Story: Joyelle got an education, a job, and a promotion. She never expected her success would mean this

Story: Joyelle got an education, a job, and a promotion. She never expected her success would mean this

Story: Joyelle got an education, a job, and a promotion. She never expected her success would mean this. . .

Joyelle never expected to be a position where the very system she thought was a safety net ultimately failed her.

 

After fleeing an abusive relationship, this single mother of four ended up in public housing in Lawrenceville, Georgia. Until that point, Joyelle had never relied on welfare for help. She always paid her rent on time and made ends meet. So, falling back on public housing was an entirely new scenario for her. It was not where or how she wanted to live, or where she wanted her four children to grow up. 

That’s why she was determined to get back on her feet. She graduated from school and was offered a full-time job with the state of Georgia, a career trajectory that put her above the poverty line. Things were looking up. 

“I was excited and grateful,” Joyelle says. “I had worked hard: I started out with the state as a student assistant and worked my way up.”

 

Falling over the benefits cliff

But that’s when Joyelle got a shocking surprise: Due to her new salary, her subsidized housing allowance disappeared and she was forced to pay almost $1,000 a month in rent.

“I was heartbroken,” she says of learning that she was losing her housing subsidy. “You work hard. They tell you to go to school and get a job. You do all these things, and you’re still not able to provide for your family. That’s devastating. I suffer from anxiety. It causes stress. It causes severe depression.”

She now faces the difficult decision of looking to move but being unable to afford apartment rent even with her salary increase.

 

 

Hindering upward mobility

Joyelle encountered what we call the “benefit cliff,” where well-intentioned policies actually prevent people from getting off public services. They make just enough to not qualify for services, but not enough to make up for the services lost in extra income. The result is a system that keeps people trapped in poverty rather than one that propels them toward self-sufficiency and the dignity that comes with it.

“There’s no help for people like me, stuck in the wealth gap,” Joyelle shares. “You have help, but if you help yourself you’re faced with adversities that you shouldn’t be faced with.”

We believe that these services should move people into a prosperous life, not keep them stuck in cycles of dependency. Visit welfarecliff.org to learn more about ways to end benefits cliffs so that more Georgians can prosper.

 

How Can You Measure Welfare Program Success? Part 2

How Can You Measure Welfare Program Success? Part 2

How Can You Measure Welfare Program Success?

Part 2

By Erik Randolph

My last blog explained dependency metrics and how they measure the success of welfare programs. However, these metrics are not the complete answer.

By also following people after they leave the system, we can gain a fuller picture of success.

This technique is common for job training programs. In fact, it is a requirement for state and local agencies receiving federal funding per the Workforce Innovation and Opportunity Act, where agencies routinely measure the status and income of persons up to a year after  exiting the job training program. 

Follow-the-person metrics can be used for welfare programs as well, as Kansas and Maine already  demonstrate. 

 

Background on Food Stamp Work Requirements

 When the U.S. economy was recovering from the Great Recession, the states of Kansas and Maine led the nation in reinstating the federal work requirement for “Able-Bodied Adults Without Dependents” (ABAWDs).   

The news media generally criticized the governments of Kansas and Maine for reinstating the rule, claiming it was cruel to push ABAWDs off food assistance. Kansas and Maine responded with follow-the-person data. 

Shortly thereafter in 2015—while Barack Obama was still president—the federal Food and Nutrition Service urged all other states to follow the federal law by reinstating the ABAWD rule. However, most states were hesitant to do so, and they continued seeking waivers and exemptions from enforcing it.

Federal law has two work requirements for the food stamp program. There is the general work requirement for persons ages 19 through 59 with notable exceptions, such as being in school half-time, physically or mentally unfit for employment, or caring for a child under six years of age or an incapacitated person. Under the general work requirement, the recipient must register for work or otherwise have good cause if they are not working at least 30 hours per week or enrolled in a job-training or workfare program. 

The second work requirement is specific to ABAWDs. This rule applies to persons ages 18 through 49, unless they are already exempt from the general work requirement or if they are responsible for a child under 18 years of age, or pregnant. Non-exempt ABAWDs cannot receive benefits for more than three months in a 36-month period unless they work for an average of 20 hours per week on a monthly basis or they participate in an approved “employment and training” program. 

States may, and routinely do, waive the ABAWD rule in areas within their state with unemployment over 10 percent, and they have the discretion to exempt up to 15% of persons from the requirement. 

During the Great Recession, Congress suspended the ABAWD rule until September 20, 2010, but many states continued waiving the requirement well into 2017. 

 

Kansas and Maine Break New Ground 

Under the administration of Governor Sam Brownback, Kansas restored the ABAWD rule in October 2013. The Kansas Department for Children and Families, with the help of the state’s Department of Labor, followed the wages of individuals exiting the food stamp program. Departments of labor typically administer unemployment insurance programs that collect wage data. 

