Exploring new understandings of the cost of living at a basic needs level using the Self-Sufficiency Standard database
Project leads: Annie Kuclick, Research Coordinator, and Lisa Manzer, Director, Center for Women’s Welfare, University of Washington School of Social Work
Data scientist: Bryna Hazelton
DSSG fellows: Azizakhon Mirsaidova, Priyana Patel, Cheng Ren, Hector Sosa. Get the participant bios here.
Project Summary: The best-known measure of income adequacy, the Official Poverty Measure, (OPM) is too low with flawed and problematic methodology. Since its original development in the 1960s, societal changes have not been reflected in the measure’s calculation. The OPM does not vary geographically or by age of children, meaning someone is considered “poor” at the same threshold in Sioux City, Iowa and South Manhattan, New York. As the OPM is used to set eligibility for critical benefits (e.g. food assistance, child care subsidies, or housing vouchers), many families unable to afford their basic needs are not considered “in need” by the OPM and cannot access these supports.
The Self-Sufficiency Standard provides an alternative to the OPM by defining the income working families need to meet their basic necessities without public or private assistance. The Standard is widely used to understand issues of income adequacy, create and analyze policy, and help individuals striving to meet their basic needs. The Standard’s aim to be a suitable replacement to the OPM cannot be realized without the ability to host each state’s data in a single database. Having this national resource for the first time would allow community partners to access historical trends, research regional comparisons, and provide a framework for analyses—something frequently requested.
The main intellectual challenge of this project will be to design a database that will house the Self-Sufficiency Standard for all 42 states and all the years in which it has been calculated. The second component will be connecting other administrative databases to provide comparison, such as HUD’s median income levels or food insecurity data by county. Once the database is designed, possible investigations are endless. Students and staff will consult with community partners using the data to design additional queries of the data.