Project focuses on using text (from company statements and job postings) to better understand inequality in labor market outcomes. It will require some data scraping, managing large amounts of text data (e.g., captures from company websites), using NLP to better understand trends in the data, and SML to code key elements in text data. It may also involve running descriptives and comparisons of text data across time periods (e.g., how the language changes).

This is an UNPAID research project.

Faculty Advisor

  • Professor: Mabel Abraham
  • Center/Lab:
  • Location: Uris Hall
  • Professor Abraham’s research examines how organizational and social network processes contribute to gender differences in economic outcomes.

Project Timeline

  • Earliest starting date: 9/13/21
  • End date: 8/31/22
  • Number of hours per week of research expected during Fall 2021: ~6
  • Number of hours per week of research expected during Summer 2022: ~20

Candidate requirements

  • Skill sets: Natural language processing; Supervised machine learning; scraping data
  • Student eligibility: freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible
  • Academic Credit Possible: Yes
  • Additional comments: I am looking for a student who also has strong verbal and written communication skills. In order to effectively check their work they will need some intuition and understanding of the text.