Akshitha Ginuga
Pronouns: she/her
Research Mentor(s): Joelle Abramowitz
Co-Presenter:
Research Mentor School/College/Department: Institute for Social Research / Other
Presentation Date: April 20
Presentation Type: Poster
Session: Session 5 – 3:40pm – 4:30 pm
Room: League Ballroom
Authors: Akshitha Ginuga, Joelle Abramowitz , Jinseok Kim, Sajiv Shah
Presenter: 31
Abstract
With the increase in electronically mediated gig work, many are considering the effect of these work arrangements on people. Studies have tracked the influence of gig work and its popularity, but different studies have found different findings. This study examines the trends regarding human well being and gig work. This study uses narrative data collected in the Panel Study of Income Dynamics by the University of Michigan. We used open-coding to develop a classification schema of work arrangements and used focus coding to classify 3000 narratives into our schema categories. Job category was determined primarily by self-identification but at times required external research. The study uses the subset of classified data to train a machine learning model to categorize the remainder of the data set into the schema categories. This study will reveal how human wellbeing is affected by job security and type of work. While results are not yet available, we anticipate that there will be a correlation between unstable work arrangements and effects on human wellbeing. We also anticipate that certain employment types may be less exhaustive. This study aims to answer previously unsolved questions regarding the connection between human wellbeing and gig work.
Interdisciplinary, Social Sciences
