Case Study

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

EY tools and analytics help governments better understand challenges and maximize success of a temporary assistance program.

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The better the question

How can data help families move from welfare to work?

One progressive state wanted to track outcomes from a government aid program and determine what variables drive success or thwart it.


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.

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The better the answer

Combining discussions and data for a human-centered approach 

An EY team talks to case managers across the state, analyzes the data and builds a tool to gauge the impact of TANF changes without guesswork. 


To maximize the impact of TANF, EY wanted to identify barriers to financial independence through data analysis, look for trends across households and model the impacts of potential policy changes against program effectiveness to ultimately recommend program enhancements. EY began with a research study and analysis of options based on reviewing TANF’s target population, the current statewide workforce ecosystem and program parameters.


The team interviewed local workforce boards and case managers across the state to better understand families’ needs and why they returned to the program. Interviews pointed to many variations in family needs between cities and smaller towns. Frontline case managers raised a wide range of hurdles families face on the path to self-sufficiency, including accessing childcare compatible with parents’ work schedules and finding time to study for a GED (to access higher wage opportunities) while working full-time and taking care of their families.


EY incorporated those perspectives into its analysis of TANF households and considered it along with data obtained from other government agencies across the state. The analysis revealed the extent to which rural families differed from more coastal urban families. For example, a family in a large city may face higher costs of living and more likely resides in an apartment but is able to rely on public transit to get to work. Families in suburban and more rural areas must travel longer distances for work and to childcare, most often using their own mode of transportation. EY also compared public policies within the state to other states to identify policy approaches that could be adapted to the state’s local context.


Additionally, EY explored other aid programs available within the state in terms of their eligibility requirements and benefits granted to recipients. Based on these efforts, EY consultants believed that replacing statewide thresholds for TANF with regional customizations could maximize benefits and address each family’s unique needs and circumstances.


As a “crystal ball” for understanding the impact of different policy options, EY developed a tool called PoliQ. By leveraging machine learning and a “likely to succeed” model, it could predict how households could be impacted and their likelihood of returning to TANF. State governments would no longer have to rely on pencil-to-paper mathematics and spreadsheets. PoliQ can be used to test how changes in individual policies positively or negatively impact households as well as to assess interaction effects across policies.


The EY team used the PoliQ tool to model a range of potential TANF policy changes in the state. PoliQ synthesized TANF recipient data across many years (over 25,000 cases), examined the effects of policy changes on household success rates (likelihood to leave the TANF program and stay off the program), and assessed interactions of policy impacts across the state’s multiple regions. EY also provided training to a number of government and legislative users on the tool and created a learning manual so that they could continue to use it independently, adjusting assumptions on eligibility requirements and wage growth, for example.


“This new tool synthesizes hundreds of pieces of important data instantly, saving government workers thousands of manual hours and delivering more data-driven results,” said Jeri Culley, EY Human Services Leader. “Users can now use the tool to quickly see how they can optimize TANF funding each year for their state and maximize the number of households they can help. And if they continue using the tool to improve the program year after year, it will hopefully create patterns of success across households and communities for this state.” 

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The better the world works

Positioning families for stability, not more fragility 

Complemented with policy recommendations, EY tool offers governments a data-driven way to optimize TANF and other aid programs. 


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.” 

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