Search Results (72)

In Latin America, over 50% of the population has relied on loans from family members or friends, reflecting the importance of trust-based networks in response to financial exclusion. This study examines how distrust in the formal financial system influences the use of informal borrowing. Using data from 17 countries for the years 2014, 2017, and 2021, and applying a fixed-effects logistic regression model by country and time, we confirm that rising distrust significantly increases the likelihood of turning to loans from personal networks. This relationship intensifies in times of crisis. Beyond this, we find that macroeconomic variables such as GDP per capita and unemployment also significantly affect informal borrowing behavior. This research contributes to the literature by integrating institutional, economic, and social variables, highlighting the role of interpersonal trust as a form of social capital. It also advances the field of personal finance by revealing an everyday strategy of financial resilience. Finally, this study offers relevant implications for public policy, advocating for a more realistic and context-sensitive approach to financial inclusion, especially in regions where credit constraints in the formal sector have pushed households to seek more accessible and flexible alternatives. Full article

Among the factors threatening food security in Nigeria are poor access to credit facilities, the high cost of inputs, and poor processing and storage. Cereals and grains are among the staple food crops highly consumed by Nigerians. In this study, multi-stage sampling procedure was used to select 1200 registered rice processors from Agricultural Development Programme zones in the Nigerian states of Lagos, Oyo, Ogun, Ondo, Osun, and Ekiti, and a structured questionnaire was used to obtain data on primary, secondary, and tertiary postharvest operations. The data were analyzed using descriptive statistics and Pearson Product Moment Correlation. The results showed that the majority (65.1%) of the respondents were male, 54.5% were 30–60 years old, 86.9% were married, 96.3% had been formally educated, and 99.9% processed, 71.5% packaged, and 79.4% stored more than 5001 kg of rice monthly. The majority (85.9%) of the respondents had no knowledge of rice moisture content and still used local means of rice processing, while 14.1% of the respondents used modern means of rice processing. We concluded that stored local rice was still subject to more wastage, spoilage, and losses due to the poor processing, packaging, and storage methods used in the study area. We recommend the adoption of modern and suitable rice technologies for processing, packaging, and storage. Furthermore, credit facilities should be made available, and inputs should be subsidized for rice farmers and processors. Full article

The lack of transparency in many AI systems continues to hinder their adoption in critical domains such as healthcare, finance, and autonomous systems. While recent explainable AI (XAI) methods—particularly those leveraging large language models—have enhanced output readability, they often lack traceable and verifiable reasoning that is aligned with domain-specific logic. This paper presents Nomological Deductive Reasoning (NDR), supported by Nomological Deductive Knowledge Representation (NDKR), as a framework aimed at improving the transparency and auditability of AI decisions through the integration of formal logic and structured domain knowledge. NDR enables the generation of causal, rule-based explanations by validating statistical predictions against symbolic domain constraints. The framework is evaluated on a credit-risk classification task using the Statlog (German Credit Data) dataset, demonstrating that NDR can produce coherent and interpretable explanations consistent with expert-defined logic. While primarily focused on technical integration and deductive validation, the approach lays a foundation for more transparent and norm-compliant AI systems. This work contributes to the growing formalization of XAI by aligning statistical inference with symbolic reasoning, offering a pathway toward more interpretable and verifiable AI decision-making processes. Full article

When students use an online eTextbook with content and interactive graded exercises, they often display aspects of two types of behavior: credit-seeking and knowledge-seeking. A student might behave to some degree in either or both ways with given content. In this work, we attempt to detect the degree to which either behavior takes place and investigate relationships with student performance. Our testbed is an eTextbook for teaching Formal Languages, an advanced Computer Science course. This eTextbook uses Programmed Instruction framesets (slideshows with frequent questions interspersed to keep students engaged) to deliver a significant portion of the material. We analyze session interactions to detect credit-seeking incidents in two ways. We start with an unsupervised machine learning model that clusters behavior in work sessions based on sequences of user interactions. Then, we perform a fine-grained analysis where we consider the type of each question presented within the frameset (these can be multi-choice, single-choice, or T/F questions). Our study involves 219 students, 224 framesets, and 15,521 work sessions across three semesters. We find that credit-seeking behavior is correlated with lower learning outcomes for students. We also find that the type of question is a key factor in whether students use credit-seeking behavior. The implications of our research suggest that educational software should be designed to minimize opportunities for credit-seeking behavior and promote genuine engagement with the material. Full article

