Hasil untuk "Home economics"

Michael Balzer, Kainat Khowaja, Christiane Fuchs

Surveillance of diseases in a pandemic is an important part of public health policy. Diagnostic testing at the individual level is often infeasible due to resource constraints. To circumvent these constraints, group testing can be applied. The economic cost evaluation from the payer's perspective typically focuses only on deterministic costs which overlooks the substantial economic impact of productivity losses resulting from quarantine and workplace disruptions. The objective of this article is to develop a mathematical model for a retrospective economic evaluation of group testing that incorporates both deterministic costs and income-based economic loss. Group testing algorithms are revisited and simulated at optimized pool sizes to determine the required number of tests. Income data from the German Socio-Economic Panel are integrated into a mathematical model to capture the economic loss. Afterward, hybrid Monte Carlo experiments are conducted by evaluating the economic cost in the Coronavirus disease 2019 pandemic in Germany. Monte Carlo experiments show that the optimal choice of group testing algorithms changes substantially when income-based economic losses are included. Evaluations considering only deterministic costs systematically underestimate the total economic cost. Algorithms with a longer quarantine duration are less attractive than shorter quarantine duration if income-based economic loss is accounted for. The findings show that current evaluations underestimate the true economic cost. Group testing algorithms with shorter duration and fewer stages are preferred, even when they require a larger number of tests. These results underscore the importance of incorporating income-based economic loss into a mathematical model.

Pietro Bini, Lin William Cong, Xing Huang et al.

Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental economics literatures, we conduct the most comprehensive set of experiments to date$-$originally designed to document human biases$-$on prominent LLM families across model versions and scales. We document systematic patterns in LLM behavior. In preference-based tasks, responses become more human-like as models become more advanced or larger, while in belief-based tasks, advanced large-scale models frequently generate rational responses. Prompting LLMs to make rational decisions reduces biases.

Jishi Ye, Jishi Ye, Yu Liu et al.

BackgroundHigh trans-fatty acid (TFA) intake is a major modifiable risk factor for cardiovascular disease (CVD), especially in older adults. This study aimed to assess global trends and health inequalities in CVD burden attributable to high TFA intake from 1990 to 2021 and project future patterns through 2036.MethodsUsing data from the Global Burden of Disease (GBD) Study 2021, we analyzed age-standardized mortality rates (ASMR), disability-adjusted life years (ASDR), and inequality indicators across 204 countries and territories. Age-Period-Cohort (APC) models and Bayesian projections were applied to estimate future trends.ResultsGlobally, ASMR and ASDR attributable to high TFA intake declined by 69 and 68%, respectively, from 1990 to 2021. The most significant reductions were observed in high-SDI regions, where comprehensive TFA bans and public health policies were implemented. In contrast, the absolute burden remains high in low- and middle-SDI countries due to limited policy enforcement and dietary interventions. Socioeconomic inequalities narrowed over time, but vulnerable populations still face elevated risks. Projections indicate a continued global decline in CVD burden attributable to TFA through 2036, though widening uncertainties reflect demographic and policy challenges.ConclusionWhile global progress in reducing TFA-related CVD burden is evident, persistent disparities and emerging risks in low-resource settings underscore the need for global elimination of industrial TFA, strengthened health systems, and targeted strategies to protect high-risk groups.

Jiaxin Liu, Yixuan Tang, Yi Yang et al.

Large Language Models (LLMs) are increasingly used in decision-making scenarios that involve risk assessment, yet their alignment with human economic rationality remains unclear. In this study, we investigate whether LLMs exhibit risk preferences consistent with human expectations across different personas. Specifically, we assess whether LLM-generated responses reflect appropriate levels of risk aversion or risk-seeking behavior based on individual's persona. Our results reveal that while LLMs make reasonable decisions in simplified, personalized risk contexts, their performance declines in more complex economic decision-making tasks. To address this, we propose an alignment method designed to enhance LLM adherence to persona-specific risk preferences. Our approach improves the economic rationality of LLMs in risk-related applications, offering a step toward more human-aligned AI decision-making.

