What Drives Poverty in Ohio?

As part of our double bottom line mission, Scioto is carrying out a multi-year research project on five key aspects of well-being. With the conclusion of this summer’s cost-benefit analysis on the earned income tax credit, we are shifting from a focus on economic growth to a focus on poverty.

As we started our 2018-2019 research on economic growth, we carried out a study of Ohio’s economy using the Genuine Progress Indicator (GPI) framework, a “GDP+” framework that incorporates environmental and social costs along with traditional economic indicators to gauge economic growth. This study served as a baseline by which to gauge the future economic growth and policy interventions to improve the economy.

As we start working on poverty, we already have two detailed poverty reports at our disposal, the Development Services Agency’s Ohio Poverty Report and the Ohio Association of Community Action Agency’s (OACAA) State of Poverty in Ohio, both published in the past six months. These two poverty reports provide a plethora of data on poverty, food insecurity, housing cost burdens, and other income-related measures disaggregated by race and geography. Between these two reports, we can find most measures of poverty addressed, with the notable exception of relative poverty.

One of the most valuable parts of the OACAA report is a large table with detailed information on the state of poverty in Ohio’s 88 counties. This set of 88 datapoints can help us explore a litany of questions about poverty in Ohio. For this post, let us make just a few surface-level observations about the state of poverty in Ohio.

  1. Poverty is not primarily urban in Ohio.

Using a standard linear regression, there is little relationship between population size and poverty level in Ohio counties. Ohio’s largest counties fall fairly close to the average poverty rates and Ohio’s large number of small- and medium-size counties generally range between 5 and 20% like the larger counties.

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Above, we highlight three reference counties, Franklin County, a large, urban, medium-high poverty county, Delaware County, a medium-large, suburban, low poverty county, and Athens County, a small, rural, high poverty county.

2. Poverty is not primarily minority-driven in Ohio.

Just as we found little relationship between population size and poverty rates, we also find little relationship between what percentage minority a county is and its poverty rate. Poverty rates show more stability with more minority-heavy counties and are slightly higher in those counties, but hardly significantly higher and lower than many small and medium-size counties.

3. Poverty is weakly related to population growth since 2012

About 15% of the variation in poverty rates in the state can be explained by population growth since 2012. It should be noted that causation could run either way in this relationship: while population growth could mean a stronger economy and more opportunities for residents, it also could be a result of lower poverty levels as people move to opportunity.

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Also notable here, though, is outlier counties. Athens County is an outlier in almost every graph we plot because its poverty rates are so incredibly high. A contributor to this is Ohio University: the Census Bureau estimates the Athens County poverty rate would have been 5-10 percentage points lower from 2012 to 2016 if off-campus college students were excluded from the poverty calculation. That being said, even factoring in this adjustment leaves Athens as the highest at worst or one of the highest at best in poverty rates statewide. This is especially surprising considering Athens County is one of the fastest-growing counties in the state.

Franklin County is also impacted by large numbers of college students, though the Census Bureau estimates the impact is only worth 0 to 2.5 percentage points. Even factoring this adjustment in, Franklin County’s poverty rate is much higher than its population growth rate would suggest it should be.

These three tests give us a small peek into what drives county-level poverty in the state of Ohio. Over the next year, we at Scioto Analysis will be working to uncover more of what drives poverty in the state as well as exploring alternative measures of poverty such as supplemental measures and relative poverty measures. If you are interested in talking about state-level poverty and approaches to reduce it, please email me. I’m always looking to connect with folks who are working to understand and reduce poverty at the state and local level.

Thank you to Tong Zhou for data manipulation support that contributed to this post.

Akron Beacon Journal Endorses Refundable Earned Income Tax Credit

On Sunday, the Akron Beacon-Journal and Ohio.com’s editorial staff endorsed making Ohio’s state earned income tax credit refundable.

In their editorial, the newspaper’s editors cited Scioto Analysis’s cost-benefit analysis released earlier this month, particularly citing the public health and education benefits brought to light by the report.

A refundable credit would benefit Ohio overall, a conclusion amplified in a report released by Scioto Analysis last week. For instance, the study finds that a refundable credit at 30 percent of the federal version would result in an average payment of $750. Among other things, the additional resources would lead to more children born at normal weights, reducing the state’s dismaying level of infant mortality. Such a refundable credit would result in more young people succeeding in higher education.

The cost-benefit analysis was a culmination of a summer-long analysis done by Scioto Analysis on the state earned income tax credit. The study was the first best-practices cost-benefit analysis on a statewide program carried out in over a decade.

