Mobile money innovations, income inequality and gender inclusion in sub-Saharan Africa

This study assesses the role of mobile money innovations on income inequality and gender inclusion in 42 sub-Saharan African countries from 1980 to 2019 using interactive quantile regressions. It finds that, first, income inequality unconditionally reduces the involvement of women in business and politics. Second, mobile money innovations interact with income inequality to have a positive impact on women in business and politics. Third, the net effects of mobile money innovations on gender inclusion through income inequality are consistently negative. Fourth, as the positive conditional or interactive effects and negative net effects are consistent across the conditional distribution of gender inclusion, thresholds at which mobile money innovations can completely dampen the negative effect of income inequality on gender inclusion are provided. Therefore, policymakers should work toward improving conditions for mobile money innovations. They should also be aware that reducing both income inequality and enhancing mobile money innovations simultaneously leads to more inclusive outcomes in terms of gender inclusion

Out-of-this-world returns: How did the market value India\u27s successful moon mission?

This paper explores the response of India\u27s stock market to a successful moon mission. The market responded positively, especially in the defense sector. Four companies experienced significant wealth gains, highlighting the potential for investment and growth in India\u27s space industry

The right information for the right career selection: can it assist Japan to achieve agricultural sustainability?

The sustainability of farming communities in Japan has become quite challenging because of the current aging population phenomenon. This situation gets more complicated with the fact that more than 70% of secondary school youth desire to have jobs related to science and technology, and no one wishes to adopt farming as their career path. The latest studies indicate that misinformation related to agricultural farming is the main reason that youth move away from adopting farming as their career option. In this research, all three pillars of sustainability have been encircled and the youth’s perception related to typhoons and farmers’ perception related to delayed snowfall tendency in recent times have been examined by using remote sensing data. A survey was conducted to observe the career selection trends of the youth at Sapporo Kaisei Secondary School located in Hokkaido. Though the students have prior information about the farming activities related to this research, it was found that among 313 participants, no one wanted to become a farmer. The cited reasons were mainly related to misinformation. With the help of Japan Agricultural Cooperatives (JA) officials, a follow-up event was arranged at Sapporo Kaisei Secondary School, and the youth were provided with correct information related to the farming profession. A questionnaire was administered to observe the effectiveness of the event. The results indicate that once correct information was provided, around 82% (23 out of 28) of the participants either strongly agreed or agreed to adopt farming as their career path. These results indicate that appropriate career counseling should be designed after analyzing the youth’s perceptions related to the specific field and understanding the accuracy of the information that the youth has for a specific field. This can help not only to achieve agricultural sustainability but could also assist in solving the challenges associated with the persistent flat unemployment rate of Japan. Furthermore, this research indicated that contrary to youth perception related to the increased frequency and related losses from climate change-associated typhoons, there has been no significant rise in typhoons over the last 5 years. Moreover, farmers’ perceptions related to late snowfall start time over the past few years can be validated using the albedo data

On deterministic approximation for nearly critical branching processes with dependent immigration

In this paper, we investigate the asymptotic behavior of a triangular array of branching processes with non-stationary immigration. In the nearly critical case, we prove weak convergence of properly normalized and scaled branching processes with immigration to a deterministic function when the immigration process is generated by dependent random variables

Algorithmic amplification and polarization in social media

There is growing concerns about how social media circulate extreme viewpoints, fuels division, and fosters radicalization. TikTok\u27s role in fostering radicalized content was examined by tracing how users become radicalized on TikTok and how its recommendation algorithms drive this radicalization. We identified the social, technological, and psychological factors that contribute to the radicalization of ideological biases on social media and proposed a conceptual lens through which to analyze and predict such radicalization. The results revealed that the pathways by which users access far-right content are manifold and that a large part of this can be ascribed to platform recommendations through a positive feedback loop. Our results are consistent with the proposition that the generation and adoption of extreme content on TikTok largely reflect the user’s input and interaction with a platform. We also discuss how trends in artificial intelligence (AI)-based content systems are forged by an intricate combination of user interactions, platform intentions, and the interplay dynamics of a broader AI ecosystem

Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation

With the ever-increasing dataset size and data storage capacity, there is a strong need to build systems that can effectively utilize these vast datasets to extract valuable information. Large datasets often exhibit sparsity and pose cold start problems, necessitating the development of responsible recommender systems. Knowledge graphs have utility in responsibly representing information related to recommendation scenarios. However, many studies overlook explicitly encoding contextual information, which is crucial for reducing the bias of multi-layer propagation. Additionally, existing methods stack multiple layers to encode high-order neighbor information, while disregarding the relational information between items and entities. This oversight hampers their ability to capture the collaborative signal latent in user-item interactions. This is particularly important in health informatics, where knowledge graphs consist of various entities connected to items through different relations. Ignoring the relational information renders them insufficient for modeling user preferences. This work presents an end-to-end recommendation framework named Knowledge Graph Enhanced Contextualized Attention-Based Network (KGCAN). It explicitly encodes both relational and contextual information of entities to preserve the original entity information. Furthermore, a user-specific attention mechanism is employed to capture personalized recommendations. The proposed model is validated on three benchmark datasets through extensive experiments. The experimental results demonstrate that KGCAN outperforms existing KG-based recommendation models. Additionally, a case study from the healthcare domain is discussed, highlighting the importance of attention mechanisms and high-order connectivity in the responsible recommendation system for health informatics

Quantile connectedness of oil price shocks with socially responsible investments

We use a new method to disentangle various sources of oil price shocks and find how these sources are connected to major global environmental, social, and governance (ESG) equity indices under extreme market movements using daily data from October 2007 to March 2022. Our quantile-based connectedness analysis shows that return connectedness considerably amplifies with the size of the shock for both positive and negative shocks, indicating that oil shocks spread more intensely during extreme market movements. We also find that oil shocks originating from unexpected variation in demand and risk substantially contribute to the variation in the ESG returns while supply shocks have relatively little effect when estimated at the median level but the contribution of all different types of shocks remain extremely high when analyzed at the tails. Our results indicate that socially responsible investments are prone to contagion and thus offer limited portfolio diversification benefits under extreme market movements. One clear implication of our study is that investors need to carefully design their risk management and diversification strategies to account for not only the source of the oil shocks but also the magnitude of the oil shock on their green equity investments. We also show that total dynamic connectedness significantly increased during the global financial crisis, European debt crisis, the Brexit episode and the recent COVID-19 outbreak

Assessment of Heavy Metals in Greenhouse Cultivated Soils, Northern Jordan

Jordan has recently observed a gradual shift in vegetable production from open-fields to greenhouses with mounting consumer concerns about food quality and safety. We investigated heavy metals in soil collected from greenhouse vegetable production area in northern Jordan. Sixty-one surface soil samples were collected, of which forty-seven from plastic-covered greenhouses and fourteen were sampled from the adjacent open-field land, with both designated for vegetable production. The average concentrations of Cr, Cu, Cd, Pb, Ni, Zn were 26.1, 26.8, 0.81, 53.0, 49.3, 139.1 mg/kg, and 19.1, 19.3, 0.66, 49.7, 46.7, 104.9 mg/kg for greenhouse and open-field soils, respectively. While the accumulation of heavy metals was consistently higher in greenhouse than in open-fields, both soils revealed a similar metal ranking with a few exceptions. Greenhouse soils revealed relatively lower pH values with higher variabilities. In greenhouse cultivated soils, CaCO3 content averaged 21.4% compared to 23% measured in open-field soils. Soil salinity showed greater values for greenhouse samples (averaging 1118.6 µs/cm) than those observed in open-field agricultural soils (a mean of 503.6 µs/cm). The soil organic matter (TOM) exhibited values in the range of 1.06-3.35% relative to 0.59-2.41% found in open-field area. The spatial distribution of heavy metal concentrations for greenhouse soils revealed higher levels in the northern soils, whereas the least was found in the southern sampling points. The Enrichment results showed 23.4% of sampling sites were moderately contaminated with Pb, and 38.3% were moderately contaminated with Cd, of which 8.5% indicating moderately severe contamination. The Igeo results indicate 25.5% of greenhouse soils were moderately contaminated with Pb and 38.3% were heavily polluted with Cd. The contamination factors showed 25.5% and 38.3% of greenhouse sampling soils were considerably contaminated with Pb and Cd, respectively. 2% indicate very high contamination for Cd and 2% showed considerable contamination for Zn. PLI indicates that only two sampling sites are polluted. The ecological risk assessment showed low Ei values for all heavy metals suggesting slight risks, except for Cd which indicate strong risk. Total potential ecological risks values showed low risk to the local environment. Cd accounts for most of the total risks (72.27-82.67%) followed by Pb (11.49-14.87%). Some greenhouse soils were non-compliant with soil quality standards especially for Ni, Cd, and Pb. The observed levels of heavy metals are attributable to agricultural activities including long-term application of pesticides, phosphatic and nitrogen fertilizers, sewage sludge, wastewater irrigation and chicken manure in addition to industrial dust and traffic related emissions