Policymakers rely heavily on the tax system to distribute direct payments to lowincome families. Anti-poverty tax credits such as the Earned Income Tax Credit, the advanced Child Tax Credits, and the federal stimulus payments combined to keep millions of Americans out of poverty during the pandemic. Such credits have strong potential to continue to reduce poverty.
These credits only work, of course, if eligible families receive them. To do so, they must file a tax return. But many low-income families who are at or below the federal poverty level are not legally required to file taxes. Policymakers need a better understanding of how many low-income families don’t file taxes (and therefore miss out on these valuable credits) in order to address this problem.
While state and federal tax agencies know who files taxes, they have very little information on the families who do not file, especially those below the poverty level with little or no earnings. State and local human-service agencies, however, serve many families below the poverty level, placing them in a unique position to assist eligible families to receive these credits.
To help the State of California understand who may be at risk of not receiving anti-poverty tax credits, the California Policy Lab (CPL) facilitated a linkage of two individual-level datasets held by state agencies: one with safety-net enrollment data and one with state tax filing data. CPL served as a trusted third party by implementing a “hashed linkage” — linking data that was de-identified through “hashing” (a one-way encryption process) by each agency.
By linking this data, we were able to help California measure how many Californians receiving safety-net benefits were at risk of not receiving federal stimulus payments, the state Earned Income Tax Credit, and the advanced Child Tax Credit. We also helped the state learn that the majority of its safety-net beneficiaries were already receiving benefits through tax filing — allowing the state to focus limited resources on those who were not receiving these benefits. This linked data also equipped the California Department of Social Services (CDSS) to conduct targeted outreach to Californians who had not filed state returns (and therefore were missing out on thousands of dollars in credits) in recent years and to direct them towards intensive resources that could help them file a return and claim these credits. This linkage led to millions of dollars in tax credits delivered to Californians who otherwise may not have received them.
The benefits of linking administrative data go beyond the take-up of anti-poverty tax credits. Administrative data can help answer many vexing policy questions faced by policymakers. However, much of the value in administrative data can be obtained only when data can be linked across multiple sources. By linking across systems at the individual level, administrative data, which is often topically narrow, can replicate the cross-domain scope of survey data.
This toolkit is intended to help staff in state governments outside of California who are interested in using administrative data and linking it across agencies to measure the take-up of safety-net benefits. We are also releasing a technical how-to toolkit for those interested in operationalizing a hashed linkage.
This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278