Recurrent flooding near Tempe Lake, Indonesia, exacerbated by climate change, presents significant challenges for rice-farming communities, threatening their livelihoods and food security. This study explores the adaptive strategies employed by farmers to mitigate flood-related risks and evaluates the role of institutional frameworks in risk-sharing mechanisms. Using a mixed-methods approach, we analysed primary survey data from 140 rice farmers and conducted interviews with irrigation pump operators. The study viewed 30 years of historical flood records to identify environmental patterns affecting agricultural practices. The findings indicate that farmers employ various adaptation strategies to enhance resilience, including irrigation technologies, risk-sharing agreements, and livelihood diversification. Informal co-operative structures play a crucial role, with landowners covering 50% of production costs and farmers receiving 60% of profits after deducting operational expenses. However, flood-related income disruptions and shifting environmental conditions threaten long-term sustainability. This study emphasizes the need to formalize risk-sharing agreements, promote flood-resistant crop adoption, and improve water management infrastructure to address these challenges. Additionally, policies should support the development of community-led co-operative models and flexible contract structures, as well as training and access credit to ensure fair compensation for climate-induced losses. Integrating institutional support, technological advancements, and community-driven adaptation strategies can enhance economic stability, strengthen food security, and ensure the long-term viability of rice farming in flood-prone regions. Full article

The use of conventional farming methods, excessive reliance on fertilizers and inputs, and abrupt shifts in climate have raised significant concerns regarding global agricultural production, particularly in developing countries like Nepal. Agriculture products such as rice hold significant importance in Nepal’s agriculture and economy, serving as a staple food and a crucial source of livelihood for its population. Sustainable cultivation and enhancing productivity are imperative for ensuring food security and economic stability in the country. Adoption of climate-smart agriculture (CSA) practices can minimize detrimental effects, promote sustainability, and enhance resilience towards climate change. We surveyed 200 farmers across four municipalities in the Chitwan District of Nepal to explore the prevalence and socio-economic drivers of the adoption of CSA practices, which include stress-tolerant varieties, efficient water management, and diversified cropping, among others. The results revealed that the adoption of pest-resistant plant varieties was a common CSA practice in the study area. Logistic regression results revealed that the adoption of CSA practices increases with an increase in the education of farmers and membership of climate-related organizations. Similarly, the adoption of CSA practices is negatively associated with an increase in farm size, farmers’ farming experience, and their access to credit facilities. Short-term courses and training could be initiated as a complement to formal education to maximize the adoption of CSA practices. Similarly, climate and farmer-related organizations should be further strengthened to maximize their capacity to facilitate more farmers and provide need-based, timely information flow. This study highlights the potential of CSA to promote sustainability and enhance resilience to climate change, but also identifies barriers such as credit access and the need for tailored policy interventions. Our findings contribute to understanding the dynamics of CSA adoption in vulnerable agricultural settings and can guide future strategies to promote sustainability and climate resilience in smallholder farming communities in developing countries. Full article

Despite the MFI insurgency, agricultural financing remains critically low, even though microcredit is widely accepted as both a substitute and compliment to formal credit. Zimbabwe is an agro-based economy and very little is known about the determinants of microcredit demand and microcredit size in smallholder resettled sugarcane farmers. Research is concentrated in short-term agriculture activities. Thus, this study aims to fill the unattended gap in lagged returns agriculture activities such as sugarcane production which takes at least a year to mature, hence, the greater need for agriculture financing alternatives such as microfinance. The study examined the determinants of both microcredit demand and loan size (magnitude of microcredit participation) by smallholder resettled A2 sugarcane farmers in Chiredzi. Primary data from 370 smallholder resettled sugarcane farmers (214 borrower participants and 156 non-borrower participants) were used. Probit and Tobit regression models were used for data analysis in STATA. Operational costs, interest rate, grace period, and land size significantly affect both the demand for microcredit and microcredit size, while education, household farming assets, extension services, and payback period significantly affect microfinance demand, and risk attitude/perception additionally determine microcredit size. Special microfinance schemes best suitable for the agriculture sector and crop/plant-specific agriculture financing schemes, currency, and macroeconomic stability are the major policy recommendations. Full article