Ziang Huang, Huashan Chen

Economic complexity - a group of dimensionality-reduction methods that apply network science to trade data - represented a paradigm shift in development economics towards materializing the once-intangible concept of capabilities as inferrable and quantifiable. Measures such as the Economic Complexity Index (ECI) and the Product Space have proven their worth as robust estimators of an economy's subsequent growth; less obvious, however, is how they have come to be so. Despite ECI drawing its micro-foundations from a combinatorial model of capabilities, where a set of homogeneous capabilities combine to form products and the economies which can produce them, such a model is consistent with neither the fact that distinct product classes draw on distinct capabilities, nor the interrelations between different products in the Product Space which so much of economic complexity is based upon. In this paper, we extend the combinatorial model of economic complexity through two innovations: an underlying network which governs the relatedness between capabilities, and a production function which trades the original binary specialization function for a fine-grained, product-level output function. Using country-product trade data across 216 countries, 5000 products and two decades, we show that this model is able to accurately replicate both the characteristic topology of the Product Space and the complexity distribution of countries' export baskets. In particular, the model bridges the gap between the ECI and capabilities by transforming measures of economic complexity into direct measures of the capabilities held by an economy - a transformation shown to both improve the informativeness of the Economic Complexity Index in predicting economic growth and enable an interpretation of economic complexity as a proxy for productive structure in the form of capability substitutability.

Emre Akusta

This study analyzes the impacts of economic growth on ecosystem in Turkiye. The study uses annual data for the period 1995-2021 and the ARDL method. The study utilizes the Ecosystem Vitality Index, a sub-dimension of the Environmental Performance Index. In addition, seven models were constructed to assess in detail the impact of economic growth on different dimensions of the ecosystem. The results show that economic growth has a significant impact in all models analyzed. However, the direction of this impact differs across ecosystem components. Economic growth is found to have a positive impact on agriculture and water resources. In these models, a 1% increase in GDP increases the agriculture and water resources indices by 0.074-0.672%. In contrast, economic growth has a negative impact on biodiversity and habitat, ecosystem services, fisheries, acid rain and total ecosystem vitality. In these models, a 1% increase in GDP reduces the indices of biodiversity and habitat, ecosystem services, fisheries, acid rain and total ecosystem vitality by 0.101-2.144%. The results suggest that the environmental costs of economic growth processes need to be considered. Environmentally friendly policies should be combined with sustainable development strategies to reduce the negative impacts of economic growth.

Ruijun Hou, Samuel Baker, Stephanie von Hinke et al.

We study the long-term health and human capital impacts of local economic conditions experienced during the first 1,000 days of life. We combine historical data on monthly unemployment rates in urban England and Wales 1952-1967 with data from the UK Biobank on later-life outcomes. Leveraging variation in unemployment driven by national industry-specific shocks weighted by industry's importance in each area, we find no evidence that small, common fluctuations in local economic conditions during the early life period affect health or human capital in older age.

Zhanhui Liu, Bing Chen, Songlei Wang et al.

This study investigated the effects of galactooligosaccharide (GOS) purity (80 % and 95 %) and heating time (60 and 150 min) on characterization and bioactivities of Maillard reaction products (MRPs) formed by conjugating tilapia scale collagen peptides with GOS at 90 °C. Spectral analyses, degree of grafting, and furosine content showed that lower-purity GOS exhibited higher glycation reactivity. Amino acid composition and advanced glycation end products analyses showed that arginine was the primary amino acid involved in glycation, with abundant formation of methylglyoxal-derived hydroimidazolone 1 (MG-H1). Digested MRPs maintained strong antioxidant activity, particularly in the lower-purity GOS group. Meanwhile, MRPs heated for 150 min enhanced L. casei, L. pentosus, and B. bifidum growth, while those heated for 60 min favored B. longum. MG-H1 level was positively correlated with antioxidant activity and B. bifidum growth (p < 0.01). This study highlights the broad application potential of glycated bioactive peptides with lower-purity GOS.

Pilar Ortíz Sandoval, Margarita Aguilera‐Gómez, Anna Kostka et al.

Abstract Anthropogenic activities, such as industrial processes, urban development, intensive agriculture and waste disposal, have significantly contributed to the continuous introduction and accumulation of a wide array of xenobiotic compounds into natural ecosystems. Among them, emerging contaminants (ECs) such as pharmaceuticals, endocrine‐disrupting chemicals (EDCs), and per‐ and polyfluoroalkyl substances (PFAS) are of increasing concern due to their persistence, bioactivity and limited regulation. ECs enter ecosystems through diverse pathways including wastewater discharge, agricultural runoff and atmospheric deposition. Once released, many of these xenobiotics can bioaccumulate in organisms and enter the food chain, posing serious risks to food safety and public health. Traditional physico‐chemical remediation methods are often insufficient or environmentally taxing, prompting a shift toward bio‐based alternatives like bioremediation. These approaches, which rely on the activity of microbial communities to degrade pollutants, offer more sustainable solutions but require further interdisciplinary research to optimise their use. The One Health framework provides an effective model for addressing the complex risks posed by xenobiotics. This research programme aims to harmonise methodologies for cumulative dietary risk assessment across Europe and explore microbial strategies for xenobiotic degradation. By integrating microbiomics, toxicology, environmental science and food safety, this approach supports the development of safer food systems and more effective pollution management in line with the ‘farm to fork’ and One Health principles.

