and welfare in an economy where sectors are complements and preferences are nonhomothetic -- two drivers of structural change. Import tariffs on a sector influence sectoral composition by affecting its price relative to other sectors and national income, as well as the sector's net exports. We qualitatively characterize these mechanisms and use a quantitative dynamic model to show that a counterfactual 20-percentage-point increase in U.S. manufacturing tariffs would have raised the manufacturing value-added share by one percentage point and increased welfare by 0.41 percent. If trading partners retaliated, welfare would have fallen.

Pankaj K Agarwal, H K Pradhan, Konark Saxena

This study examines active liquidity management by Indian open-ended equity mutual funds. We find that fund managers respond to inflows by increasing cash holdings, which are later used to purchase less-liquid stocks at favourable valuations. Funds with less liquid portfolios tend to maintain larger cash reserves to manage flows. Funds that make active liquidity choices yield statistically and economically significant gross and net returns. The performance differences between funds with varying activeness in altering liquidity highlight the importance of active liquidity management in markets with substantial cross-sectional liquidity differences such as India.

Ad Knotter

Magdalena López

This article analyzes Paraguayan economic elites and their political practices, based on the study of three livestock agricultural sector business organizations: the Union of Production Trade Associations, the Rural Association of Paraguay, and the Paraguayan Chamber of Exporters and Marketers of Cereals and Oilseeds. The article examines the political profile of the groups, whose original capital was economic, with the goal of shedding light on their strategies and mechanisms of politicization within the sector (initially organizing in business associations aiming to gain political influence) and outside the sector (in relation to governments and political parties). Este artículo analiza a las élites económicas paraguayas y la construcción de sus prácticas políticas, a partir del estudio de tres organizaciones empresariales del sector agropecuario-ganadero: la Unión de Gremios de la Producción, la Asociación Rural del Paraguay y la Cámara Paraguaya de Exportadores y Comercializadores de Cereales y Oleaginosas. El artículo examina el perfil político de estos grupos, cuyo capital inicial fue de carácter económico, con el objetivo de arrojar luz sobre sus estrategias y mecanismos de politización tanto dentro del sector (organización inicial en gremios empresariales con fines de influencia política) como fuera de él (en relación con los gobiernos y los partidos políticos).

Varga Rada, Crizbășan Cristina

Natives of the Low Rhine region, the Batavi were a community of ethnic soldiers engaged in a treaty with Rome, overseeing a heavy supply of men to the Roman auxiliary units. The present article examines the pottery discovered at and retrieved from three sites associated with Batavian auxilia: Războieni, Adony, and Romita. Their status as military sites shaped around the consumption habits of Batavian auxiliary troops makes them comparable. The comprehensive examination of pottery consumption patterns provides valuable insights into the dynamics of material culture in Dacia and Pannonia, facilitating our understanding of supply networks, consumption preferences, and cultural interactions within these frontier regions. The quantitative analyses of fabric and form distributions reveal distinctive trends at each site, reflecting variations in local production, regional trade networks, and social practices. The qualitative interpretations highlight the significance of certain vessel types and their cultural associations. While in the overall perspective, they emphasise the similarity between pottery consumption at the three forts shaped by military presence and regional trade networks, they also highlight potential markers of Batavian identity within the auxiliary units stationed there. By contextualising pottery within broader historical and archaeological frameworks, this research contributes to a more nuanced understanding of the cultural aspects of auxilia.

Vitalii V. Venger, Andrii Y. Ramskyi, Natalia I. Romanovska et al.

The article analyzes the current state and prospects of development of exports of domestic industrial products to the rapidly developing countries of Asia, in particular to Indonesia, Thailand, Bangladesh and the Philippines. It is shown that today Ukraine is a full-fledged participant in the globalization process, and its further development will largely depend on the priorities of its foreign economic integration policy, the formation and implementation of which should be based on building strategic relations with both individual Asian countries and various regional associations. It is substantiated that the need to intensify Ukraine’s cooperation with Asian countries is caused not only by the global transformation in the balance of power of the world economy, but also by the need for export diversification of foreign trade in the context of Russian aggression. The analysis shows that by 2022, the demand for domestic industrial products in the markets of Indonesia and Thailand had positive dynamics and ensured, albeit not a significant increase in the export of Ukrainian high-tech goods. Despite the positive trade balance, demand for domestic industrial products in the markets of Bangladesh and the Philippines was characterized by a gradual decline until 2022. In general, the share of domestic industrial products in the structure of exports to these countries is quite low: Indonesia - 0.7%, Thailand - 3.0%, Bangladesh - 4.3%, and the Philippines - 9.9%. At the same time, the share of industrial products from these countries in the structure of Ukrainian imports was much higher, in particular: Indonesia - 20.0%, Thailand - 82.1%, Bangladesh - 96.8%, and the Philippines - 92.4%. Due to the large-scale invasion of Ukraine by Russia, the article estimates the losses in foreign trade between Ukraine and rapidly developing Asian countries in 2022. In particular, it is shown that the volume of foreign trade turnover between Ukraine and Indonesia will decrease by 39.2% compared to 2021, Thailand - by 35.3%, Bangladesh - by 48.6%, and the Philippines - by 38.6%. Based on the analysis of scientific and statistical literature, it is established that the determining instrument of trade policy of the rapidly developing Asian countries is customs tariffs, various preferential regimes and benefits for industrial products in accordance with various bilateral and regional trade agreements. In order to further develop partnership relations between Ukraine and the rapidly developing countries of Asia, it is proposed to intensify and strengthen the dialogue on deepening trade and economic cooperation through the system of trade policy instruments. In terms of further research, additional studies are proposed to determine the prospects for concluding bilateral trade agreements on FTAs between individual countries and developing a long-term strategy for trade and economic cooperation.

