Hasil untuk "Home economics"

S. Edition, N. Nie, Karin Steinbrenner

H. Blossfeld, J. Huinink

Hung‐Hao Chang

R. Arnott, A. Palma, R. Lindsey

Sanjiv Ranjan Das, Richard Stanton, Nancy Wallace

This article reviews the recent literature on algorithmic fairness, with a particular emphasis on credit scoring. We discuss human versus machine bias, bias measurement, group versus individual fairness, and a collection of fairness metrics. We then apply these metrics to the US mortgage market, analyzing Home Mortgage Disclosure Act data on mortgage applications between 2009 and 2015. We find evidence of group imbalance in the dataset for both gender and (especially) minority status, which can lead to poorer estimation/prediction for female/minority applicants. Loan applicants are handled mostly fairly across both groups and individuals, though we find that some local male (nonminority) neighbors of otherwise similar rejected female (minority) applicants were granted loans, something that warrants further study. Finally modern machine learning techniques substantially outperform logistic regression (the industry standard), though at the cost of being substantially harder to explain to denied applicants, regulators, or the courts. Expected final online publication date for the Annual Review of Financial Economics, Volume 15 is November 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Fengke Lin, Yuedi Xu, Siyu Lin et al.

Pukeng tea (PKT), a traditional Chinese dark tea, has been consumed for centuries, yet its volatile and microbial dynamics remain unclear. This study integrated metabolomics, chemometrics, and microbiome analysis to explore PKTs with 3–20 years of storage. HS-SPME-GC–MS identified 189 volatiles, mainly alcohols, aldehydes, and ketones. PCA and PLS-DA revealed distinct metabolite patterns, with 46 differential volatiles, such as 1-butanol and 1-penten-3-ol, characterized as potential discriminants among PKT samples. Microbiota analysis showed 11 dominant bacterial genera, shifting from Firmicutes in early storage to Actinobacteriota in later stages, while Aspergillus dominated fungal communities. Correlation analysis revealed significant associations between dominant microbes such as Staphylococcus and Saccharopolyspora and aroma-active volatiles, suggesting microbial contributions to PKT's evolving flavor. This study provides the first integrated characterization of volatile and microbial diversity in PKT, offering insights into quality control, product authentication, and functional microbe discovery for the sustainable development of traditional dark teas.

E. Glaeser, Joseph Gyourko

In this essay, we review the basic economics of housing supply and the functioning of US housing markets to better understand the distribution of home prices, household wealth and the spatial distribution of people across markets. We employ a cost-based approach to gauge whether a housing market is delivering appropriately priced units. Specifically, we investigate whether market prices (roughly) equal the costs of producing the housing unit. If so, the market is well-functioning in the sense that it efficiently delivers housing units at their production cost. Of course, poorer households still may have very high housing cost burdens that society may wish to address via transfers. But if housing prices are above this cost in a given area, then the housing market is not functioning well— and housing is too expensive for all households in the market, not just for poorer ones. The gap between price and production cost can be understood as a regulatory tax, which might be efficiently incorporating the negative externalities of new production, but typical estimates find that the implicit tax is far higher than most reasonable estimates of those externalities.

Oluleke Babayomi, Babatunde Olubayo, I. Denwigwe et al.

Sub-Saharan Africa (SSA) is home to 75% of the world’s unelectrified population, and approximately 500 million of these live in rural areas. Off-grid mini-grids are being deployed on a large scale to address the region’s electrification inequalities. This study aims to provide a comprehensive review of the research on the off-grid renewable mini-grids in SSA. The study covers the current status of the level of deployment of off-grid mini-grids. It also reviews multi-criteria decision-making models for optimizing engineering, economics, and management interests in mini-grid siting and design in SSA. The statuses of financing, policy, and tariffs for mini-grids in SSA are also studied. Finally, the current status of energy justice research in respect of mini-grids in SSA is reviewed. The study shows the important role of decentralized renewable technologies in the electrification of SSA’s rural population. Within a decade since 2010, the rural electrification rate of SSA has increased from 17% to 28%, and 11 million mini-grid connections are currently operational. Despite these gains, the literature points to several injustices related to the present model by which SSA’s renewable mini-grids are funded, deployed, and operated. Hence, several recommendations are provided for the effective application of the energy justice framework (EJF) for just and equitable mini-grids in SSA.

Man Zhao, Ruwen Guan, Kai Wang et al.

