In today’s world, poverty is a significant challenge affecting millions globally. According to the World Bank’s data, around 9.2% of the global population lived in extreme poverty as of 2020, with a daily income of less than $1.90. Eradicating poverty by 2030 is a critical goal set by the United Nations. To achieve this goal, it is essential to gain insight into poverty’s root causes and find practical ways to alleviate it.

Our group recently studied the topic of Data Science for Society in our course, where we focused on analyzing global poverty rates. Through our analysis, we aimed to gain insight into poverty’s nature, trends, and patterns and provide practical suggestions for poverty alleviation efforts by organizations worldwide.

Our analysis revealed that poverty rates have been declining globally over the past few decades, though there are still significant disparities between regions and countries. We identified correlations between poverty rates and other indicators such as health, education, and access to basic services. Our attempts to use regression models to identify the most critical poverty reduction indicators were challenging due to the strong correlations between different indicators. We concluded that poverty reduction requires a multidimensional approach, addressing various socioeconomic factors rather than a single solution.

Studying the topic of global poverty rates has several benefits for the broader areas of data science, inequality, and society. Firstly, it allows for the use of data-driven approaches to analyze poverty rates, identifying patterns and trends to gain insights into its root causes. This approach can be applied to other areas of research, such as income inequality and social mobility.

Secondly, studying poverty rates can highlight the disparities and inequalities that exist within society. It provides an opportunity to examine the factors that contribute to these inequalities, such as race, gender, and socioeconomic status. By understanding these factors, researchers can develop targeted interventions and policies to address the root causes of inequality and promote social justice.

Lastly, studying poverty rates and their correlation with other indicators such as health and education highlights the importance of social determinants of health. It emphasizes the need for a holistic approach to healthcare, one that addresses not only the medical needs but also the social determinants of health such as access to basic services, education, and employment.

Our findings suggest that organizations invest in policies and programs that provide social security, health insurance, employment, and training initiatives to create job opportunities and increase income, thereby reducing poverty rates. Additionally, organizations should focus on improving access to healthcare services, particularly for maternal and child health, as well as basic services such as electricity, sanitation, and clean fuels. Supporting targeted interventions and policies focusing on vulnerable groups can significantly reduce poverty.

In conclusion, poverty reduction is a complex and multifaceted issue that requires a multidimensional approach. Studying global poverty rates has several benefits for the broader areas of data science, inequality, and society. By addressing the root causes of poverty, researchers can work towards promoting social justice and building a better world for all.

My Blog W8-9 / Knowledge Integration and Project Planning: Data, Inequality and Society (2022-2023) by blogadmin is licensed under a Creative Commons Attribution CC BY 3.0