“Policymakers have a thirst for solid numbers that can help them understand the impacts of the policy options they have,” said Scioto Analysis Principal Rob Moore. “This the first of many new cost-benefit analyses that will be providing vital information to decision makers in the state.”

What will the impact of Ohio’s Recent EITC Expansion Be?

By Rob Moore and Tong Zhou

The federal earned income tax credit (EITC) is one of the most successful antipoverty programs in the United States, lifting 6 million Americans out of poverty each year by giving low-income families a break on their taxes. Seeing the success of the EITC at the national level, 29 state governments have instituted their own earned income tax credits, including Ohio.

In wrangling over this year’s high-profile gas tax increase, some opponents of an increase worried about the impact of a regressive tax increase on low-income families. To offset these impacts, the Ohio General Assembly expanded the earned income tax credit, increasing the federal match from 10% to 30% and nearly doubling the expected size of the state earned income tax credit.

One feature of the current state earned income tax credit in Ohio is that it is nonrefundable, meaning the value of the credit cannot exceed the taxpayer’s tax liability. While this keeps costs of the program down, it also minimizes the impact of the program. Because of this, some have advocated for a refundable earned income tax credit. On top of this, when Scioto Analysis surveyed the Ohio legislature earlier this year, legislators asked about refundable tax credits more than any other topic.

In order to assess the impact of a refundable earned income tax credit, Scioto Analysis built a model this summer, which resulted in the cost-benefit analysis on refundable credits released last week. In this paper, we model the budgetary and wage impacts of the program, then project and monetize four key economic impacts: tax distortions, labor market distortions, birthweight benefits, and college enrollment benefits.

Using LSC estimates for the cost of the EITC expansion in the transportation bill, we can use our model to predict the impacts the change that was made this year. Tables 1 and 2 below show the monetized impacts of the separate policies. Table 3 shows the impact of the change from 10% nonrefundable to 30% nonrefundable.

Basically, by using past studies on the EITC and its impacts on public health and education, we can estimate that the expansion will lead to a $45 increase in wages per low-income worker, about 2,000 new workers entering the workforce, about 5 less instances of low birthweight per year, and 11 new college enrollees per year. This means the change in the EITC in the transportation budget will generate a net benefit of $4 million per year over the status quo 10% nonrefundable policy.

While the recent policy change likely had net benefits, refundability would yield net benefits anywhere from 3 to 15 times higher than the policy change this year. They also would cost the state much more, creating political feasibility issues for such a change in the future.

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Tax Credit Refundability Would Yield Economic, Education, and Health Benefits

(Columbus, OH) – On Friday, Scioto Analysis released a cost-benefit analysis of proposals to make Ohio’s earned income tax credit refundable.

The cost-benefit analysis incorporated findings from policy research studying past earned income tax credit expansions and elements of its design to measure human development impacts of the policy.

“In this analysis, we found that refundability reforms would put an extra $150 to $900 in the pocket of the average low-income Ohioan,” said Rob Moore, principal for Scioto Analysis. "It would also bring anywhere from 3,000 to 60,000 new workers into the workforce, would prevent 20 to 120 cases of low infant birthweight per year, and would lead to 40-230 new college enrollments every year, generating anywhere from a $5 to $130 million in new economic activity,”

This analysis sheds new light on the state earned income tax credit, showing for the first time that human development benefits tied to education and health outweigh the tax distortion costs and potential labor market costs levied by the earned income tax credit.

“In all 10,000 simulations we ran of the change from a 30% nonrefundable to a 10% refundable tax credit, we found the change in policy would have net economic benefits,” said Tong Zhou, co-author of the analysis. “This was also the case in all but 7 of the 10,000 simulations we ran of the change to a more robust 40% refundable tax credit.”

This paper represents the first best-practices cost-benefit analysis on a state policy in Ohio in over a decade. Scioto Analysis looks forward to promoting the use of more cost-benefit analysis in the upcoming years.

For more information, please contact Rob Moore, principal, Scioto Analysis, (614) 743-1840, rob@sciotoanalysis.com

Tips for Conducting Cost-Benefit Analysis

By Tong Zhou

Politicians always argue that their policies will benefit citizens and society. However, we often have little information to assess how right they are about these claims. But how do we know when these claims are correct or not?

To create a standardized approach for objectively assess the economic efficiency of public policies, economists created cost-benefit analysis, a tool for weighing the economic costs and benefits of proposed policies. In this post, I will provide some tips for beginners doing cost benefit analysis.

