Research

Lobo, Daniel and Ryan Brutger. 2025. "Fairness According to Whom? Divergent Perceptions of Fairness among White and Black Americans and Its Effect on Trade Attitudes." American Political Science Review, First View, p. 1-14. [link]

Abstract

Racial divides in American attitudes toward trade are often explained by labor market discrimination and traits like nationalism. However, recent research shows that perceptions of fairness, particularly “asymmetric fairness” concerns about “falling behind” other countries, significantly shape these attitudes. We theorize that linking these perspectives offers new insights. Drawing on critical race theory and cognitive psychology, we theorize that Black Americans, unlike their white counterparts, do not view trade through the lens of asymmetric fairness. Since Black Americans have not benefited from the same social, economic, and political privileges, they are less concerned with “falling behind” and instead focus on fairness as equality. This leads them to evaluate trade agreements through a “principled fairness” lens, contributing to support for trade policies that benefit both the home and foreign country, as opposed to prioritizing an “America First” trade agenda. We test this theory in a national survey experiment and find strong support. 

"Class-Based Disparities in STEM Education: The Case of Data Science" (with Bo Yun Park, Isaac Sloan, and David J. Harding) 

Abstract

Disparities by race, gender, and class in STEM education are a longstanding concern due to their implications for inequality and scientific innovation. While prior research has examined racial and gender disparities at great length, less is known about class-based disparities in STEM education at the college-level. Even less is understood about how cultural capital and social embeddedness affect disengagement from STEM fields. We examine these processes through the case of data science, a newly formed and understudied STEM discipline attracting many students. This study investigates disparities in unenrollment from an introductory data science course at a large R1 state university in California, using survey, administrative, and interview data. We find substantial disparities in persistence in data science by class background and show that cultural capital and social embeddedness help to explain why students from disadvantaged backgrounds face greater challenges in STEM education. This study contributes to a better understanding of class-based disparities in STEM education as well as to a deeper understanding of the nascent field of data science education.