Hasil untuk "Economics"

R. Lucas, L. Rapping

W. Landes, R. Posner

Kevin M. Murphy, Finis Welch

W. Becker, David M. Kreps

Ankita Kundu, Istvan Fekete, Sakhi Roy

Abstract Background Tobacco use among women in India remains under-researched despite its growing public health burden and strong sociocultural influences. Understanding its determinants is essential for designing gender-sensitive interventions. Methods This study analysed data from 724,115 women aged 15–49 years from the National Family Health Survey (NFHS-5, 2019–2021). A binary logistic regression model estimated the marginal effects of demographic, socioeconomic, and health-related predictors of tobacco consumption (smoked or smokeless). Results Overall, 6.28% of women reported current tobacco use. Prevalence increased sharply with age, peaking among women aged 45–49 years (+8.14 percentage points relative to those aged 15–19 years). Tobacco use was substantially higher among women with no formal education (+10.91 percentage points), those in the poorest wealth quintile (+11.79 percentage points), and residents of select Northeastern states, where prevalence exceeded 50%. In contrast, higher educational attainment and greater household wealth were associated with lower tobacco use. Women covered by health insurance (+2.04 percentage points) and those reporting chronic health conditions (+1.38 percentage points) exhibited higher tobacco use, suggesting possible behavioural risk compensation and stress-related coping mechanisms. After adjustment for socioeconomic and educational factors, rural residence was associated with a modest reduction in tobacco use. All estimated associations were statistically significant at the 1% level (p < 0.01). Conclusions Female tobacco use in India is shaped by intersecting demographic, economic, and health vulnerabilities, with novel evidence of higher use among insured and chronically ill women. Policy implications Targeted cessation efforts for older, less-educated, and low-income women, integration of tobacco screening into primary care and chronic disease management, and insurance-linked cessation incentives are recommended to address these disparities.

P. Romer

T. Mills

Yingxuan Yang, Ying Wen, Jun Wang et al.

The rise of Large Language Models (LLMs) has transformed AI agents from passive computational tools into autonomous economic actors. This shift marks the emergence of the agent-centric economy, in which agents take on active economic roles-exchanging value, making strategic decisions, and coordinating actions with minimal human oversight. To realize this vision, we propose Agent Exchange (AEX), a specialized auction platform designed to support the dynamics of the AI agent marketplace. AEX offers an optimized infrastructure for agent coordination and economic participation. Inspired by Real-Time Bidding (RTB) systems in online advertising, AEX serves as the central auction engine, facilitating interactions among four ecosystem components: the User-Side Platform (USP), which translates human goals into agent-executable tasks; the Agent-Side Platform (ASP), responsible for capability representation, performance tracking, and optimization; Agent Hubs, which coordinate agent teams and participate in AEX-hosted auctions; and the Data Management Platform (DMP), ensuring secure knowledge sharing and fair value attribution. We outline the design principles and system architecture of AEX, laying the groundwork for agent-based economic infrastructure in future AI ecosystems.

Michael J. Zellinger, Matt Thomson

Practitioners often navigate LLM performance trade-offs by plotting Pareto frontiers of optimal accuracy-cost trade-offs. However, this approach offers no way to compare between LLMs with distinct strengths and weaknesses: for example, a cheap, error-prone model vs a pricey but accurate one. To address this gap, we propose economic evaluation of LLMs. Our framework quantifies the performance trade-off of an LLM as a single number based on the economic constraints of a concrete use case, all expressed in dollars: the cost of making a mistake, the cost of incremental latency, and the cost of abstaining from a query. We apply our economic evaluation framework to compare the performance of reasoning and non-reasoning models on difficult questions from the MATH benchmark, discovering that reasoning models offer better accuracy-cost tradeoffs as soon as the economic cost of a mistake exceeds \$0.01. In addition, we find that single large LLMs often outperform cascades when the cost of making a mistake is as low as \$0.1. Overall, our findings suggest that when automating meaningful human tasks with AI models, practitioners should typically use the most powerful available model, rather than attempt to minimize AI deployment costs, since deployment costs are likely dwarfed by the economic impact of AI errors.