Firstly, let’s look at the cost benefit analysis as laid out in Boardman et. al’s Cost-Benefit Analysis: Concepts and Practice. The following nine steps summarize the process of cost benefit analysis:

1.     Define the policy that we are analyzing and the set of alternative policies that could be adopted in place of the policy. 

2.     Define who has standing in the study, or whose benefits and costs count for the analysis. Sometimes the standing can be the citizens of a city, sometimes the standing can be broader like all human beings on Earth. Standing depends on the specific policy project.

3.     Then, we must determine all the impacts caused by the policy. Any policy will have benefit categories and cost categories just as a coin has two sides. We want to be as objective as possible in this process as biased determination of impacts can make your analysis meaningless. Sometimes we can be guardians and spenders unconsciously. Peer review is a good way to avoid this problem.

4.     Now we can quantify the impacts found in the last step. For example, in the study we are doing this summer, we found that the Earned Income Tax Credit (EITC) will increase the number of workers in the labor market. In this step, we want to determine how many more workers will enter the job market. Sometimes it can be hard to translate impact categories into numbers as translation requires serious analysis and literature review to find an accurate number. You will read a lot of literature to find the best sources of data and effect sizes. Reliable sources play a significant role here: we must stand on the shoulders of giants in cost-benefit work.  

5.     Monetize all impacts. Some people may argue that not everything has a price: things like time and life are often considered “priceless.” However, people make tradeoffs of their time and risks of life all the time. We can survey people to ask them their willingness to pay for these goods. Monetization is a key step in any cost-benefit analysis because it allows us to compare seemingly incomparable outcomes.

6.     Even though we have the monetized all impacts, not all benefit or cost are realized today. In order to get an accurate estimation of present value, or how we value these future outcomes now, we will discount all impacts occurring in the future. The discount rate usually ranges from 3% to 7%, though this is a point of contention in the world of cost-benefit analysis. 

7.     Finally, we have determined the cost and benefits of the policy. Simply subtract costs from benefit to calculate the net benefit. A positive net benefit suggests the policy is economically efficient, while a negative net benefit suggests the policy is economically inefficient.  

8.     Even though we have determined the results, we still want to know how accurate our results are. To do this, we perform sensitivity analysis such as a Monte Carlo Simulation, the best practice in determining the accuracy of cost-benefit analytical outcomes. Sensitivity analysis is a tool that will tell us the most likely net benefit.

9.     The final step is to report your findings in a format that provides guidance to policymakers. 

After carrying out all the steps above, we want to double-check that the policy impacts we found that are directly caused by the policy. We do not want to count correlated impacts that would not bear out as causal in a final report. Even though researchers may find that Nobel Prize winners usually eat more chocolate than normal people, eating more chocolates will not necessarily increase your likelihood to win a Nobel Prize. 

Although the steps listed above seem pretty straightforward, it is very easy to still encounter uncertainties in the model which may undermine the accuracy of the final results. For example, some impacts are more important than others so we might need to weight the impact for policymakers. Prices may fluctuate over time so we need to use the current price. My suggestion is to spend some time reading how others do cost-benefit analysis. Finding other similar examples can not only teach us innovative ways to deal with uncertainties but also let us compare different ways to find the best one.

My final suggestion for beginners is to keep practicing cost-benefit analysis. We cannot truly master it unless we practice. Practice goes a long way and only by refining our craft will we improve at it.

 Tong is a Data Analysis Intern at Scioto Analysis and a data science student at Denison University.

Moore Appears on The Wonk Podcast

Last week, Scioto Analysis Principal Rob Moore appeared on the Association for Public Policy Analysis and Management’s podcast The Wonk speaking about the use of cost-benefit analysis in the state of Ohio.

In April, Scioto Analysis released a report assessing the use of cost-benefit analysis in the state of Ohio from 2012 to 2018, building off a 50-state survey released by the Pew Charitable Trusts in 2013. In this study, Scioto Analysis found that there had not been a single best-practice cost-benefit analysis carried out in Ohio in the past decade.

“Policymakers aren’t getting data on the economic impacts of their policies,” said Moore on the podcast.

Other approaches to policy analysis such as cost-effectiveness analysis, well-being metrics, and measures of poverty and inequality were also discussed on the podcast.

“People who care about government working well should be interested in the use and prevalence of cost-benefit analysis because it provides something that we can argue about and discuss that is a bit more objective,” said Moore.

