Financial inclusion and social protection have been recognised as the primary essential stimuli from the potential they carry as avenues for economic development, especially with respect to reduction in poverty and inequalities, the creation of employment and the enhancement overall welfare and livelihood. However, inclusive access to financial resources and equitable access to social protection interventions have remained a significant concern in Nigeria. In addition, the emergence of the COVID-19 pandemic exposed the weakness of Nigeria in all sectors of the economy such as energy, health, education and food systems and low-level inclusive access to financial resources and social protection coverage. On the other hand, this study argues that financial inclusion and social protection has the potential to mitigation shocks orchestrated by the COVID-19 pandemic. This study empirically examines how social protection interventions and access to financial resources responded to COVID-19 pandemic. The study made use of data sourced from the World Bank’s COVID-19 national longitudinal phone survey 2020 and applied the logit regression. The findings show that social protection and access to financial resources significantly associated with the likelihood of shock mitigation during the COVID-19 pandemic. The results show that social protection intervention reduces the probability of being severely affected by shocks by 0.431. Given this result, the study recommends that the government should put more effort into proper social protection intervention to mitigate the effect of the COVID-19 pandemic.

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.

Several studies have explored green economy and the needs for improvement on the standard of living among low-income families or households in many developing countries including Bangladesh. Similarly, there is an emphasis on economic growth and vision 2030 is regarded stressed. Nonetheless, there is less attention in exploring green economy in propelling sustainable financial inclusion among low-income families and households in Bangladesh in order to attain vision 2030 and overall economic growth.  The primary objective is to explore green economy in fostering sustainable financial inclusion among low-income families and households in Bangladesh in enhancing economic growth and vision 2030 in Bangladesh. Content Analysis (CA) and systematic literature review (SLR) as an integral part of qualitative research. Secondary data were gathered through different sources such as: Web of Science (WOS), related journals, published references, research papers, library sources and reports. The results indicated that poverty is a prime challenge impeding sustainable financial inclusion among low-income families and households in Bangladesh. The study has further established the potential of green economy in improving well-beings and social fairness in fostering sustainable and inclusive finance among families or households with low-income in the country. The paper also highlighted the necessity of implementing policy relating to vision 2030 by enhancing sustainable and inclusive finance among low-income households in particular and overall economic growth in the country in general. In conclusion, it has been reiterated that green economy has been a mechanism for achieving sustainable development in general and poverty eradication among low-income households in Bangladesh. It is therefore suggested that the government and economic policymakers should provide enabling environment for improving green economy among low-income households in achieving Vision 2030 and overall economic growth in the country.

The potential of entrepreneurship to reduce poverty is closely tied to critical factors such as access to finance, training and education, networks and social capital, and supportive regulatory environments. Understanding and addressing these underlying issues through the lens of the Social Capital theory can help foster an entrepreneurial spirit in cities and mitigate poverty through business and community development. This paper explores the insights and standpoints of key stakeholders about poverty in Saint John and its impact on entrepreneurship. The study uses a quantitative method and analyzes data from surveys with stakeholders. The results show that social isolation, system inflexibility, individual issues, housing, and financial support programs are significant poverty challenges in Saint John, and these issues have implications for entrepreneurship. By integrating Social Capital Theory into policy initiatives, policymakers can enhance community resilience and empower vulnerable individuals. This application of social capital principles provides a holistic framework for designing effective poverty-reduction measures, offering transformative insights applicable not only to Saint John but also to diverse small cities. The study contributes a nuanced understanding of poverty’s impact on entrepreneurship, advocating for inclusive strategies that resonate with the social fabric of communities.

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.