This study evaluates the effects of pre-harvest chitooligosaccharide (COS) application on the quality of Cabernet Gernischt grapes and wine. Grapevines were sprayed with 0.37 mM COS and 0.1 % Tween 80 at fruit expansion (CE), veraison (CV), and maturity (CM) stages, for the control (CK), water was used with 0.1 % Tween 80. Post-harvest, the physicochemical properties, phenolic composition, antioxidant capacity, color attributes, and sensory characteristics of the grapes and wine were analyzed. COS treatment promoted the physicochemical indicators, phenolic substances, antioxidant capacity and color quality of grapes compared to CK, thus improving the quality of wine. Sensory evaluation showed the sensory quality of wine treated with COS is superior to that of CK, especially for the CM stage produced the best sensory quality. These findings demonstrate that COS is an effective plant elicitor for enhancing red wine grape and wine quality, offering a practical approach to improving sensory and physicochemical attributes.

Sintha Fransiske Simanungkalit, Chandrayani Simanjorang, Dora Samaria et al.

Latar Belakang: Prestasi puncak seorang atlet tidak hanya ditentukan oleh intensitas latihan rutin, tetapi juga dipengaruhi secara signifikan oleh integrasi faktor pendukung yang kompleks seperti asupan gizi, tingkat pengetahuan, dan kondisi fisik secara menyeluruh. Tujuan: Penelitian ini bertujuan untuk menganalisis hubungan antara kebugaran fisik, konsumsi protein, pengetahuan gizi, dan status gizi dengan tingkat prestasi atlet. Metode: Penelitian ini menggunakan metode kuantitatif observasional analitik dengan desain cross-sectional. Populasi penelitian mencakup atlet sepak bola, catur tuna netra, dan taekwondo yang dipilih melalui teknik total sampling dengan total 143 responden. Data dikumpulkan melalui kuesioner, pengukuran antropometri, dan bleep test. Uji normalitas menunjukkan data tidak berdistribusi normal (p-value<0,05), sehingga analisis hubungan antar variabel dilakukan menggunakan uji Chi-Square. Hasil: Mayoritas responden adalah laki-laki (85,3%) dan atlet tingkat regional (67,8%). Hasil penelitian menunjukkan bahwa 61,0% atlet memiliki kebugaran fisik yang baik, meskipun 60,8% di antaranya memiliki konsumsi protein di bawah rata-rata kebutuhan harian. Sebanyak 53,1% responden memiliki pengetahuan gizi yang baik dan 77,2% memiliki status gizi normal. Hasil analisis bivariat menunjukkan adanya hubungan signifikan antara prestasi dengan kebugaran fisik (p-value=0,013), konsumsi protein (p-value<0,001), dan pengetahuan gizi (p-value=0,008). Namun, status gizi ditemukan tidak memiliki hubungan signifikan dengan prestasi (p-value=0,055). Kesimpulan: Kebugaran fisik, konsumsi protein, dan pengetahuan gizi merupakan faktor krusial yang berhubungan langsung dengan prestasi atlet. Keberhasilan atlet sangat bergantung pada dukungan asupan dan pengetahuan gizi serta kondisi fisik yang prima.

Qi Qi, Danmeng Liu, Liang Wang et al.

IntroductionIron is an essential nutrient during pregnancy and may influence the early development of the neonatal gut microbiota. This study aimed to investigate the association between maternal dietary iron intake during pregnancy and the gut microbiota (GM) characteristics of both the mother and neonate in a well-characterized cohort.MethodsNinety-five mother-neonate dyads were included in this study. Mothers completed a food frequency questionnaire (FFQ) providing estimates of dietary iron intake during pregnancy, and participants were categorized into higher (≥ median) or lower (&lt; median) groups of maternal dietary iron intake. Fecal samples were collected from mothers (third trimester) and from neonates, and assessed via 16S rRNA amplicon sequencing. Differences in diversity and abundance of GM were compared between groups.ResultsThere was no difference in profile or diversity in maternal samples however, neonatal samples indicated greater diversity of GM in infants of mothers with higher intakes of iron (Shannon p = 0.04; Simpson p = 0.01). After stratification by delivery mode, in the stratum of normal vaginal delivery (NVD), Simpson diversity remained higher in the infants’ GM of mothers with higher intakes of iron (p = 0.04). The relative abundance of the core genus Bifidobacterium in NVD and cesarean section (CS) neonates showed higher in the higher group than that in the lower group, as the difference was not statistically significant. Maternal dietary iron intake was significantly associated with the neonate GM composition with variation explained 10.24% (p = 0.007).ConclusionAdequate dietary iron intake during pregnancy may promote beneficial bacterial colonization and increase the biodiversity of the neonate GM.

Ying Cheng, Yilin Zhang, Xuechun Pang et al.