Later this summer, Scioto Analysis will be releasing a best-practice cost-benefit analysis of Ohio’s state earned income tax credit, exploring the implications of proposals to make the credit refundable.

Housing Fund Won't Solve Housing Affordability on Its Own

Late last month, a group of business and community leaders announced a $100 million fund for construction of new affordable housing in Franklin County.

The fund is financed entirely by private-sector investors and will provide low-cost loans to developers who agree to affordability requirements in their rental pricing.

It’s good to see private-sector players taking action on housing affordability, but these are just low-cost loans, meaning the true value of the contribution will likely shake out to only a few percentage points in reduced rates, or a few million dollars in actual subsidies.

This is likely to be a drop in the bucket when it comes to reducing housing cost burdens. A 2017 study by the Affordable Housing Alliance of Central Ohio found that in 2013 almost 100,000 Franklin County families were housing cost burdened, spending more than 30 percent of their income on rent. The number is likely even higher now with Franklin County’s population growth and increasingly hot housing market.

So what can we do to ease the burden of housing costs locally?

A recent analysis by Brookings Institution researcher Jenny Schuetz found that poor households in a sample of heartland metro areas pay almost 70 percent of their incomes towards housing. Schuetz said that direct subsidies will be needed to bridge the gap between incomes and monthly housing costs for the poorest families.

Across the country, other city councils and mayor’s offices are also looking at zoning codes. Often designed around increasing housing values and tinged with the dark past of racial segregation, zoning codes are getting major overhauls in cities like Minneapolis, Philadelphia and Seattle, as well as statewide reforms in California and Oregon, where lawmakers have angled to ease housing cost burdens by making it easier to build new housing in expensive cities.

Columbus’ “Insight 2050 Corridor Concepts” study released this spring suggested that easing zoning restrictions on the Broad/Main east/west corridor, Cleveland Avenue, Olentangy River Road and the Parsons to Groveport Road corridors could lead to reduced transportation, utility and environmental costs, in addition to the lower rental costs that density provides.

Another option would be to look at the other side of the cost burden leger: incomes. While reducing the cost of housing through targeted interventions is one way to reduce housing cost burdens, interventions that increase incomes can give families resources to pay for housing and other necessities they may have.

Programs like direct cash assistance, targeted economic development incentives and early childhood education investments are good tools to increase incomes. Economist Timothy Bartik has found that improving test scores, increasing educational attainment, improving public health and reducing crime can also boost local wages.

All in all, a few million dollars in private loan subsidies shouldn’t hurt families, but it falls far short of the impact city and county officials could have on reducing housing cost burdens for Columbus families.

This column originally appeared in Columbus Alive.

County’s poverty reduction blueprint ambitious, challenging

Earlier this month, the Franklin County Commissioners outlined a 120-point plan to fight poverty in Ohio’s largest county.

The Blueprint for Reducing Poverty in Franklin County is the culmination of a process that started almost a year and a half ago when the county first submitted a request for proposals for development of a county-wide poverty assessment and strategic plan.

Throughout this process, there has been a healthy dose of skepticism from many community members. Some balked at the price tag for the study. Some wondered why every steering committee member had a title like “CEO” or “Director” and none of them had “community member.”

In the end, though, the county emerged with a strong action plan. While 120 points seems audacious and unwieldy, the steering committee settled on three major priorities: creating living-wage jobs, increasing access to job training and growing the number of academically successful students living in poverty.

Even more exciting is the county’s additional commitment: to study an expanded child care subsidy program and a universal pre-K program, both of which could be massive levers for poverty reduction.

The county has also committed to action steps to carry out these goals. It will be creating an “Innovation Center” housed at the Columbus Partnership and funded by the county, with full-time staff overseen by a “Leadership Council” appointed by the Franklin County Commissioners.

The Innovation Center has the opportunity to be an incredible catalyst for poverty reduction in Franklin County, but it has a big task ahead of it to balance two competing ideals.

On the one hand, the Innovation Center is conceived as a policy analysis unit. It is being asked to “tackle ideas to treat the causes of poverty” and to “vet... big ideas,” and the center is funded by Franklin County Job and Family Services’ Strategic Transformation and Research Unit. This means it will be charged with analyzing the cost effectiveness of alternative poverty alleviation strategies.

On the other hand, the Innovation Center is charged with “bringing community partners together and implementing the blueprint,” and some of those community partners may disagree with the center’s policy analysis and may also have political reasons to support less effective poverty alleviation programs over more effective programs.