According to a report by the Foundation for Government Accountability, Kansas had 28,144 ABAWDs on food stamps in October 2013. One year later, in October 2014, there were 9,193. The following October, the number dropped to 7,601.

The drop in enrollment among this population may be alarming if one assumes these individuals were worse off, as many in the news media did. However, the follow-the-person data showed otherwise. On average, the annual wages of these individuals rose above the poverty line, from $6,703 in December 2013 to $13,304 in the fourth quarter of 2014. 

 

Maine had a similar experience.

No longer requesting an ABAWD waiver in 2014, the Maine Departments of Labor and Health & Human Services cooperated in following the wages of the 6,866 who did not comply with the reinstatement of the work requirement and exited the program. The Governor’s Office of Policy and Management under the political leadership of Governor Paul LePage analyzed the wage data. Its report showed total wages for this group more than doubled from the third quarter of 2014 to the fourth quarter in 2015. On average, quarterly wages increased from $1,984 to $3,514, also raising the wages of many ABAWDs above the poverty level. 

 

 

What Follow-the-Person Metrics Could Mean for Georgia and Other States

Unfortunately, both Kansas and Maine abandoned the follow-the-person data collection—not for policy reasons related to the effectiveness of the metrics but because of changes in political leadership.

Nevertheless, the states demonstrated that follow-the-person metrics can be applied to welfare programs in addition to job training programs. There is no good reason why Georgia and other states could not also implement follow-the-person metrics for welfare programs by having their welfare agencies cooperate with their departments of labor.

Additionally, states are not limited to using department of labor wage data. They could also initiate surveys to collect more detailed data on the well-being of individuals after exiting a program. 

Do you think it would be good for Georgia to begin using dependency and follow-the-person metrics to measure the success of welfare programs? Let us know in the comments below.

 

Erik Randolph is Director of Research at the Georgia Center for Opportunity. This blog reflects his opinion and not necessarily that of the Georgia Center for Opportunity.

DISINCENTIVES FOR WORK AND MARRIAGE IN GEORGIA’S WELFARE SYSTEM

Based on the most recent 2015 data, this report provides an in-depth look at the welfare cliffs across the state of Georgia. A computer model was created to demonstrate how welfare programs, alone or in combination with other programs, create multiple welfare cliffs for recipients that punish work. In addition to covering a dozen programs – more than any previous model – the tool used to produce the following report allows users to see how the welfare cliff affects individuals and families with very specific characteristics, including the age and sex of the parent, number of children, age of children, income, and other variables. Welfare reform conversations often lack a complete understanding of just how means-tested programs actually inflict harm on some of the neediest within our state’s communities.

How Can You Measure Welfare Program Success? Part 1

How Can You Measure Welfare Program Success? Part 1

How Can You Measure Welfare Program Success?

Part 1

By Erik Randolph

If you want to know how well welfare programs work, ask welfare agency administrators how they measure success. This was suggested by Randy Hicks, President and CEO of the Georgia Center for Opportunity (GCO), years ago. Almost invariably these administrators will answer that they measure success by how many people they serve. When the total number of people they serve goes up, the programs are more successful. Or are they?

To the contrary, program participation does not measure success. Furthermore, the chances are that welfare agency administrators lack the metrics to tell us how successful the programs truly are.

Program participation can measure demand for the program, or it might indicate the number of people in need. In these cases, program participation is useful information. But does it actually measure success? 

The more important goal of welfare programs is to help people overcome their financial difficulties and escape poverty. This enables them to live more fulfilling lives. Public policy should not encourage them to languish on assistance for years on end but rather help them improve their circumstances until they no longer need assistance, or their reliance on assistance becomes lessened. Welfare agencies generally lack metrics to effectively measure this important goal.

Which revises our original question slightly: How can you measure success?

Dependency Metrics

One potential way to measure success is to use dependency metrics that evaluate the percent of the population who are dependent on major welfare programs. This is partially done at the federal level but not at all at the state level.

In 1994, Congress passed the Welfare Indicators Act. It focuses on food stamps, Temporary Assistance for Needy Families (TANF) cash grants, and Supplemental Security Income (SSI). Every year, the U.S. Secretary of Health and Human Services is required to file a report with Congress showing dependency on those three welfare programs.

The most recent report was released in 2018. The pie chart below comes from page eight of that report, showing for the year 2015 the percentages of the national population according to their proportion of their total income dependent on the value of food stamps, TANF cash grants, and SSI. The higher the proportion of an individual’s income that comes from these three assistance programs, the worse off the person probably is. For example, if the value of food stamps constitutes more than 50 percent of an individual’s income, that person cannot be well off financially. In comparison, when food stamps constitute 25 percent to 50 percent of an individual’s income, it means the person has more additional income and is better off than when food stamps comprise more than 50 percent  of total income. And having less than 25 percent of total income coming from food stamps is better than having 25 percent to 50 percent of total income on food stamps.

