How one state uses data to enhance assistance to low-income families

When people fall into poverty, a program called Temporary Assistance for Needy Families (TANF) is available to help lift them back up again. Created in 1996, TANF provides block grants for states to use at their discretion, within certain parameters, so long as the recipients of the financial aid are actively engaged in one of the eligible activities of participation, such as working or being in school, and additionally taking care of children under the age of 18.

TANF fills an acute need in one southern state where over 13% of the state’s overall population lives below the poverty line. Once a family is enrolled in TANF, they receive a cash grant. Additionally, adults who need work support are referred to a third-party career sourcing agency, which helps them obtain the skills and experience they need to maintain a career. Maximum program benefits last up to 48 months, so enrollees have enough time to establish themselves and eventually can support their family over the long term. Currently, this state has over 30,000 families enrolled in TANF.

The objective of the program is for families to move “from welfare to work.” While on the program, many eventually emerge from poverty. Yet some may struggle to escape it entirely — upended by sudden setbacks like a job loss, a car breakdown, or childcare issues — and they end up back on TANF. Since 2016, about 30% of families in this state have returned to the program within the first two years.

Since the inception of TANF, states have lost insight into the specific challenges of the most vulnerable families they serve, such as how to gain the skills required in our digital era. This state wanted to begin measuring actual outcomes for families, not just the number of people in the program and dollars spent. They were curious which households remained financially stable and independent after exiting the program — and why. This data would provide insights into each family’s unique circumstances, revealing common barriers and recommendations into how TANF requirements could be altered to maximize long-term success for families.

This is where Ernst & Young LLP (EY) came in. EY was selected to help analyze the opportunities around TANF because we have a proven track record of evaluating the effectiveness of safety net programs and modeling policy interventions for the government and public sector. EY helped equip the state’s decision-makers with an analytics tool that highlights the policy impacts made by legislatures in these types of government programs. Behind the data, families are facing real challenges — and helping them delivers higher employment rates, less crime, more children living in stable homes and more secure communities. EY wanted to work together with this state government to explore solving some of these long-standing critical issues.

EY has demonstrated how data analytics and science can empower states to get better results from government assistance and drive greater outcomes for the families who need them most — and not just within TANF. These capabilities can be used to continue assessing family needs, research open grants, leverage evidenced data to develop grant applications, and monitor outcomes. Governments can apply a robust data analytics capability in the decision-making process to see which approaches have had the greatest success and build from those actions. With a data-driven state of mind and modern tools, state officials can operate from a position of crisis prevention instead of crisis reaction.

Working with this southern state, EY leveraged the power of PoliQ to test impacts of several potential policy changes, including:

1. Localizing eligibility for temporary cash assistance at the county level rather than statewide: This would increase the wage limits for TANF recipients in more urban and higher cost of living areas (areas with higher self-sufficiency standards), allowing them to stay in the program longer and thus increasing their chances of success.
   
   
2. Localizing eligibility for transitional services (including childcare, education, transportation, and health care): Today, families may decline a promotion or raise for fear of breaching income requirements and losing government aid. This policy change would allow households that are moving positively toward graduating from TANF to use transitional services longer in a more gradual approach to exiting the program.
   
   
3. Increased limits for transitional childcare (to three years, from two years): Parents would have an extra year to obtain wage growth, job stability and save money.
   
   
4. School readiness copayment exemption for children up to age 5 for low-income families: A parent enrolled in TANF and complying with program requirements would not have to pay these fees.

EY also identified and analyzed other opportunities for future governmental considerations, including but not limited to increasing income eligibility limits, allowing the pursuit of a GED as a core educational activity rather than non-core, and pursuing more data transparency agreements with other aid agencies and transportation stipends.

By maximizing the number of families who graduate from TANF and reducing recidivism, more funding becomes available for new families in need — and helps them stay above the poverty line with stable jobs. The opportunity extends beyond this one state: this data transformation could be rolled out across TANF programs nationwide, or even used at the federal level, and can be customized for other governmental aid programs.

“Each time you help a family with kids in need get back on their feet, not only are the parents likely to be more successful, but their kids are also positioned to be more successful later in life and better contribute to their community and society,” Culley said. “By improving the effectiveness of TANF with EY’s modeling tool, we are hopefully helping future generations succeed across the country.”