The center will have to be cautious to not be either a spineless community “yes man” or a detached ivory tower of research. This will mean staffing with a consideration for balancing community buy-in and analytic rigor. It will also mean tough conversations about priorities and use of community resources.

With all that in mind, Franklin County is off to a good start. Let’s hope this promising beginning leads to results for poverty reduction in Central Ohio.

Rob Moore is the principal for Scioto Analysis, a Columbus-based policy analysis firm.

Cost-Benefit Analysis for Foundations

On their face, foundation managers and government budgeting officers seem very different. One is focused on funding mission-based programs and the other is focused on paving roads and contracting for garbage pickup. One designs programs for the public while the other solicits input from the public for its programs. One serves the interest of private philanthropists, the other serves the interests of the public.

That being said, both private foundations and public budgeting offices face the same challenge: they must decide how to expend limited resources to fulfill a social mission. Whether it is reducing poverty, promoting economic growth, increasing educational attainment, improving public health, or any other social goal, both public-sector budgeters and private-sector foundations are interested in finding out the most effective way to make society better with limited resources.

One notable example of a foundation that has embraced this challenge of resource maximization is New York’s Robin Hood Foundation.

In 2017, Robin Hood spent $116 million on anti-poverty programs in New York City. Founded by hedge fund investors familiar and comfortable with quantitative methods, the foundation was inculcated from the start with a focus on what they call “relentless monetization,” a strategy that translates every benefit generated by their programs into dollar terms.

As Robin Hood economist and relentless monetization brainchild Michael Weinstein says in his book The Robin Hood Rules for Smart Giving,

A basic problem that plagues philanthropic decisions is that there is no natural yardstick by which to measure, and therefore compare, different philanthropic outcomes. Said another way, there is no one natural way to compare, for the purposes of fighting poverty, the value of providing job training to a would-be carpenter to the value of providing better middle school instruction.

So how does Robin Hood overcome this basic problem? They do it by using a tried and true method for comparing seeming incomparable options: cost-benefit analysis.

Robin Hood has created a playbook of 163 metrics, complete with equations designed to help suss out what the real impact of their programs are on their bottom line of reducing poverty in New York City. Some of these metrics are based off internal evaluation of their own programs, but most are pulled from comparable rigorous studies that are then applied to Robin Hood programs. These metrics are used to calculate the relative impact of different programs on their poverty-reduction mission.

The 168-page document looks a lot like the Washington State Institute for Public Policy’s benefit-cost database, full of equations and citations and addressing a considerable breadth of program options. While it does not include sensitivity analysis and focuses more narrowly on benefits accruing to program participants rather than society at large, Robin Hood’s metrics document does provide program officers with rigorous, relevant information about the relative cost-effectiveness of program options.

All this being said, Robin Hood’s scope is smaller than public sector players: its $116 million in annual spending is a drop in the bucket compared to New York City’s $139 billion in annual operating and capital spending. Even the largest foundation in the world, the Bill and Melinda Gates Foundation, only has a total annual budget of about half the size of the city of Phoenix’s total annual expenditures.

Nonetheless, foundations are getting more and more into the cost-benefit game. Savvy philanthropists and program officers who want to maximize their impact are turning to tried and true tools of applied economic analysis to guide budgeting decisions. And with more application of these tools, both program participants and results-oriented donors will be better off.

Moore Talks Food Insecurity on "Prognosis Ohio"

Scioto Analysis Principal Rob Moore joined Health Policy Professor and Prognosis Ohio Host Dan Skinner last month at the Columbus Arts Festival to talk about food insecurity and its impact on public health in the state of Ohio.

“We have a lot of evidence showing that if you are food insecure, your children are more likely to have birth defects, anemia, lower nutrient intakes, cognitive problems, risks of being hospitalized, asthma, behavioral problems, depression, and there’s also problems for adults when it comes to decreased nutrient intake, worse outcomes on health exams, poor sleep outcomes, etc,” Moore said.

Moore also talked about public policies that impact food insecurity in Ohio.

“Being a SNAP [food stamp] recipient reduces your chance of being food insecure by five to twenty percent,” said Moore, “so that means that about 65,000-260,000 Ohioans are pulled out of food insecurity by the SNAP program [every month].”

Moore also cited Scioto Analysis research that finds that SNAP Education financial literacy programs are cost-effective at reducing food insecurity, lifting one person out of food insecurity for every $700 spent on the program.

Prognosis Ohio is an Ohio health policy and politics report hosted by Dan Skinner.