Topic Modeling Analysis of Federal Paid Family Leave Proposals and Hearings in Congress
Yu-Ling Chang, Professor
Institute for Research on Labor and Employment
Applications for Fall 2024 are closed for this project.
Paid family leave benefits the well-being of workers and their family members (including newborns, adopted children, and ill family members) by offering partially or fully compensated time away from work for family caregiving. However, the U.S. is exceptional because it is the only wealthy country without a national-level paid family leave program(PFL). While other countries, including less wealthy countries, provide some paid maternity leave, the U.S. has a patchwork of inequitable leave programs, including federal unpaid family leave, state-run PFL, and employer-provided fringe benefits. Previous research points out unequal access to and limited usage of state-run and employer-provided paid time off, particularly affecting women and racial minority groups. These disparities underscore the need for a federal policy that ensures equitable access to paid family leave. However, it lacks analysis of the recent Congressional proposals and hearings. Thus, this study aims to explore the topics appearing in the recent Congressional proposals and hearings on federal paid leave policy and examine to what extent the topics address disparities by gender and race/ethnicity. Utilizing text data from Congressional bills and hearings from January 2017 to June 2024, the study employs topic modeling to identify the topics. For validation, I will compare the results with human-coded qualitative content analysis. The findings will inform policy implications for a national paid leave policy, emphasizing the need for inclusive designs that ensure universal access that could ameliorate gender and racial disparities.
Role: 1. Data Collection and Management: manually extract relevant Congressional bills and hearings
2. Data analysis: support summarizing, coding, and analyzing text to help discover themes around bills and hearings about paid family leave (at the federal level)
3. Literature review of existing literature to inform the study
Tasks will be available in accordance with project needs, your individual talents, and your readiness for specific contributions.
Qualifications: 1. Familiarity with literature review and summarization
2. Skills/ relevant experience in qualitative data processing and analysis
3. Ability to work effectively both independently and as a team member
4. Interest in topic areas of paid family leave policy, work-family balance, and gender and racial disparities
Preferred qualifications:
1. Background in working with text data (especially Congressional text data is preferable)
2. Knowledge and background in topic modeling and/or qualitative analysis
3. Comfort with data management skills using Microsoft Excel
4. Experience with Python and/or MAXQDA
Day-to-day supervisor for this project: MinJee Keh, Ph.D. candidate
Hours: 6-8 hrs
Off-Campus Research Site: Students will be primarily performing research activities and attending project meetings remotely in the 2024 Fall semester.
Social Sciences