Tomohiro Hirano, Joseph E. Stiglitz

This paper presents a model that studies the impact of credit expansions arising from increases in collateral values or lower interest rate policies on long-run productivity and economic growth in a two-sector endogenous growth economy, with the driver of growth lying in one sector (manufacturing) but not in the other (real estate). We show that it is not so much aggregate credit expansion that matters for long-run productivity and economic growth but sectoral credit expansions. Credit expansions associated mainly with relaxation of real estate financing (capital investment financing) will be productivity-and growth-retarding (enhancing). Without financial regulations, low interest rates and more expansionary monetary policy may so encourage land speculation using leverage that productive capital investment and economic growth are decreased. Unlike in standard macroeconomic models, in ours, the equilibrium price of land will be finite even if the safe rate of interest is less than the rate of output growth.

Ayse D. Lokmanoglu, Carol K. Winkler, Kareem El Damanhoury et al.

With globalization's rise, economic interdependence's impacts have become a prominent factor affecting personal lives, as well as national and international dynamics. This study examines RT's public diplomacy efforts on its non-Russian Facebook accounts over the past five years to identify the prominence of economic topics across language accounts. Computational analysis, including word embeddings and statistical methods, investigates how offline economic indicators, like currency values and oil prices, correspond to RT's online economic content changes. The results demonstrate that RT uses message reinforcement associated economic topics as an audience targeting strategy and differentiates their use with changing currency and oil values.

Goran Hristovski, Gjorgji Gockov, Viktor Stojkoski

Recent studies highlight economic complexity's role in mitigating fiscal crises, often measured via an economy's trade structure. Trade, however, is just one facet of an economy's structure and omits critical innovative activities like research. Here, we investigate how a multidimensional approach to economic complexity-including both trade and research structures-relates to fiscal instability. By using data on over 230 national fiscal crises from 1995 to 2021 and hazard duration analysis, we assess how measures of trade and research complexity combine to explain crisis likelihood. We find that the interaction of complexity dimensions significantly reduces crisis probability, whereas individual indexes alone are not robust predictors. This suggests that economies focusing on a single dimension may be more vulnerable, thus highlighting the importance of balanced development across multiple areas. These findings offer valuable insights for policymakers aiming to enhance economic resilience and mitigate fiscal risks.

Davide Fiaschi, Angela Parenti, Cristiano Ricci

This paper studies the evolution of economic activities using a continuous time-space aggregation-diffusion model, which encompasses competing effects of agglomeration and congestion. To bring the model to the real data, a novel discretization technique over time and space is introduced. This technique effectively disentangles spatial effects into pure topography, agglomeration, repulsion, and diffusion forces, which is crucial for developing robust econometric methods in spatial economics. Our empirical analysis of personal income across Italian municipalities from 2008 to 2019 validates the model's primary predictions and demonstrates superior performance compared to the most common spatial econometric models in the literature.

Wadim Strielkowski, Oxana Mukhoryanova, Oxana Kuznetsova et al.

This paper analyzes sustainable regional economic development and land use employing a case study of Russia. The economics of land management in Russia which is shaped by both historical legacies and contemporary policies represents an interesting conundrum. Following the dissolution of the Soviet Union, Russia embarked on a thorny and complex path towards the economic reforms and transformation characterized, among all, by the privatization and decentralization of land ownership. This transition was aimed at improving agricultural productivity and fostering sustainable regional economic development but also led to new challenges such as uneven distribution of land resources, unclear property rights, and underinvestment in rural infrastructure. However, managing all of that effectively poses significant challenges and opportunities. With the help of the comprehensive bibliographic network analysis, this study sheds some light on the current state of sustainable regional economic development and land use management in Russia. Its results and outcomes might be helpful for the researchers and stakeholders alike in devising effective strategies aimed at maximizing resources for sustainable land use, particularly within their respective regional economies.

Tomohiro Furuki, Tomohiro Nobeyama, Shunji Suetaka et al.

