This study examines the spatial distribution of socioeconomic conditions in Colombia, using Moran's Index as a tool for spatial autocorrelation analysis. Key indicators related to education, health, infrastructure, access to basic services, employment, and housing conditions are addressed, allowing the identification of inequalities and structural barriers. The research reveals patterns of positive autocorrelation in several socioeconomic dimensions, suggesting a concentration of poverty and underdevelopment in certain geographic areas of the country. The results show that municipalities with more unfavorable conditions tend to cluster spatially, particularly in the northern, northwestern, western, eastern, and southern regions of the country, while the central areas exhibit better conditions. Permutation analyses are employed to validate the statistical significance of the findings, and LISA cluster maps highlight the regions with the highest concentration of poverty and social vulnerability. This work contributes to the literature on inequality and regional development in emerging economies, demonstrating that public policies should prioritize intervention in territories that exhibit significant spatial clustering of poverty. The methodology and findings provide a foundation for future studies on spatial correlation and economic planning in both local and international contexts.

This study explores the feminization of poverty and the dynamics of the care economy in rural areas, focusing on the municipality of Génova, Quindío, Colombia. The novelty of this study lies in its analysis of the compounded effects of the COVID-19 pandemic on women’s economic participation and care responsibilities in a rural context, offering insights relevant to Latin America. This study addresses the critical problem of how increased caregiving responsibilities and labor informality during the pandemic have disproportionately impacted economically active women, exacerbating gender inequalities. The objective is to analyze the relationship between the care economy and feminization of poverty, providing policy recommendations for post-pandemic recovery in rural settings. The methodology consisted of a two-stage approach. In the first stage, a probabilistic stratified sampling design was applied using data from the Colombian National Population and Housing Census and the Génova, Quindío, and Colombia Municipal Panel. In the second stage, fieldwork was conducted with a sample of 347 women using the RedCap application for data collection. The results indicate a significant increase in unpaid domestic and caregiving work during the pandemic, particularly for the elderly, disabled, and children. Additionally, labor informality increased, further limiting economic opportunities for women. The key conclusion is that public policies aimed at reducing gender disparities in rural labor markets must prioritize caregiving support and formal employment opportunities for women. These findings suggest that addressing the care economy is crucial for closing gender gaps and fostering equitable economic recovery in rural Latin American areas.

Central Sulawesi has been grappling with significant challenges in human development, as indicated by its Human Development Index (HDI). Despite recent improvements, the region still lags behind the national average. Key issues such as high poverty rates and malnutrition among children, particularly underweight prevalence, pose substantial barriers to enhancing the HDI. This study aims to analyze the impact of poverty, malnutrition, and household per capita income on the HDI in Central Sulawesi. By employing panel data regression analysis over the period from 2018 to 2022, the research seeks to identify significant determinants that influence HDI and provide evidence-based recommendations for policy interventions. Utilizing panel data regression analysis with a Fixed Effect Model (FEM), the study reveals that while poverty negatively influences with HDI, underweight prevalence is not statistically significant. In contrast, household per capita income significantly impacts HDI, with lower income levels leading to declines in HDI. The findings emphasize the need for comprehensive policy interventions in nutrition, healthcare, and economic support to enhance human development in the region. These interventions are crucial for addressing the root causes of underweight prevalence and poverty, ultimately leading to improved HDI and overall well-being. The originality of this research lies in its focus on a specific region of Indonesia, providing localized insights and recommendations that are critical for targeted policy making.

The native peoples of the State of Mexico, especially the Mazahua community, present a high degree of marginality and food vulnerability, causing their inhabitants to be classified within the poor and extremely poor population. The objective of the research is to propose a food vulnerability index for the Mazahua community of the State of Mexico through the induction-deduction method, contrasting the existing literature with a semi-structured exploratory interview to identify the main factors that affect the native peoples. The study population was selected taking into account the number of inhabitants and poverty levels. The sources of information, in addition to documentary sources, were key informants and visits to Mazahua families that facilitated information about the different variables: natural, economic, social, cultural component, degree of adaptability and resilience for the creation and better understanding of the food vulnerability index in the communities under study.