This research paper aims to use several forms of regression analysis and neural networks to examine disparities in American income based on demographic groups. We present three models: a linear regression, a logistic regression, and a neural network to determine significant predictive factors in determining income. Exploring concepts presented by past literature in the field of economics, our research aims to pinpoint disparities and provide explanations for why they may be present in American income patterns. Our research confirms that being a female and being black has a negative relationship with income. Based on literary analysis, these findings can be explained by the presence of several discriminatory factors in the workplace and domestic life. Additionally, we examine the relationship between income and being an immigrant in the United States. Past literature points to the economic success of immigrants. Our findings on the relationship between immigration and wealth are inconclusive. Following the analysis of regressors, we examine how income inequality may present a negative externality and how immigration may present a positive externality.
"American Income: Analyzing Workplace and Domestic Biases."
The Downtown Review.
Available at: https://engagedscholarship.csuohio.edu/tdr/vol9/iss2/6
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