States Reported Conducting Numerous Data Matches for SNAP and the Matches They Find Most Useful Provide Real- Time Access to Recent Data

		All States Conduct a Range of Data Matches for SNAP, and Most States Have Implemented the Recently Required New Hire Data Match

In response to our survey, all states reported conducting multiple data  matches for income information that they use to determine SNAP  eligibility. Data matches that do not have the characteristics that states found  particularly useful can be used as leads to detect income that households  may not have reported. States Reported Challenges with Following Up on Data Matches, Using Data Matches for Multiple Programs, and Costs

		Necessary Follow-Up on Data Matches Is Challenging, Especially Related to Earnings and Out-of-State Benefits

In our survey, we asked states about overall challenges for the income- related data matches they use, and the issue the most states found very  or extremely challenging was following up to verify information provided  by those matches. These  secondary data sources are often collected for multiple purposes and  thus may not be sufficiently recent, accurate, or complete for SNAP  eligibility determinations. Unlike several data matches for  unearned income, all data matches for earned income lack one or more  characteristics that are useful for determining SNAP eligibility. More states  reported that costs associated with The Work Number were very or  extremely challenging than they did for other national data sources. Some states limit their use of The Work Number or do not use it at all due  to costs, according to interviews with state and FNS officials and  comments from states in response to our survey. FNS Has Efforts Underway to Promote the Use of Data Matching to Improve SNAP, but May Be Missing Some Opportunities

		FNS Efforts Include Initiating Demonstration Projects and Pilots, Promoting Program Integration, and Working with CMS to Reduce Duplicative Processes

			SNAP Demonstration Projects and Pilots

FNS has initiated several demonstration projects or pilots aimed at using  data matching to improve client access, program integrity, or program  efficiencies for SNAP (see table 5). FNS Has Not Widely Disseminated Promising Practices for Data Matching or Assessed Options to Reduce Commercial Data Costs

Despite FNS’ current efforts, 32 states reported that more information  from FNS on promising data matching practices, such as on the use of  different data matches or on ways to filter data to streamline worker  follow-up, would be extremely or very useful, according to our survey. Likewise, officials we  interviewed from three of the six states said that it would be useful for  FNS to facilitate additional information sharing on state practices, such as  implementation issues, so they could be aware of how other states were  implementing data matches:
Officials from one state said FNS could provide information about  whether other states were effectively using data matches that they  found challenging to implement in their state. Further, although FNS is beginning to explore ways to reduce the costs of  The Work Number for state SNAP agencies by working with CMS to  expand its use through the Hub, it has not yet systematically analyzed  spending and SNAP data needs for this service and more thoroughly  considered how various factors would affect costs. Without such an approach, FNS will not be able  to identify the best ways to leverage the government’s buying power  through strategic sourcing practices and potentially reduce costs and  improve performance. Recommendations for Executive Action

We recommend that the Secretary of Agriculture:  1. Take additional steps to collect and disseminate information on  promising practices that could help improve data matching processes  among state SNAP agencies, including broad and timely  dissemination of information on results of recent relevant pilots or  demonstrations. Work with HHS (as appropriate) to analyze spending and understand  data needs for SNAP across federal and state contracts and in  relation to other programs as FNS explores ways to potentially reduce  the costs of using commercial data services.
Why GAO Did This Study

During fiscal year 2015, state SNAP agencies provided about 46 million low-income individuals approximately $70 billion in federally funded benefits, and an additional $7.6 billion in federal and state funds was spent in administering the program in fiscal year 2014, according to the most recent data. SNAP agencies use data matching to verify eligibility information about applicant or recipient households, including their incomes, as well as to help detect improper payments. GAO was asked to review issues related to data matching in administering SNAP.
This report examines (1) the extent to which states use data matching to obtain income information and find these matches useful for SNAP eligibility, (2) challenges states experience using data matching, and (3) actions FNS has taken to promote data matching for SNAP. GAO surveyed all state SNAP directors for a 100 percent response rate and interviewed state officials in six states that varied in caseload size, geography, and other criteria, and visited local offices in three of these states. GAO also reviewed relevant federal laws, regulations, and agency documents and interviewed agency officials.

What GAO Found

In administering the Supplemental Nutrition Assistance Program (SNAP), all state SNAP agencies verify household income by conducting multiple data matches, which they find useful for detecting potential discrepancies related to SNAP eligibility (see figure below), according to GAO's survey of all state SNAP directors. Most states reported that particularly useful data matches provided current information, can be accessed in real-time (i.e., immediately), and are from original sources. Some data sources for unearned income, including from the Social Security Administration, have all these characteristics. Data matches for earned income lacked one or more of these useful characteristics, but can be used as leads to follow up on with households or employers.
States identified challenges with following up on data matches and with the costs of data matching. The issue states cited most often in GAO's survey as very or extremely challenging was the need to conduct follow-up for data that are not sufficiently recent, accurate, or complete, which can be cumbersome and time-consuming. Officials GAO interviewed in several states were implementing ways to manage follow-up. Over one-third of states also reported that costs associated with accessing certain commercial data to verify earnings were very or extremely challenging, with some states limiting their use of these data due to costs.
The Department of Agriculture's (USDA) Food and Nutrition Service (FNS), which oversees the SNAP program, has efforts underway to promote data matching to improve program administration, but may be missing some opportunities. For example, FNS has initiated pilot or demonstration projects to improve program integrity or service to households. However, FNS has not actively collected or disseminated information on promising data matching practices, consistent with federal internal controls. Further, 32 states reported in GAO's survey that more information from FNS on promising data matching practices would be extremely or very useful. With more information, states will have increased awareness of other potentially useful or cost-effective practices. In addition, FNS has begun to explore ways to help states reduce the cost of using commercial data, but has not systematically analyzed spending and SNAP needs for these data to consider how to best leverage government buying power through strategic sourcing practices. Without this analysis, FNS may not be able to identify the best ways to lower data matching costs.

What GAO Recommends

GAO recommends that FNS disseminate information on promising practices to state SNAP agencies, and analyze spending and data needs as it explores ways to reduce costs of using commercial data. FNS agreed with these recommendations