Would you believe that the median wealth ($41k) of a single white woman in the U.S. is 60 percent of that for a single white male? Given the history of gender inequality in the U.S., it seems plausible. Now, would you believe that for the median single black or Hispanic woman, her net wealth is 400 times smaller than the median white female’s? These numbers, reported this year by the Insight Center for Community Economic Development, remind us that there is an astonishing level of inequality along race and gender lines in our society. While most look at these numbers and agree that such discrepancies are unacceptable, it is difficult to imagine solutions. Stanford helps to solve such inequalities by its efforts to diversify its student body and faculty. By educating and promoting talented people from the populations affected, we produce leaders more likely to tackle these problems that our current leaders have not solved.

It is tempting to believe that discrimination on the basis of race and sex does not exist. Barack Obama and Nancy Pelosi hold power that would lead us to believe that race and gender discrimination have been eradicated. Sociological research conducted within the last two decades tells us otherwise. Studies show that for identical resumes, those with typical black names were selected for interviews at a rate 50 percent lower than resumes with typical white names. They also show that there was a 2:1 bias towards hiring a male over a female with identical applications for faculty positions. This measurable bias shows that minority and female applicants for jobs are at a disadvantage relative to their white male counterparts. There is also evidence that during testing, groups threatened by negative stereotypes often perform below their ability level. While diversity efforts to correct for these biases often stir controversy, the default system of affirmative action for the majority largely escapes examination.

Critics of diversity efforts rightly believe that applicants should be evaluated solely on their merits. These and other studies show that the achievements of women and minorities are often viewed as inferior to the same achievements of men and non-minorities, antithetical to this meritocratic ideal. This bias is usually unconscious and is displayed even by people with egalitarian attitudes; however, research shows that it can be mitigated. When selection committees have minorities and women represented at a level of 30 percent or above, the bias along race and gender lines is greatly reduced. This 30 percent represents a tipping point that leads to a more fair evaluation of applicants for competitive positions. Stanford’s action to increase diversity among the students and faculty is an attempt to reach this critical mass. Programs to increase diversity are often criticized as reverse discrimination, but if we achieve this tipping point, the end result will be a university that rewards merit above all other factors. Again, the research currently shows that as a society, we are not yet rewarding merit equally across all groups.

This is not a simple story of whites being prejudiced towards minorities or men viewing women as inferior. Men, women, minorities and whites all demonstrate these biases against women and minorities and other marginalized groups. Our society also marginalizes people based on religion, sexuality and other aspects of our humanity. While we await the day when sufficient diversity to overcome bias has been achieved, we can conduct a rigorous introspection of our own actions and evaluations and ask if our own decisions are based solely on merit. We can actively support diversity efforts on campus that are being conducted by staff, faculty and students. We can also consider joining groups like ASSU Diversity & Tolerance or the Diversity Advocacy Committee of the GSC. While fairness here at Stanford is the immediate goal of these diversity efforts, I hope eventually we can start to solve problems like the recently reported 400-fold wealth gap.

Daniel Soto

Ph.D. Candidate in Physics