The Determinants of Household Poverty in the USA

It is the dependent variable in this analysis. Family size is basically the number of people in a single family. The county-level family sizes were obtained by dividing the total number of people within respective counties by the number of households. Large family sizes are known to increase dependency levels, thereby making it more difficult for the breadwinners to invest in productive income-generating ventures and subsequently increasing poverty levels.
Urbanization refers to the percentage of the entire population in a county that resides in urban areas. According to Cali and Menon (2009), urbanization has a net poverty reduction effect, since it invigorates business aspirations and income prospects for the population. Unemployment rate is the percentage of the entire population within a locality that is unemployed. Unemployed people tend to have higher poverty levels. Higher unemployment rates correspond to higher poverty levels within a population.
Income refers to the median income of all the employed individuals within a county. The higher the average income for a population, the lower the poverty levels within it. Higher income enables workers to live more comfortably since they can meet their needs and obligations more easily. Equally, better-paid people tend to have more room for investing and, therefore, diversifying their earnings to accumulate even higher income.
This study aims to establish the individual strengths of each of the independent variables as a determinant of poverty (the dependent variable). Correlation analysis has been used to establish the nature of the relationship between each of the independent variables and poverty (whether positive or negative). Using regression analysis, the independent variables are investigated for their ability to predict levels of poverty within the sample population. This type of analysis also provides the basis for obtaining a predictive model that can be used to project future poverty

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