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Feature Selection of Post-graduation Income of College Students in the United States

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2018)

Abstract

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. We discuss how higher numbers of students in a cohort who grew up in Zip code areas where over 25% of the population hold a Professional Degree was predictive of more college graduates being classified as High income.

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Notes

  1. 1.

    Social capital represents trust, solidarity, and reciprocity in collective social interactions and engagement in community-based activities [16].

References

  1. Autor, D.H.: Skills, education, and the rise of earnings inequality among the ‘other 99 percent’. Science 344(6186), 843–851 (2014)

    Article  Google Scholar 

  2. Chetty, R., Friedman, J., Saez, E., Turner, N., Yagan, D.: Mobility report cards: the role of colleges in intergenerational mobility. Technical report, Stanford University (2017)

    Google Scholar 

  3. Hout, M.: Social and economic returns to college education in the United States. Ann. Rev. Sociol. 38, 379–400 (2012)

    Article  Google Scholar 

  4. National Center for Educational Statistics [NCES]. Percentage of 18- to 24-year-olds enrolled in degree-granting postsecondary institutions, by level of institution and sex and race/ethnicity of student: 1970 through 2015. http://nces.ed.gov/programs/digest/d15/tables/dt15_302.60.asp?current=yes. Accessed 1 Mar 2018

  5. Beaudry, P., Green, D.A., Sand, B.M.: The declining fortunes of the young since 2000. Am. Econ. Rev. 104(5), 381–386 (2014)

    Article  Google Scholar 

  6. Valletta, R.G.: Recent flattening in the higher education wage premium: polarization, skill downgrading, or both? In: Education, Skills, and Technical Change: Implications for Future US GDP Growth. University of Chicago Press (2017)

    Google Scholar 

  7. Federal Reserve Bank of New York. The Labor Market for Recent College Graduates. https://www.newyorkfed.org/research/college-labor-market/index.html. Accessed 1 Mar 2018

  8. Altonji, J.G., Arcidiacono, P., Maurel, A.: The analysis of field choice in college and graduate school: determinants and wage effects (no. w21655). National Bureau of Economic Research (2015)

    Google Scholar 

  9. Witteveen, D., Attewell, P.: The earnings payoff from attending a selective college. Soc. Sci. Res. 66, 154–169 (2017)

    Article  Google Scholar 

  10. U.S. Department of Education. https://www.newyorkfed.org/research/college-labor-market/index.html. Accessed 1 Mar 2018

  11. U.S. Census Bureau: Distribution of Personal Income 2010 (2010). https://www.census.gov/2010census/data/. Accessed 1 Mar 2018

  12. Beasley, J.E., Chu, P.C.: A genetic algorithm for the set covering problem. Eur. J. Oper. Res. 94(2), 392–404 (1996)

    Article  Google Scholar 

  13. Goldberg, D.: Genetic Algorithms in Optimization, Search and Machine Learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  14. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newsl. 11(1), 10–18 (2009)

    Article  Google Scholar 

  15. Chetty, R., Hendren, N., Kline, P., Saez, E.: Where is the land of opportunity? The geography of intergenerational mobility in the United States. Q. J. Econ. 129(4), 1553–1623 (2014)

    Article  Google Scholar 

  16. Putnam, R.D.: Our Kids: The American Dream in Crisis. Simon and Schuster, New York (2016)

    Google Scholar 

  17. Lucas, S.R.: Effectively maintained inequality: education transitions, track mobility, and social background effects. Am. J. Sociol. 106, 1642–1690 (2001)

    Article  Google Scholar 

  18. Lucas, S.R., Byrne, D.: Effectively maintained inequality in education: an introduction. Am. Behav. Sci. 61(1), 3–7 (2017)

    Article  Google Scholar 

  19. Avery, C., Turner, S.: Student loans: do college students borrow too much—or not enough? J. Econ. Perspect. 26(1), 165–192 (2012)

    Article  Google Scholar 

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Correspondence to Ewan Wright .

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Appendix A

Appendix A

The dataset analyzed in this study can be accessed at https://collegescorecard.ed.gov/data/.

30 potential attributes include:

Group One: School information

  1. 1.

    School Type (e.g. private school)

  2. 2.

    Predominant Awarded Degrees (e.g., Bachelor degree)

  3. 3.

    Student Size

  4. 4.

    Instructional Expenditure per Student

  5. 5.

    Ratio between Part-time and Full-time Students

  6. 6.

    Degree Completion Rate

  7. 7.

    Average Faculty Salary

Group Two: Admission information

  1. 8.

    Admission Rate

  2. 9.

    Average SAT Score

Group Three: Cost information

  1. 10.

    In-State Tuition

  2. 11.

    Out-of-State Tuition

Group Four: Student information

  1. 12.

    Percentage of White Students

  2. 13.

    Percentage of Black Students

  3. 14.

    Percentage of Asian Students

  4. 15.

    Percentage of American Indian Students

  5. 16.

    Percentage of Hispanic Students

  6. 17.

    Percentage of Female Students

  7. 18.

    Percentage of First-Generation Students

  8. 19.

    Average Age of Entering College

  9. 20.

    Average Debt

Group Five: Family and community information

  1. 21.

    Percentage of Students whose Family Income was classified as Low

  2. 22.

    Percentage of Students whose Family Income was classified as Lower Middle

  3. 23.

    Percentage of Students whose Family Income was classified as Higher Middle

  4. 24.

    Percentage of Students whose Family Income was classified as High

  5. 25.

    Percentage of Students whose Family Income was classified as Very High

  6. 26.

    Percentage of Students whose Parents were 1st Generation College Student

  7. 27.

    Percentage of Students whose Parents Have a Middle School Degree

  8. 28.

    Percentage of Students whose Parents Have a High School Degree

  9. 29.

    Percentage of Students whose Parents Have a Post-High-School Degree

  10. 30.

    Population from Students’ Zip Codes over 25% with a Professional Degree.

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Wright, E., Hao, Q., Rasheed, K., Liu, Y. (2018). Feature Selection of Post-graduation Income of College Students in the United States. In: Thomson, R., Dancy, C., Hyder, A., Bisgin, H. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2018. Lecture Notes in Computer Science(), vol 10899. Springer, Cham. https://doi.org/10.1007/978-3-319-93372-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-93372-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93371-9

  • Online ISBN: 978-3-319-93372-6

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