The COVID-19 pandemic has significantly restricted household resilience, particularly in developing countries. The study investigates the correlation between livelihood capital and household resilience amid uncertainties due to the COVID-19 pandemic, specifically in Bekasi Regency, West Java Province, Indonesia. Livelihood capital encompasses social, human, natural, physical, and financial, which are crucial in shaping household resilience. This study used the SEM-PLS method and utilized a survey of 120 respondents (household heads) from four villages in two districts (Muaragembong and South Tambun) in Bekasi Regency to identify critical factors that either enhance or impede rural household resilience during and after the pandemic. Findings reveal that households possessing human capital, financial capital, and empowerment are more adept at navigating socioeconomic difficulties during and after the pandemic. However, this research stated that trust and social networks enhance household resilience during the pandemic, whereas social norms are crucial for rebuilding household resilience in the post-pandemic phase. The finding revealed that social cohesion adversely affected household resilience during and after the pandemic, while trust diminished household resilience in the post-pandemic COVID-19 phase. These findings offer insight to policymakers, scholars, and other stakeholders aiming to foster household resilience during and in recovery efforts after the pandemic.

This is a review of empirical studies with the objective of analyzing the theoretical-practical discussions that have been raised internationally to deepen the understanding of the access of rural youth to higher education as an object of study. For this purpose, a narrative review was designed, considering scientific articles published in three different languages and concerning studies conducted in 21 different countries in all regions of the world. The results reveal three discussions: a) the strong interest that higher education has regained in the life expectations of rural young people and their families, especially as a means of social advancement; b) the inequalities that most affect the access of rural youth to higher education are the lack of academic offerings in rural areas and the discontinuities that occur around rural socio-cultural capital; c) since the inequalities experienced by rural youth are diverse, actions to promote greater democratization cannot be limited to implementing systems of grants and scholarships. It is concluded that the major project consists of creating a differentiated higher education model that, in terms of location, academic offerings, recognition of knowledge, and articulation with the environment, allows rural youth to experience their professional training not as an inevitable process of acculturation, but as a continuation of their socio-cultural capital and their territorial yearnings.

This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

Low enrollment intention threatens the funding pools of rural insurance schemes in developing countries. The purpose of this study is to investigate how social capital enhances the enrollment of health insurance among rural middle-aged and elderly. We propose that social capital directly increases health insurance enrollment, while indirectly influences health insurance through health risk avoidance. We used data from the China Health and Retirement Longitudinal Study (wave 4) dating the year of 2018, instrumental variable estimation was introduced to deal with the endogeneity problem, and the mediation analysis was used to examine the mechanism of social capital on insurance enrollment. The results show that social capital is positively related to social health insurance enrollment, and the relationship between social capital and social health insurance enrollment is mediated by health risk avoidance.

Coordination and integration among farms within agri-food chains are crucial to tackle the issue of fragmentation within the primary sector, both at the European and national level. The Italian agri-food system still complains about the need to aggregate supply to support market dynamics, especially for niche and quality products that characterize the Made in Italy. It is well known that the Italian agri-food sector is closely linked to the relationship between agriculture on one hand and culture/tradition on the other, which is reflected in the high number of quality products that have obtained EU PDO (Protected Designation of Origin) and PGI (Protected Geographical Indication) recognition. The development of vertical forms of coordination has found significant support in recent years from the integrated supply chain design approach, which is increasingly becoming an essential tool for implementing rural development policies. In this context, the study provides a comparison between companies that have joined the Integrated Supply Chain Projects of the Rural Development Program and those that have not applied. The aim is to highlight any differences in order to understand policy impact. The analysis is based on the Emilia-Romagna region Farm Accountancy Data Network (FADN) data, and the sample consists of more than 2 thousand farms. The statistical analysis conducted compares treated and non-treated using the Welch-t-test for independent unmatched samples. The main results show higher values for treated farms when structural variables are analyzed, like the utilized agricultural area or the agricultural work unit. In general, higher balance sheet performances emerged for treated farms. In conclusion, this study shows that the Integrated Supply Chain Projects represent a worthwhile tool both to increase cooperation, food quality, and to enhance a competitive agricultural sector.