Date of Submission

Spring 2021

Academic Program


Project Advisor 1

Gautam Sethi

Abstract/Artist's Statement

Labor market discrimination on the basis of race is a phenomenon that has faced a wide range of analyses over the past century. With the development of major theories like taste-based, statistical, and implicit discrimination, the subject received both greater attraction and greater conflict. While each form of discrimination maintains viable theoretical background, the underlying purpose behind these forms is to find a way to eliminate discrimination in the workplace. In this paper, the theories of statistical and implicit discrimination are tested through the application of a longitudinal dataset known as the ADD Health Study. By applying this dataset, regression models are constructed that examine a respondent’s race and how it affects their annual income earnings, while controlling for specific test-score variables that are indicative of skill. After these regressions are examined, the data is applied to create graphical representations that slightly favor the theory of statistical discrimination, but demonstrate a presence of both theories.

Open Access Agreement

On-Campus only

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

This work is protected by a Creative Commons license. Any use not permitted under that license is prohibited.

Bard Off-campus Download

Bard College faculty, staff, and students can login from off-campus by clicking on the Off-campus Download button and entering their Bard username and password.