Georgia has the ability to generate dependency metrics through the Georgia Gateway, including TANF cash grants, food stamps, medical assistance, and two other programs. These are means-tested programs, meaning the Department of Human Services has not only participation numbers but also income information of the applicants and recipients. The Department could relatively easily have its I.T. crew write scripts to spit out reports periodically showing the number of individuals and families by dependency on their income on those programs captured through the Gateway. Coupled with Census data, the Department could produce periodic reports showing how dependency changes over time and further break down the data by demographic groups. 

Furthermore, because every individual has a unique identifier, the I.T. crew could produce additional scripts to follow people over time. This would allow for more sophisticated analytics showing the financial progress of people and families in the system. 

Dependency metrics are not perfect. They do not capture persons who would be eligible for the program but do not participate. However, the number of these individuals are regularly estimated and could be presented as additional information in the analysis. 

Ideally, it would be best if the dependency metrics captured all assistance programs. Currently, this is not possible.

Assistance Programs Breakdown

Exactly How Many Programs Do People Benefit From? 

Often people qualify for multiple assistance programs. Their children might be on Medicaid and receiving free school lunches. At the same time, the household may be receiving food stamps. Additionally, if the parent or parents work, they may be receiving the Earned Income Tax Credit (EITC) and Additional Child Tax Credit. We just listed five programs that welfare families typically receive. 

And there are more programs. If the family has young children under five, they could receive food packages from the Women, Infants, and Children (WIC) program. Additionally, the family may be receiving childcare assistance, Section 8 rental assistance, and/or energy assistance.

Now you might think that we have a dataset somewhere telling us the total number of welfare programs families are benefiting from. If you assumed that we do, you would be wrong. No such dataset exists.

The reason? First, the welfare system is disjointed. There is no single agency or dataset that can tell us the total number of programs people are on. Even Georgia’s award winning Gateway, which is one of the better integrated eligibility systems in the country, cannot tell you. While the Gateway can tell us about food stamps, Medicaid, WIC, TANF, and subsidized childcare services, it is missing the refundable tax credits, free school lunches breakfasts, Section 8 rental assistance, and other welfare programs not listed. 

Second, statistical sources do not include all welfare programs in their questionnaires and have other limitations, such as serious time lags. For example, the American Community Survey asks about food stamps, Medicaid, and Supplemental Security Income but practically none of the other programs, making a statistical inference for the complete picture impossible. 

The Survey of Income and Program Participation gets us closer, giving us childcare assistance, WIC, energy assistance, and public housing, among others. However, it is still missing the refundable tax credits, including the EITC which is one of the big three welfare programs. Worse, SIPP is structured for longitudinal studies that makes the survey totally impractical for monitoring program participation on a regular and timely basis.

Adopting Dependency Metrics in Georgia

Dependency metrics would improve our ability to measure success, and state leaders should consider implementing them in Georgia. 

Georgia would do a better job than the federal government with dependency metrics. The Gateway houses the data for critical programs, enabling Georgia to produce monthly estimates, more timely estimates, and for more programs. In contrast, the Feds apparently cannot meet its obligation in producing annual reports, provides only national data for only three programs, and there are significant time lags. The most recent Federal report came out on May 4, 2018, with 2015 and some 2016 data.

Once implemented at the state level, dependency metrics will improve over time. If and when further integration, consolidation, and streamlining of eligibility systems occur, as recommended by GCO, dependency metrics will become more complete and more useful.

However, they are not the sole answer. There is another way to measure success that would complement well dependency metrics. This will be the topic of my next blog.

In the meantime, do you have ideas on how we can measure success in welfare programs? We would love to hear them. Be sure to put them down in the comments below.

Erik Randolph is Director of Research at the Georgia Center for Opportunity. This blog reflects his opinion and not necessarily that of the Georgia Center for Opportunity.

DISINCENTIVES FOR WORK AND MARRIAGE IN GEORGIA’S WELFARE SYSTEM

Based on the most recent 2015 data, this report provides an in-depth look at the welfare cliffs across the state of Georgia. A computer model was created to demonstrate how welfare programs, alone or in combination with other programs, create multiple welfare cliffs for recipients that punish work. In addition to covering a dozen programs – more than any previous model – the tool used to produce the following report allows users to see how the welfare cliff affects individuals and families with very specific characteristics, including the age and sex of the parent, number of children, age of children, income, and other variables. Welfare reform conversations often lack a complete understanding of just how means-tested programs actually inflict harm on some of the neediest within our state’s communities.