The GAO concluded in a report issued in early-2021 that there are “advantages…in the use of data verification for both agencies and beneficiaries… For beneficiaries, agencies’ use of data to verify income or assets can reduce documentation beneficiaries must submit or help them receive benefits more quickly, according to GAO’s review of studies.” In short, the GAO concluded that, in many cases, federal agencies that oversee a number of income-dependent benefit programs, like energy assistance and housing assistance “may be missing opportunities to help state or local administering agencies enhance their data verification.” Data verification and “data analytic practices, can enable programs to identify potential fraud or improper payments…”
For agencies, data verification can help reduce improper payments and improve administrative efficiencies, particularly when data are timely and accurate. However, agency officials GAO interviewed also cited challenges including cost and inconsistent data quality that can create inefficiencies. For beneficiaries, agencies’ use of data to verify income or assets can reduce documentation beneficiaries must submit or help them receive benefits more quickly, according to GAO’s review of studies. However, beneficiaries may experience benefit delays and increased burden if there are data discrepancies to resolve. Federal agencies have made some efforts to address challenges, such as identifying ways to reduce data service costs.
In 2021, the GAO conducted a review of several programs that assist low-income individuals and concluded, among other things, that better data matching can prevent fraud, waste, and abuse, and can better direct funds to people that really need it. In a report, the GAO looked at Earned Income Tax Credit (EITC), Housing Choice Vouchers, Low Income Home Energy Assistance Program (LIHEAP), Medicaid (Modified Adjusted Gross Income (MAGI) eligible), Supplemental Nutrition Assistance Program (SNAP), and Supplemental Security Income (SSI). All of these programs have income eligibility requirements.
The amount of improper payments made for a number of federal assistance programs is staggering. In 2019, improper payments for four programs was around $90b: Medicaid ($57.4b), the Earned Income Tax Credit (EITC) ($17.4b), Supplemental Security Income (SSI) ($5.5b), and SNAP ($4b).
For energy assistance programs, the GAO added that “[b]ased on [its] review of state plans, 13 agencies administering LIHEAP reported using no electronic data to verify beneficiaries’ income, verifying income in other ways, such as checking beneficiaries’ documents.” The report added that while the U.S. “Department of Health and Human Services (HHS) has encouraged LIHEAP agencies to use electronic data to improve program integrity, [it] has not taken recent steps to share information that could facilitate its use. HHS officials said that doing so could help state agencies’ verification efforts.
For housing assistance programs, the GAO noted that while “state [and] local public housing agencies administering Housing Choice Vouchers have the flexibility to use other data sources [to verify benefits information…the [U.S.] Department of Housing and Urban Development (HUD) has not made efforts to better understand or share information on the use of other data sources that could further enhance efficiency or accuracy in verifying beneficiary income.” This lack of verification is unfortunate because a HUD official who oversees the housing choice voucher system said that “[a]dditional efforts could help housing agencies learn about ways to enhance their current data verification practices…”
U.S. Gov’t Accountability Off., GAO-21-183, Federal Low-Income Programs: Use of Data to Verify Eligibility Varies Among Selected Programs and Opportunities Exist to Promote Additional Use, 14 (Feb. 2021).
Eric J. Ellman is Senior Vice President for Public Policy and Legal Affairs at the Consumer Data Industry Association (CDIA) in Washington, DC. He also served for eight months as Interim President and CEO of the Association. More