Wenhao Jiang Studies the Devaluation of “Women’s Jobs”

“I come from a household background that is not very typical in China: My mom earns much more income than my dad, which is the opposite of the conventional cultural norms,” said Wenhao Jiang. 

The new assistant professor of Sociology now studies inequality, mostly along the lines of gender, in the U.S. labor market. Jiang’s research focuses on the devaluation of jobs that have become more feminized (i.e., increasingly performed by women), and on the cultural mechanisms at play.  

“When there's an increasing percent of women in a given job role, its pay tends to decline,” he said. “People think of these jobs as less competent, less powerful, and potentially deserving less pay. They lose prestige but retain moral goodness.” 

In a paper currently in print, forthcoming in the American Sociological Review, Jiang’s findings are different from what many economists believe drives statistical inequalities in the labor market. “There is a lot of controversy here and very mixed findings,” he said.  

While economists tend to think that pay for these types of jobs is reduced first — because they provide other non-money amenities like better childcare and work hours, thus attracting more women — sociologists like Jiang argue that there are cultural elements at play when the feminization of jobs impacts pay. 

As part of his research, Jiang turned to text analyzes using natural language processing (NLP) to gather data from Google's nGram Viewer (which digitizes around eight percent of all texts ever published in human history) and popular culture, such as TV and movie scripts and magazines, to measure changes in how people gender-type and portray different occupations.  

“I use text analysis methods and construct those cultural measures for each decade, then trace the change of how different occupations are linked with different gender evaluation concepts like morality, competency and powerfulness,” he said. “Then the story is pretty simple: a lot of jobs that come to be thought of as more feminine actually lose a lot of valuations, with real consequences on wages.” 

Jiang is teaching a new graduate seminar course on machine learning and causal inference, emphasizing statistical rigor. He is one of two faculty joining the Department of Sociology this fall, along with Assistant Professor of Sociology Daniel Scott Smith.