This study evaluated the safety of Lacticaseibacillus rhamnosus (L. rhamnosus) KF7, a probiotic strain originating from kefir, for potential use in infant and children's food products in China. The safety evaluation was conducted through a comprehensive approach involving whole-genome sequencing, in vitro and in vivo studies using alternative models: Caenorhabditis elegans (nematodes). Genomic analysis confirmed the absence of virulence factors and antibiotic resistance genes in KF7. Phenotypic characterization demonstrated that KF7 exhibits no antibiotic resistance, does not produce indole, is non-hemolytic, and lacks amino acid decarboxylase activity. Furthermore, KF7 was found to not produce histamine and generate only a minimal amount of tyramine when cultured in MRS medium supplemented with amino acids. The strain was also shown to produce both L- and D-lactic acid, with the concentration of D-lactic acid falling within a safe range. To further evaluate the safety of KF7 in vivo, nematode model was employed, with Pseudomonas aeruginosa serving as a pathogenic control and L. rhamnosus GG, known to be safe for infants and children, as a positive control. The results demonstrated that KF7 does not adversely affect nematode egg-laying, egg development, growth, locomotion, or lifespan. Instead, it appeared to have beneficial effects on gut microbiota and extending the nematodes' lifespan. In summary, this study established the method of using nematode as an in vivo evaluation to the safety of probiotics, and the safety data of the KF7 strain was supplemented by in vitro research, which proved that KF7 has a certain degree of safety.

Tobias Schmidt, Kai-Robin Lange, Matthias Reccius et al.

As interest in economic narratives has grown in recent years, so has the number of pipelines dedicated to extracting such narratives from texts. Pipelines often employ a mix of state-of-the-art natural language processing techniques, such as BERT, to tackle this task. While effective on foundational linguistic operations essential for narrative extraction, such models lack the deeper semantic understanding required to distinguish extracting economic narratives from merely conducting classic tasks like Semantic Role Labeling. Instead of relying on complex model pipelines, we evaluate the benefits of Large Language Models (LLMs) by analyzing a corpus of Wall Street Journal and New York Times newspaper articles about inflation. We apply a rigorous narrative definition and compare GPT-4o outputs to gold-standard narratives produced by expert annotators. Our results suggests that GPT-4o is capable of extracting valid economic narratives in a structured format, but still falls short of expert-level performance when handling complex documents and narratives. Given the novelty of LLMs in economic research, we also provide guidance for future work in economics and the social sciences that employs LLMs to pursue similar objectives.

Ayusha Fayyaz, Zoltan Bartha

The goal of this research is to uncover the channels through which research and development (R&D) impacts economic growth in developing countries. The study employed nine variables from three broader categories in the World Economic Forum database, each covering 32 countries from the lower-middle-income group for the year 2019. The theoretical framework is based on the R&D ecosystem, which includes components such as Institutions, Human capital, Capital market, R&D, and Innovation. Each of these components can contribute to the economic development of the country. Using Structural Equation Modelling (SEM), we build a path diagram to visualize and confirm a potential relationship between the components. R&D features had a positive impact on innovation (regression weight estimate: +0.34, p = 0.001), as did capital market institutions (regression weight estimate: +0.12, p = 0.007), but neither had a significant impact on growth. According to the Schumpeterian institutional interpretation, R&D and innovation efforts may not lead to sustained growth in middle-income countries. We find no significant connection between innovation performance and economic growth. This suggests that while R&D and capital markets may contribute to innovation through entrepreneurship, this contribution is not impactful enough to drive economic growth in developing countries. Our findings provide further evidence of the middle-income trap.

Daniel Loebell, Mingmar Sherpa, Ram Datta Bhatta

The Millennium Challenge Corporation (MCC), started in 2004 by the United States Congress, focuses on development initiatives involving good governance, sustainable economic growth, and poverty reduction. Since its inception, it has invested over 13 billion US dollars in 30 countries. Nepal is a recent beneficiary, signing a compact valued at 500 million US dollars in 2017, ratified in 2022. The compact mainly invests in road infrastructure and electricity transmission, including construction of 315 kilometers of high-voltage transmission lines, three substations, and upgrading 100 kilometers of the East-West highway. By supporting commercialization of Nepals 40 gigawatt hydropower potential, the MCC aims to enhance Nepals structural economic prosperity. Beyond economics, the compact influences Nepals foreign policy by diversifying partnerships and reducing overdependence on neighbors China and India. The establishment of the Millennium Challenge Account (MCA)-Nepal fosters host-country ownership, mitigating geopolitical concerns. Building on International Relations scholar Shiping Tangs Institutional Foundations for Economic Development (IFED) framework, this paper shows how the MCC addresses Nepals infrastructure development capacity. Success stories from Ghana and other MCC countries highlight transformative progress toward prosperity, positioning Nepal to leverage both development and diplomatic opportunities. Drawing from multiple sources, this paper argues that the MCC compact provides Nepal with opportunities to overcome infrastructural barriers to growth.