Reentrant condensation (RC) is a protein behavior in which the protein solution shifts between the one- and two-phase state more than twice by increasing a single parameter. Although RC would be a candidate mechanism for the physicochemical design of food additives, no realistic model has been established under diverse contaminants like food materials. Here, we found that a mixture of cola and milk yielded RC. At pH 3.2–3.6, cola induced milk condensation at 30–40%, while lower or higher concentrations of cola did not. Furthermore, we reduced this cola/milk system to two pure components, casein in milk and polyphosphate (polyP) in cola, and investigated the characteristics of casein concentration and zeta potential. This was the first experimental demonstration of RC occurrence in a multicomponent system. The well-characterized cola/milk system would explore both the universal nature of proteins and the industrial application of RC.

Rafael Andersson Lipcsey

As AI adoption accelerates, research on its economic impacts becomes a salient source to consider for stakeholders of AI policy. Such research is however still in its infancy, and one in need of review. This paper aims to accomplish just that and is structured around two main themes. Firstly, the path towards transformative AI, and secondly the wealth created by it. It is found that sectors most embedded into global value chains will drive economic impacts, hence special attention is paid to the international trade perspective. When it comes to the path towards transformative AI, research is heterogenous in its predictions, with some predicting rapid, unhindered adoption, and others taking a more conservative view based on potential bottlenecks and comparisons to past disruptive technologies. As for wealth creation, while some agreement is to be found in AI's growth boosting abilities, predictions on timelines are lacking. Consensus exists however around the dispersion of AI induced wealth, which is heavily biased towards developed countries due to phenomena such as anchoring and reduced bargaining power of developing countries. Finally, a shortcoming of economic growth models in failing to consider AI risk is discovered. Based on the review, a calculated, and slower adoption rate of AI technologies is recommended.

Md. Toaha, Laboni Mondal

The paper examined the impact of agricultural credit on economic growth in Bangladesh. The annual data of agriculture credit were collected from annual reports of the Bangladesh Bank and other data were collected from the world development indicator (WDI) of the World Bank. By employing Johansen cointegration test and vector error correction model (VECM), the study revealed that there exists a long run relationship between the variables. The results of the study showed that agriculture credit had a positive impact on GDP growth in Bangladesh. The study also found that gross capital formation had a positive, while inflation had a negative association with economic growth in Bangladesh. Therefore, the government and policymakers should continue their effort to increase the volume of agriculture credit to achieve sustainable economic growth.

Joshua Gans, Abhishek Nagaraj

This paper explores the economics of Augmented Reality (AR) and Virtual Reality (VR) technologies within decision-making contexts. Two metrics are proposed: Context Entropy, the informational complexity of an environment, and Context Immersivity, the value from full immersion. The analysis suggests that AR technologies assist in understanding complex contexts, while VR technologies provide access to distant, risky, or expensive environments. The paper provides a framework for assessing the value of AR and VR applications in various business sectors by evaluating the pre-existing context entropy and context immersivity. The goal is to identify areas where immersive technologies can significantly impact and distinguish those that may be overhyped.

Rama K. Malladi, Phillip Thompson

As the United States is witnessing elevated racial differences pertaining to economic disparities, we have found a unique example contrary to the traditional narrative. Idaho is the only US state where Blacks earn more than Whites and all other races. In this paper, we examine how Idaho Blacks might have achieved economic success and, more importantly, what factors might have led to this achievement in reducing racial and economic disparities. Preliminary research suggests that fewer barriers to land ownership, smaller populations, well-knit communities, men's involvement in the family, and a relatively less hostile environment have played a significant role. Further research by historians can help the nation uncover the underlying factors to see if some factors are transportable to other parts of the country.

Mark E. Rifkin

The effects of unanticipated crises on health care and first-responder systems are reflected in climate-fueled environmental emergencies, to which human resilience is diminished by our chronic disease epidemic. For example, people who depend on specialized medications, like refrigerated insulin for diabetes, will likely face additional challenges in receiving treatment and care during extreme heat, floods, disasters, and other adverse events. These circumstances may be compounded by staff and equipment shortages, lack of access to fresh food, and inadequate healthcare infrastructure in the wake of a disaster. Simply put, our health care and first-response systems struggle to meet the demands of chronic disease without such crises and may be fundamentally unable to adequately function with such crises present. However, nutrition’s primacy in preventing and controlling chronic disease directly enhances individual and public resilience in the face of existential threats. Highlighting the shared diet-related etiology clearly demonstrates the need for a national policy response to reduce the disease burden and potentiate mitigation of the sequelae of climate risks and capacity limits in our food and health care systems. Accordingly, this article proposes four criteria for nutrition policy in the Anthropocene: objective government nutrition recommendations, healthy dietary patterns, adequate nutrition security, and effective nutrition education. Application of such criteria shows strong potential to improve our resiliency despite the climate and public health crises.