Collaborative Workshop on Socioeconomic Inequality
84 Church St SE
Minneapolis, MN 55455
CLA’s Data Science Initiative will hold a half-day collaborative workshop with the aim of bringing together researchers from across CLA who are interested in socioeconomic inequality, broadly defined.
- Two keynotes
- Ilenin Kondo, Federal Reserve Bank of Minneapolis, "A More Equal Union? Exploring Granular Income Inequality and Mobility by Place, Gender, Race, and Ethnicity using IDDA."
- Bianet Castellanos, U of M Department of American Studies, Institute for Advanced Study
- Brief presentations by CLA researchers
- A presentation from the UMN Data Science Initiative and CLA Data Science Initiative about new seed grant opportunities
- Roundtable discussions among participants
- Plenty of opportunities for interacting with other researchers on the topics at hand
- Immigration and labor shortage
- Labor and wages
- Health inequality
- Race and gender inequality
- The politics of access to data itself
- And more!
We welcome both participants who currently work with big data and/or data science, as well as those who do not work with data but who might be interested in doing so in the future.
Registration will close November 9 or when the event reaches capacity. Please note that if the workshop is in high demand, first priority be given to CLA registrants.
Submission deadline: October 6 (extended)
We seek submissions from CLA faculty for 5-minute presentations of research on topics related socioeconomic inequality. Non-tenure-track CLA faculty, postdocs, and researchers are also welcome to apply. We encourage submissions from those who currently work with big data and/or data science, as well as those who do not work with data but who might be interested in doing so in the future.
Learn about each activity.
Welcome address from the organizing committee.
Illenin O. Kondo, Federal Reserve Bank of Minneapolis
A More Equal Union? Exploring Granular Income Inequality and Mobility by Place, Gender, Race, and Ethnicity using IDDA
Shifting earnings inequality among U.S. workers over the last five decades has been widely studied, but understanding how these shifts evolve across smaller groups has been difficult. Publicly available data sources typically only ensure representative data at high levels of aggregation, so they obscure many details of earnings and income distributions for smaller populations. We define and construct a set of granular statistics describing income distributions, income mobility and conditional income growth for a large number of subnational demographic groups in the U.S. for a two-decade period (1998-2019). The full set of statistics that we construct is available publicly as the Income Distributions and Dynamics in America, or IDDA, dataset. We use the resulting data to explore the evolution of income inequality and mobility for detailed groups defined by place, gender, race, and ethnicity. We find that patterns identified from the universe of tax filers and W-2 recipients that we observe differ in important ways from those that one might identify in public sources.
Talk details to be announced.
Take a stretch break and use the opportunity to talk with colleagues about morning keynotes and related topics.
Talk details to be announced.
Galin Jones, chair of CLA's Data Science Initiative will share information about seed grant opportunities.
Attendees will be encouraged to discuss topics from the morning presentation over lunch. Tables will be arranged into topic areas.
Event organizers share conclusions and any follow-up opportunities.
Please contact CLADSI@umn.edu with any questions you may have.
Michael Gallope, Department of Cultural Studies & Comparative Literature
Fatih Guvenen, Department of Economics
Melissa Polonenko, Department of Speech-Language-Hearing Sciences