Dereje Fedasa Hordofa

Globalization is a complex phenomenon with far-reaching and multidimensional influences on national growth trajectories. Previous studies examining these effects have often relied on overly simplistic measures that mask heterogeneity across contexts. This paper aims to overcome such limitations by disentangling the impacts of different globalization dimensions on Ethiopia’s economic development over 1981–2021 using an autoregressive distributed lag (ARDL) approach in order to assess the long-term and short-term effects of Ethiopia’s economic growth rates and a comprehensive globalization index encompassing the economic, social, and political dimensions of globalization. Long-run and short-run relationships are estimated via ARDL to analyze these relationships in the context of factors like investment, workforce, trade, and capital stock, while the robustness of findings is ensured through dynamic ordinary least squares, fully modified ordinary least squares, and canonical cointegrating regression techniques. Key results indicate that overall and economic globalization exhibit negative long-term associations with growth when analyzing these relationships taking into account other factors, suggesting a need for a cautious, sequenced approach to integration to optimize benefits as local capacities develop over time. However, political globalization carries positive long-run effects. Social globalization displays no clear linkage. Short-run impacts also differ by dimension. Gross capital formation, trade openness, and capital stock remain consistently growth-enhancing. The negative correlation in the labor force likely reflects structural challenges. Robustness analyses substantiate these conclusions, enhancing confidence. Findings imply a cautious, sequenced approach to integration may optimize potential benefits as local adaptive capacities develop over time. This investigation’s tailored nature provides contextually grounded empirical knowledge with broader policy applications. More refinement of country-level analyses remains warranted to fully uncover globalization’s complexity. Overall, incremental global ties alone proved insufficient for development acceleration. Findings carry relevance for managing integration strategies towards inclusive, sustainable development nationally and beyond.

Zi Wang, Xingcheng Xu, Yanqing Yang et al.

We propose a deep learning framework, DL-opt, designed to efficiently solve for optimal policies in quantifiable general equilibrium trade models. DL-opt integrates (i) a nested fixed point (NFXP) formulation of the optimization problem, (ii) automatic implicit differentiation to enhance gradient descent for solving unilateral optimal policies, and (iii) a best-response dynamics approach for finding Nash equilibria. Utilizing DL-opt, we solve for non-cooperative tariffs and industrial subsidies across 7 economies and 44 sectors, incorporating sectoral external economies of scale. Our quantitative analysis reveals significant sectoral heterogeneity in Nash policies: Nash industrial subsidies increase with scale elasticities, whereas Nash tariffs decrease with trade elasticities. Moreover, we show that global dual competition, involving both tariffs and industrial subsidies, results in lower tariffs and higher welfare outcomes compared to a global tariff war. These findings highlight the importance of considering sectoral heterogeneity and policy combinations in understanding global economic competition.

Bas Sanders

Counterfactuals in quantitative trade and spatial models are functions of the current state of the world and the model parameters. Common practice treats the current state of the world as perfectly observed, but there is good reason to believe that it is measured with error. This paper provides tools for quantifying uncertainty about counterfactuals when the current state of the world is measured with error. I recommend an empirical Bayes approach to uncertainty quantification, and show that it is both practical and theoretically justified. I apply the proposed method to the settings in Adao, Costinot, and Donaldson (2017) and Allen and Arkolakis (2022) and find non-trivial uncertainty about counterfactuals.

Haaroon Yousaf, Naomi A. Arnold, Renaud Lambiotte et al.

Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world interacting systems. For this reason, different attempts have been made to enrich the network language, leading to the emerging field of higher-order networks. In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.