Annie Liang

Machine learning algorithms can now outperform classic economic models in predicting quantities ranging from bargaining outcomes, to choice under uncertainty, to an individual's future jobs and wages. Yet this predictive accuracy comes at a cost: most machine learning algorithms function as black boxes, offering little insight into \emph{why} outcomes occur. This article asks whether machine learning can guide the development of new economic theories. Economic models serve an important purpose beyond prediction -- they uncover the general mechanisms behind observed behaviors. A model that identifies the causal pathways of economic development is more valuable than one that merely predicts which countries will escape poverty, because it enables policymakers to encourage that development in countries where it might not have happened otherwise. Similarly, a model that predicts imperfectly across many domains can be more valuable than one that is highly accurate in a specific domain, since the former allows insights and data obtained from one setting to inform decisions and policy in another. Applying machine learning algorithms off-the-shelf is unlikely to yield such models. But recent work shows that, when reconceived with the aims of an economic modeler in mind, machine learning methods can improve both prediction and understanding. These approaches range from adversarially training algorithms to expose the limits of existing models, to imposing economic theory as a constraint on algorithmic search. Advances in large language models complement these strategies and open new research directions.

E. Xie, K. S. Reddy, Jie Liang

María Gricelda Vázquez-Carrillo, Arturo Hernández-Montes, Natalia Palacios-Rojas et al.

Abstract One of the most typical dishes of traditional Mexican cuisine is pozole, made with nixtamalized maize. This dish has a special place as part of the identity of Mexican culture. However, it is time-consuming to prepare. With an increasing demand for precooked maize for pozole and the limited information on its preparation process, this study aims to assess the impact of both traditional (TN) and commercial nixtamalization (CN) on the quality of processed maize and its reception by consumers, focusing on the three most popular maize landraces used in pozole recipes. This study was carried out with the Cacahuacintle (‘CAC’), Elotes Occidentales (‘EO’) and Ancho (‘AN’) landraces, which were nixtamalized using the traditional method (only lime) and the commercial method (lime + additives) and the grain was flowered. The quality of the flowered grain was determined, and a sensory analysis consisting of magnitude of difference tests, a descriptive analysis, affective test and evaluation of consumer preferences was carried out. The ‘CAC’ landrace, when processed traditionally, yielded the highest sensory and commercial quality. The ‘EO’ landrace demanded a longer flowering time, resulting in less volume but retaining the aleurone layer. This characteristic helped preserved a portion of the anthocyanins. Consistently, maize landraces subjected to traditional nixtamalization displayed higher ratings for attributes related to masa and nejayote aroma. The ‘CAC’ landrace subjected to CN faced challenges in acceptability due to odors of acetic acid and sulfuric acid. These findings underscore the importance and advantages the TN techniques. They also emphasize the need to preserve grain quality and meeting consumer preferences when exploring alternative maize processing methods for emerging markets.

Tekilu Tadesse Choramo, Jemal Abafita, Yerali Gandica et al.

Global and regional integration has grown significantly in recent decades, boosting intra-African trade and positively impacting national economies through trade diversification and sustainable development. However, existing measures of economic integration often fail to capture the complex interactions among trading partners. This study addresses this gap by using complex network analysis and dynamic panel regression techniques to identify factors driving economic integration in Africa, based on data from 2002 to 2019. The results show that economic development, institutional quality, regional trade agreements, human capital, FDI, and infrastructure positively influence a country's position in the African trade network. Conversely, trade costs, the global financial crisis, and regional overlapping memberships negatively affect network based integration. Our findings suggest that enhancing a country's connectivity in the African trade network involves identifying key economic and institutional factors of trade partners and strategically focusing on continent-wide agreements rather than just regional ones to boost economic growth.

S M Toufiqul Huq Sowrov

International trade has been in the forefront of economic development and growth debates. Trade openness, its definition, scope, and impacts have also been studied numerously. Tariff has been dubbed as negative influencer of economic growth as per conventional wisdom and most empirical studies. This paper empirically examines relationships among trade openness as trade share to GDP, import tariff rate and economic growth. Panel dataset of 11 G-20 member countries were selected for the study. Results found a positively significant correlation between trade openness and economic growth. Tariff has negatively significant correlation with economic growth in lagged model. OLS and panel data fixed-effects regression were employed to carry out the regression analysis. To deal with endogeneity in trade openness variable, a 1-year lag regression technique was conducted. Results are robust and significant. Policy recommendation suggests country specific trade opening and tariff relaxation.