Collaborative Workshop on Socioeconomic Inequality
84 Church St SE
Minneapolis,
MN
55455
Event Overview
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.
Features:
- Two keynotes
- Illenin 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, "Housing Justice: Reflections on Indigenous Organizing, Scaling Up, and the Ethics of Data Collection."
- 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
Potential topics:
- Immigration and labor shortage
- Automation
- Labor and wages
- Monopolies
- Health inequality
- Race and gender inequality
- The politics of access to data itself
- And more!
Get Involved
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.
REGISTER TO ATTEND
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.
SUBMIT A TALK ABSTRACT
Submission deadline: October 13 (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.
Workshop Schedule
Learn about each activity.
Welcome address from the organizing committee.
Keynote Speaker
Illenin O. Kondo, Federal Reserve Bank of Minneapolis
Talk Title
A More Equal Union? Exploring Granular Income Inequality and Mobility by Place, Gender, Race, and Ethnicity using IDDA.
Abstract
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.
Speaker Bio
Keynote Speaker
Bianet Castellanos, U of M Department of American Studies, Institute for Advanced Study
Talk Title
Housing Justice: Reflections on Indigenous Organizing, Scaling Up, and the Ethics of Data Collection.
Abstract
To be announced
Speaker Bio
Bianet Castellanos is Distinguished McKnight University Professor of American Studies and Director of the Institute for Advanced Study. She has spent three decades working with Maya communities in Mexico and California. Her publications include Indigenous Dispossession: Housing and Maya Indebtedness in Mexico (Stanford UP 2021) and A Return to Servitude: Maya Migration and the Tourist Trade in Cancún (UMN Press 2010), along with the anthologies Detours: Travel and the Ethics of Research in the Global South (U Arizona Press 2019) and Comparative Indigeneities of the Américas: Toward a Hemispheric Approach (U Arizona Press 2012).
Take a stretch break and use the opportunity to talk with colleagues about morning keynotes and related topics.
- Fatih Guvenen, Economics
"An Overview of GRID: A New International Database of Inequality Statistics." - Claire Kamp Dush, Sociology
"A Brief Overview of the IPUMS Contextual Determinants of Health Database." - Benjamin Toff, Journalism and Mass Communication
"Mapping Minnesota's Local News and Information Ecosystem." - Aldo Rustichini, Economics
"A GWAS of Political Participation and Party Affiliation." - Ellen Messer-Davidow, English
"Meanings, Metrics, and Other Muddles: The Supreme Court Opinions on the SFFA Cases." - Kazeem Adepoju, Statistics
"An Improved Generalized Probability Distribution for Estimation of Income Related Measures of Inequality." - Rebekah Nagler, Journalism and Mass Communication
"U.S. Public Perceptions of Disparities in COVID-19 Mortality." - Michael Gallope, Cultural Studies & Comparative Literature
"Inequality in the Music Industry."
Galin Jones, Chair of CLA's Data Science Initiative, and Hayley Borck, Managing Director of the UMN 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.
Contact Us
Please contact CLADSI@umn.edu with any questions you may have.
Organizing Committee:
Michael Gallope, Department of Cultural Studies & Comparative Literature
Fatih Guvenen, Department of Economics
Melissa Polonenko, Department of Speech-Language-Hearing Sciences
Partners
College of Liberal Arts Office of Research and Graduate Programs
Institute for Research in Statistics and its Applications
Liberal Arts Technologies & Innovation Services
Sponsors
University of Minnesota Data Science Initiative