By: Emily F. Keller

Determining the income level that working families need to meet their basic needs requires a dynamic approach. An interdisciplinary team at the University of Washington (UW) Data Science for Social Good (DSSG) program is laying the groundwork for the integration of multi-sourced data from across the U.S. to provide researchers and policy makers with an expanded breadth of opportunities for analyzing complex information in new ways.

The project is centered on designing and testing a database for the Self-Sufficiency Standard, a project of the Center for Women’s Welfare at the UW School of Social Work that determines the income level required for working families to meet their minimum basic needs, based on family composition, children’s ages, and geographic differences in the cost of living. The Standard provides an alternative to the Official Poverty Measure that is currently used to calculate eligibility for benefits such as housing subsidies and food assistance across the U.S.

The DSSG program brings together students, stakeholders, data scientists and domain researchers to work on project teams for a 10-week period. The project “Exploring New Understandings of the Cost of Living at a Basic Needs Level Using the Self-Sufficiency Standard Database” is one of our projects hosted this year by the eScience Institute. The project team includes four student fellows from universities around the country, a data scientist at the eScience Institute, and the project co-leads Annie Kucklick, Research Coordinator, and Lisa Manzer, Director of the Center for Women’s Welfare.

Incorporating Stakeholder Perspectives

The DSSG program incorporates stakeholder perspectives through direct meetings early in the program. The team met with a research director at the Colorado Center on Law and Policy that uses the Standard to help Coloradans working in low wage jobs receive better wages, and support employment through child-care assistance and refundable tax credits. They also met with a special grants project manager for the Workforce Development Council of Seattle-King County, which uses the Standard in their Calculator tool that provides financial counseling to customers, and evaluates customer outcomes such as measuring the number of people who reached self-sufficiency by gender, age, race and ethnicity. Finally, the team met with the founder of the Standard, Dr. Diana Pearce.

Team members said the stakeholder meetings provided them with a deeper understanding of the methodology of the Standard and how it is used in practice, as well as helping them understand the potential uses of the database they are creating, which has informed the database design.

Fellow Priyana Patel, a master’s student in Human Centered Design and Engineering at the UW, said, “Meeting with stakeholders who utilize the Self-Sufficiency Standard has allowed our team to understand the power dynamics within the context of the budget-based tool and the project itself. Dharma Dailey, the program’s Human Centered Design Mentor, has encouraged us to take ownership of what social good means and who should be engaged in this public process. Conversations with our project leads and partners have elevated how we can strengthen the capacity to make data more transparent for users with varying technical backgrounds.”

Fellow Hector Joel Sosa, a Ph.D. student in Social Psychology at the University of Massachusetts – Amherst, said, “Meeting with stakeholders was a very crucial component of the program. Specifically, meeting with stakeholders gave our team insight into how the Self-Sufficiency Standard is applied and used to help others. This includes meetings with our team leads and other community partners, which showed us that the Self-Sufficiency Standard is used to create reports which can then be shown to government officials to push for more just legislation.”

Interdisciplinary Learning

Project co-lead Annie Kuclick described the mutual learning she experienced and observed as part of the team. “I have really seen the fellows playing to their strengths and also taking the opportunity to teach one another. It seems like a lot of learning is happening as a result of the diversity of professional backgrounds,” she said. “This process has really cultivated my own expertise with our data and processes – the best way to learn is having to teach others. I look forward to becoming more familiar with the coding environments the students are using and becoming more acquainted with Python.”

Fellow Cheng Ren, a doctoral student in the Berkeley School of Social Welfare at University of California, said that improving the data infrastructure for the Standard is an important step towards integrating multiple data sources efficiently, and a task that benefits from the team’s interdisciplinary backgrounds. “Although building databases are mostly coding skills in Python, my experience in social welfare research always reminds me to use simple mathematics and data to reflect social issues for the general public. Thus, I always try to help come up with some intuitive comparison and data visualization,” he said.

Working together, the fellows are putting data from the Standard for all available years and U.S. states into one table. This is a complex task, as the Standard has 719 family types to account for. The team is adding other datasets such as Consumer Price Index regional data for inflation adjustment, a city-county crosswalk, and statistical geographic area designations called Public Use Microdata Areas to enable linkages to data from the American Community Survey. This will make it possible to compare data in new ways, such as looking at family type and income levels across different states, or comparing costs between cities.

Future Opportunities

Team members said that their work on the project has opened up greater opportunities for research about affordability for families, while simultaneously expanding their knowledge.

Fellow Azizakhon Mirsaidova, a master’s student in Artificial Intelligence at the McCormick School of Engineering at Northwestern University, said, “I’m particularly going to integrate collaboration techniques we have widely used while working with teams in various tasks in my future data science teams. In addition, I’ve greatly been exposed to technical stacks such as Python, SQL, SQLAlchemy and extensively working with database systems, which will assist me in forthcoming data science projects.

Bryna Hazelton, a Senior Research Scientist in the UW Physics Department and the eScience Institute, said, “The Self-Sufficiency Standard is very widely used, in large part because it is a fair accounting of the actual costs of living for families. This project is exciting because it will immediately improve the depths and types of analysis that can be done with the Self-Sufficiency Standard and since the standard is used so widely, the impact can be very large.”

Project co-lead Lisa Manzer said, “This project is really opening up a lot of possibilities for how we’ll conduct future analysis on the increasing cost-of-living for low-income families. The database will really streamline and transform how we process our data going forward.”

The DSSG final presentations will take place via Zoom and in person on Wednesday, August 17th from 1:00 to 3:00 p.m. PDT. The event is open to the public. Registration is required. More information is available here.