Ege Erdil

We develop a theoretical model that addresses the economic trade-off between cost per token versus serial token generation speed when deploying LLMs for inference at scale. Our model takes into account arithmetic, memory bandwidth, network bandwidth and latency constraints; and optimizes over different parallelism setups and batch sizes to find the ones that optimize serial inference speed at a given cost per token. We use the model to compute Pareto frontiers of serial speed versus cost per token for popular language models.

Alessandro Bellina, Paolo Buttà, Vito D. P. Servedio

Economic complexity algorithms aim to uncover the hidden capabilities that drive economic systems. Here, we present a fundamental reinterpretation of two of these algorithms, the Economic Complexity Index (ECI) and the Economic Fitness and Complexity (EFC), by reformulating them as optimization problems that minimize specific cost functions. We show that ECI computation is equivalent to finding eigenvectors of the network's transition matrix by minimizing the quadratic form associated with the network's Laplacian. For EFC, we derive a novel cost function that exploits the algorithm's intrinsic logarithmic structure and clarifies the role of the regularization parameter in its non-homogeneous version. Additionally, we establish the existence and uniqueness of its solution, providing theoretical foundations for its application. This optimization-based reformulation bridges economic complexity and established frameworks in spectral theory, network science, and optimization. The theoretical insights translate into practical computational advantages: we introduce a conservative, gradient-based update rule that substantially accelerates algorithmic convergence, with potential implications for a broader class of algorithms, including the Sinkhorn-Knopp method. Finally, we apply the energetic framework to a real-world trade network, demonstrating how link-wise energy provides a direct way to identify structurally relevant and vulnerable regions of the export matrix, thus complementing and enriching standard economic complexity analyses. Beyond advancing our theoretical understanding of economic complexity indicators, this work opens new pathways for algorithmic improvements and extends applicability to general network structures beyond traditional bipartite economic networks.

J. M. Buchanan

Richard N. Langlois, N. Foss

We argue that since Coase’s seminal 1937 paper on “The Nature of the Firm,” there has been an odd and unjustified separation between price theory and the economics of organization. For example, matters of production has been the domain of the former exclusively. However, a new approach to economic organization, here called “the capabilities approach,” that places production center-stage in the explanation of economic organization, is now emerging. We discuss the sources of this approach and its relation to the mainstream economics of organization.

Denis Kalua, Andrew Munthopa Lipunga, Fredrick Banda

Research aims: The study investigates the influence of monetary policy on the balance sheet performance of commercial banks in Malawi. Design/Methodology/Approach: The study employed an explanatory research design using time series data obtained from financial reports of commercial banks and economic reports published by the Reserve Bank of Malawi from 2012 to 2022. Regression analyses were conducted to establish the influence of monetary policy on balance sheet performance (loan and overdraft growth). Research findings: The results suggest that the monetary policy instruments, namely, the liquidity reserve requirement (LRR), Lombard rate, policy rate, and open market operations, have insignificant influence on the loan and overdraft growth in commercial banks in Malawi. Theoretical contribution/Originality: This implies that these monetary policy tools are not the exterior determinants of the balance sheet performance of commercial banks in Malawi. Research limitation: The study used a single measure of the balance sheet performance of commercial banks.

F. Diebold, José A. López

P. Lambert

M. Raddant, T. Di Matteo

This is a review about financial dependencies which merges efforts in econophysics and financial economics during the last few years. We focus on the most relevant contributions to the analysis of asset markets' dependencies, especially correlational studies, which in our opinion are beneficial for researchers in both fields. In econophysics, these dependencies can be modeled to describe financial markets as evolving complex networks. In particular we show that a useful way to describe dependencies is by means of information filtering networks that are able to retrieve relevant and meaningful information in complex financial data sets. In financial economics these dependencies can describe asset comovement and spill-overs. In particular, several models are presented that show how network and factor model approaches are related to modeling of multivariate volatility and asset returns respectively. Finally, we sketch out how these studies can inspire future research and how they contribute to support researchers in both fields to find a better and a stronger common language.

Susan Babinec, Ian Baring-Gould, Amy N. Bender et al.

