There is a short introduction of the project below. To get a deeper understanding of the project, the first important document is project-notebook.pdf which is a knitted version of the main working document that includes analysis, codes, references etc. The second important document is project-presentation.pdf which is a document used in a 15-minutes project presentation.
Studying the relationship between income and education has been the focus of many studies. The studies have concluded that a strong connection exists between higher education and income (Card, 1999). In general, individuals with stronger education are more likely to be employed and earn a big salary compared to less educated people (Card, 1999). For that reason, education is described as an investment in human capital (Wolla & Sullivan, 2017).
This study examines this phenomenon from the perspective of people that have acquired their education from colleges in the United States. As the connection between education and income has been shown in the existing literature, this study strives to further examine the associations between college related features and income level years after graduation. This project is not limited to only considering educational aspects but expands it to family backgrounds.
In this study, a Bayesian approach is taken to observe the bond between the educational and family related features and earnings in a multivariate linear regression setting. It is in our interest to find out how accurately the selected features can predict future income for the students. Aim of our project is to find a way to predict average income after studying at a university for any non-specialized university in the United States. We build three different multivariate models – separate, pooled and hierarchical – to predict income after school.
As this study is conducted in a university environment by university students and presented mainly to other students and faculty, the findings of this study could be especially meaningful for the members of the group and peers on the course.