Access to finance is essential for fostering financial inclusion, improving household economic well-being, and stimulating economic growth. However, if not prudently managed, it can become a double-edged sword, increasing the risk of over-indebtedness, particularly among low-income households. This paper investigates the borrowing behavior of rural households in Egypt, exploring whether it is motivated by the optimization of intertemporal consumption or reflects deeper financial vulnerabilities. The study enhances our understanding of rural households’ financial behavior in Egypt and contributes to the literature by introducing perceived general self-efficacy as a key behavioral factor. The paper employs a quantitative methodology using a probit analysis of the Egypt Labor Market Panel Survey to explore the factors affecting the demand for formal loans, informal borrowing, and Rotating Saving and Credit Associations (RoSCAs). The results show that informal credit plays a dominant role in meeting rural households’ financial needs. A significant positive relationship between formal and informal credit suggests they are complementary. Elderly, married, less educated, and poorer individuals are more likely to seek both forms of credit, with employment stability being a key differentiator. Self-efficacy also has a significant positive effect. No significant regional differences are observed, except in the case of informal borrowing, with rural households in Upper Egypt showing less reliance, suggesting that social image may influence financial behavior in this region. The results suggest that demand for credit is driven by economic and financial vulnerability of rural households. The paper highlights key policy implications. First, to enhance participation in formal credit market, credit policies should offer more affordable, tailored credit relevant to starting a business rather than financing consumption, part of which is conspicuous. Second, the low self-efficacy among the rural poor suggests a need for policies that combine credit access with financial literacy and debt management support to prevent over-indebtedness. Full article

With the rapid evolution of the rural digital economy, analyzing the impact of digital literacy level on farm households’ formal borrowing is crucial for easing credit constraints and fostering rural economic growth. Leveraging the data from the 2020 China Family Panel Studies (CFPSs) and applying binary probit models and the Karlson–Holm–Breen (KHB) method, this study delineates the positive correlation between the digital literacy level and increased formal borrowing among farm households. The findings, which were robust against endogeneity and robustness tests, underscore the role of digital literacy level in augmenting farmers’ earnings and social networks, with a notably stronger mediation by earnings. The effects are particularly significant for middle-aged and older, less educated males in the central and western regions, in contrast with younger, highly educated females in the east. This research advocates for enhancing rural digital infrastructure and education, alongside financial system reforms, to advance rural financial development and support sustainable rural revitalization. Full article

This study provides an overview of the different dimensions of financial inclusion, its socioeconomic impacts on society’s sustainable development, and future research agendas. Initially, 620 studies were identified using Scopus and other databases, employing keywords such as financial literacy, financial inclusion, financial capability, women’s empowerment, fintech, artificial intelligence, financial accessibility, sustainable development goals, and economic growth. After refinement based on focus and relevance, 325 papers were analyzed in detail for review, primarily focused on India and emerging economies. This review highlights that access to finance by untouched segments of society is essential for sustainable and socio-economic development in developing economies. The official banking system, an effort by the government to assist the financially disadvantaged, can incorporate the impoverished into a formal financial system through campaigns and credit system reforms. Socioeconomic programs reinforce one another and foster the development of children, women, families, and society. This research paper undertakes a systematic literature review primarily focused on relevant articles in broad areas of financial inclusion and its impact analysis and offers a valuable agenda for future research. Full article

The concept of learning has multiple interpretations, ranging from acquiring knowledge or skills to constructing meaning and social development. Machine Learning (ML) is considered a branch of Artificial Intelligence (AI) and develops algorithms that can learn from data and generalize their judgment to new observations by exploiting primarily statistical methods. The new millennium has seen the proliferation of Artificial Neural Networks (ANNs), a formalism able to reach extraordinary achievements in complex problems such as computer vision and natural language recognition. In particular, designers claim that this formalism has a strong resemblance to the way the biological neurons operate. This work argues that although ML has a mathematical/statistical foundation, it cannot be strictly regarded as a science, at least from a methodological perspective. The main reason is that ML algorithms have notable prediction power although they cannot necessarily provide a causal explanation about the achieved predictions. For example, an ANN could be trained on a large dataset of consumer financial information to predict creditworthiness. The model takes into account various factors like income, credit history, debt, spending patterns, and more. It then outputs a credit score or a decision on credit approval. However, the complex and multi-layered nature of the neural network makes it almost impossible to understand which specific factors or combinations of factors the model is using to arrive at its decision. This lack of transparency can be problematic, especially if the model denies credit and the applicant wants to know the specific reasons for the denial. The model’s “black box” nature means it cannot provide a clear explanation or breakdown of how it weighed the various factors in its decision-making process. Secondly, this work rejects the belief that a machine can simply learn from data, either in supervised or unsupervised mode, just by applying statistical methods. The process of learning is much more complex, as it requires the full comprehension of a learned ability or skill. In this sense, further ML advancements, such as reinforcement learning and imitation learning denote encouraging similarities to similar cognitive skills used in human learning. Full article

