Tech Has a Gender Problem
Heather Haveman, Professor
Sociology, Sociology
Applications for Spring 2024 are closed for this project.
The tech sector has a gender problem. Women are underrepresented in engineering and management jobs. Journalists and industry veterans have documented the bro culture that pervades tech and leads to discrimination and harassment. With 2 PhD students, I am analyzing gender inequality in the tech sector in 2 separate (but related) papers. Both involve the analysis of employee reviews of their firms from Glassdoor.com.
1) Gendered Discourse in Tech Firms
In this part of the project, we analyze the gender slant of tech firms, meaning cultural conceptions of what work means, who workers are, and who has power. Such gendered conceptions can erect barriers to equality by framing ideal workers, work activities, and company goals as masculine. Male-typed conceptions of jobs and firms raises questions about how well women fit into firms and whether they are competent in many jobs.
We observe cultural conceptions of tech firms by applying natural-language processing techniques (specifically, word embeddings) to employee discourse. We measure how much discourse about tech firms is slanted male vs. neutral or female. We also investigate associations between the gender slant of tech-employee discourse and things that tech firms value, such as innovation and competence.
In the fall, we will wrap up this project (we hope!) and extend our methodology to other concepts, such as diversity, excellence, and sustainability. These concepts may be talked about the same by male and female employees, so our analyses will include not just gender but also employee age, job/role in the firm, and the nature of the firms employees work in (size, location, public/private ownership, etc.) We will also expand beyond the tech sector to other sectors of the US economy: consumer products, business services, manufacturing, health, finance, and government.
2) Work-Family Balance
Balancing work and life outside work, especially family obligations, is a challenge for many employees, with bosses and coworkers sending email and slack messages around the clock and expecting quick responses, work assigned with short deadlines that often require working extra hours or over the weekend. Work-family conflict stresses employees, hampering their performance and their physical health. It forces many employees to leave and find jobs elsewhere. It has especially negative effects on female employees because women do more domestic labor than men.
We are using dictionary methods and statistical analysis to determine which employees, in which firms are more likely to bring up work-family balance (or conflict) when they describe their jobs and workplaces. We are also analyzing how employees feel about work-family balance (positive, negative, and neutral), and predicting which employees in which firms express more positive or negative sentiment about it.
Role: We are looking for apprentices to work closely with Professor Haveman and PhD students Will Rathje and Jasmine Sanders, with weekly meetings, either in person or on zoom. (1) Some apprentices will join ongoing work on gender in employee discourse. (2) Others will join ongoing work on work-family balance.
The work will entail writing clean, well-documented scripts in Python/Jupyter notebooks to clean data, produce descriptive/exploratory statistics, conduct multivariate analyses, and visualize results. Documentation is critical to ensure that others -- your future selves, Professor Haveman, and others -- can fully comprehend what you have done and why. The work will also require students to LOOK at data, to validate analytical methods and make sure they are capturing what we intend.
Students will learn how a project unfolds, and how we discover the stories that we to tell with data. They will also interact with Professor Haveman regularly (usually weekly) and gain experience documenting their work so that others can understand it.
Qualifications: Much of the data is text, so we need apprentices with experience in NLP: regular expressions, named-entity recognition, sentiment analysis, word embeddings, topic models, etc. We also need apprentices with proficiency in Python, experience with ML classifiers, a deep understanding of statistical analysis, and an appreciation for the nuances of managing and analyzing complex datasets.
I value apprentices who pay close attention to detail, are enthusiastic, and can stick to a schedule and follow through on deliverables. Apprentices must have be willing attend carefully to the details of their coding assignments. This required them to inspect the raw data frequently to make sure that their code is doing what it is supposed to do. It also requires them to clearly and completely document their code so that other team members can understand it, and in the future, they can easily revise or reuse it.
Day-to-day supervisor for this project: Jasmine Sanders, William Rathje, Graduate Student
Hours: 6-8 hrs
Related website: http://www.heatherhaveman.net/
Social Sciences Digital Humanities and Data Science