Transitioning from fossil-fuel power generation to renewable energy generation and energy storage in remote locations has the potential to reduce both carbon emissions and cost. This study presents a techno-economic analysis for implementation of a hybrid renewable energy system at the South Pole in Antarctica, which currently hosts several high-energy physics experiments with nontrivial power needs. A tailored model of resource availability and economics for solar photovoltaics, wind turbine generators, lithium-ion energy storage, and long-duration energy storage at this site is explored in different combinations with and without existing diesel energy generation. The Renewable Energy Integration and Optimization (REopt) platform is used to determine the optimal system component sizing and the associated system economics and environmental benefit. We find that the least-cost system includes all three energy generation sources and lithium-ion energy storage. For an example steady-state load of 170 kW, this hybrid system includes 180 kW-DC of photovoltaic panels, 570 kW of wind turbines, and a 3.4 MWh lithium-ion battery energy storage system. This system reduces diesel consumption by 95% compared to an all-diesel configuration, resulting in approximately 1200 metric tons of carbon footprint avoided annually. Over the course of a 15-year analysis period the reduced diesel usage leads to a net savings of 57 million United States dollars, with a time to payback of approximately two years. All the scenarios modeled show that the transition to renewables is highly cost effective under the unique economics and constraints of this extremely remote site.

Bronson Mutanda, Maireva Chrispen

The advent of digital financial technology left the business community and its clients celebrating convenient ways of online shopping, paying bills and money transfers. However, digital banking technology came with its share of challenges, due to highly digitalised economies in the context of the Fourth Industrial Revolution, cyber fraudsters are increasingly targeting and leveraging on financial market infrastructures. Cyber security of banking institutions and the financial systems across the globe remains a major concern of Central Banks, investors, internal auditors and financial risk managers. The purpose of this research paper was to examine the efficacy of cyber fraud prevention measures used by commercial banks in Zimbabwe. The study also looked into the difficulties encountered in managing cyber-fraud. Results indicate that cyber fraud risk management strategies adopted by Commercial banks are partly effective which indicates existence of opportunities for cyber fraudsters to attacks and get away with it at the expense of clients, banks and the financial system as a whole. Results also indicate that Commercial banks are facing quite a number of challenges which include the following: lack of sophisticated systems, cyber attackers are always ahead, some of the clients do not take awareness messages send to them seriously, some clients share passwords and credit cards and lack of enough education and knowhow of employees. The study therefore concludes that, cyber fraud risk management strategies adopted by Commercial banks are partly effective. Monetary and fiscal authorities need to continue monitoring Commercial banks with regard to implementation of cyber security risk based supervision framework.

Tiziana Leone, Alison Gemmill, Laura Brown

Background While secular trends in high-income countries show an increase in the mean age at menopause, it is unclear if there is a similar pattern in low-income and middle-income countries (LMICs), where women’s exposure to biological, environmental and lifestyle determinants of menopause may differ. Premature (before age 40 years) and early (ages 40–44 years) menopause could have negative repercussions on later life health outcomes which in ageing societies could mean further stress on low-resource health systems. An evaluation of such trends in LMICs has been hampered by the suitability, quality and comparability of data from these countries.Methods Using 302 standardised household surveys from 1986 to 2019, we estimate trends and CIs using bootstrapping in the prevalence of premature and early menopause in 76 LMICs. We also developed a summary measure of age at menopause for women who experience menopause before the age of 50 years based on demographic estimation methods that can be used to measure menopausal status in surveys with truncated data.Results Trends indicate an increasing prevalence of early and premature menopause in LMICs, in particular in sub-Saharan Africa and South/Southeast Asia. These regions also see a suggested decline of the mean age at menopause with greater variation across continents.Conclusions This study enables the analysis of menopause timing by exploiting data generally used for the study of fertility by methodologically allowing the use of truncated data. Findings show a clear increase in prevalence of premature and early menopause in the regions with the highest fertility with possible consequences for later life health. They also show a different trend compared with high-income regions, confirming a lack of generalisability and the importance of accounting for nutritional and health transitions at the local level. This study calls for further data and research on menopause on a global scale.