The financial inclusion (FI) literature claims that expanding access of the poor to consumer credit in the formal financial system is an instrument to alleviate poverty. This view has been increasingly challenged by the financialization literature, which highlights unsustainable indebtedness of low-income borrowers following the introduction of FI policies. While a welcome contribution and antidote to mainstream assessments, much of the financialization literature on Brazil has, to date, focused on a macro-oriented analysis. As a major testing ground for FI, a better understanding of these dynamics in Latin America’s largest country deepens our understanding of the socio-economic consequences of inserting poor populations into the financial system. This paper contributes to the financialization literature by drawing on interviews conducted in a Rio de Janeiro slum to understand how debt dynamics operate in everyday life, along with its structural conditioners. It shows that FI policies provide limited consumption smoothing and instead increase the structural vulnerability of low-income households. Wage and welfare support along with greater oversight and control of financial products supplied to low-income borrowers are warranted. Full article

Target2 is the Eurozone’s cross-border payment system, which is mandatory for the settlement of euro transactions involving Eurozone central banks. It is being used to save the Eurozone from imploding. A key underlying problem is that the Eurozone does not satisfy the economic conditions for being an Optimal Currency Area, i.e., a geographical area over which a single currency and monetary policy can operate on a sustainable, long-term basis. The different business cycles in the Eurozone, combined with poor labour and capital market flexibility, mean that systematic trade surpluses and deficits will build up because inter-regional exchange rates can no longer be changed. Surplus regions need to recycle the surpluses back into deficit regions via transfers to keep the Eurozone economies in balance. But the largest surplus country—Germany—refuses to formally accept that the European Union is a ‘transfer union’. However, deficit countries, including the largest of these—Italy—are using Target2 for this purpose. Target2 has become a giant credit card for Eurozone members that import more than they export to other members, but with two differences compared with normal credit card debt: neither the debt nor the interest that accrues on the debt ever needs to be repaid. Furthermore, the size of the deficits being built up is causing citizens in deficit countries to lose confidence in their banking systems, leading them to transfer their funds to banks in surplus countries. Target2 is also being used to facilitate this capital flight. However, these are not viable long-term solutions to systemic Eurozone trade imbalances and weakening national banking systems. There are only two realistic outcomes. The first is a full fiscal and political union, with Brussels determining the levels of tax and public expenditure in each member state—which has long been the objective of Europe’s political establishment. The second outcome is that the Eurozone breaks up. Full article

Cowpea productivity in West Africa is low due to several challenges, including limited access to high-quality seeds. Despite the development of improved varieties, smallholder farmers face difficulties in accessing certified seeds. This study interviewed 634 households in south-central Niger to assess how farmers access cowpea seeds. Most farmers (94.6%) relied on informal seed systems, with local markets being the primary source (72.8%). The formal seed sector, including the private sector, had a limited role. Farmers typically stored seeds for seven months utilizing pesticides (54.9%) and hermetic methods (42.6%). Interestingly, the price of seeds of improved cowpea varieties in local markets was 1.19 times higher than that of local varieties, but only 25% of the cost of certified seeds. Interventions to strengthen informal seed systems and improve access to credit could enhance the adoption of high-quality cowpea seeds and increase productivity. Full article

Rice agriculture provides millions of households with a steady source of income and employment. However, for small and marginal farmers, the exorbitant cost of production inputs presents a formidable obstacle in their pursuit of acquiring it. Credit constraints are a significant impediment to the adoption of agricultural technologies. Therefore, this paper identifies the determinant of access to rural credit and its impact on Boro rice production technology adoption in Bangladesh using cross-sectional data. The study employed probit regression, propensity score matching (PSM), inverse probability weighting (IPW), and inverse probability weighted regression adjustment (IPWRA) techniques. The findings indicate that age, family size, working members, and involvement in safety net programs negatively and significantly influence access to rural credit, while earning persons in the family, literacy, rice farming experience, remittance, and total income positively influence access to rural credit. The positive and significant ATT values suggested that access to rural credit has a positive and significant effect on technology adoption and the level of technology use. It was also found that access to rural credit has a heterogeneous effect. In particular, non-government organization (NGO) credit has a more significant impact on technology adoption than formal bank credit. Access to credit and the adoption of agricultural technologies can be greatly improved with the help of a location-specific rural credit policy and strong monitoring from the formal banking sector. Full article