How does wealth impact college access?

Earlier this week, I got my most recent copy of the Journal of Policy Analysis and Management in the mail. This journal is published by the Association for Public Policy Analysis and Management, and it is one of the most frequently cited journals by policymakers. 

In the most recent edition, there was one article that caught my eye. The study focused on college admissions and the difference between wealth and income. My colleague Rob Moore wrote recently about some ways we can address the wealth gap, but this paper was focused on one specific disparity created by wealth inequality.

The article is titled The racial wealth gap, financial aid, and college access, by Philip Levine from Wellesley College and Dubravaka Ritter from the Federal Reserve Bank of Philadelphia. 

These authors found that because wealth assets such as retirement savings and home equity are not included in financial aid calculations, the potential students who come from families with these assets get a significant subsidy compared to those that do not. Essentially, colleges and universities are underestimating the capacity that families with these uncounted assets have to pay for college. 

One argument against counting these assets in financial aid calculations is that because they are relatively illiquid, they don’t actually have much bearing on a family's ability to afford tuition. While this is true to some extent, research has shown that families with these assets, particularly home equity, are able to leverage their wealth to smooth their consumption under certain conditions. Essentially, they can spend more now because they know they’ll have money down the road.

Conversely, families that don’t have these assets don’t have the luxury to spend now because they don’t have money waiting for them later on. If someone doesn’t have a retirement savings account or home equity to liquidate later, then they have a lower ability to consume in the short run knowing that they’re going to need to have something once they're done working. 

The equity concern created by this system is that white families are more likely to have illiquid sources of wealth than other racial groups. This means that relative to other racial groups, white students receive a subsidy when they apply for financial aid. 

This is an interesting finding in and of itself, but the authors went further and quantified the impact of this subsidy. Specifically, they found that this particular feature of the financial aid system explains between 10 - 15 percent of the gap in educational advancement and student loan levels between racial groups. 

From a policy perspective, considering all wealth assets available to a family is a fairly simple way for colleges and universities to promote more equitable outcomes through their financial aid processes. It will take some careful consideration to properly adjust the formulas for these types of assets, but these authors have shown that not accounting for them at all is a mistake. 

The goal of financial aid in colleges and universities is to reduce the monetary barriers that prevent families with fewer resources from pursuing higher education. Right now, the formula isn’t benefiting the people who need aid the most, it’s benefiting the people who have generational wealth. If colleges and universities want to ensure their financial aid process is fair and equitable, they’ll have to rethink how they account for certain assets.

Analyzing Ohio’s two $15 minimum wage proposals

Last week, Senate Bill 254 was referred to the Ohio Senate’s Ways and Means Committee.

At a time when momentum is growing to increase Ohio’s minimum wage via ballot initiative, this new bill puts forth an alternative to increasing the minimum wage.

Senate Bill 254 has a few key differences from the proposed ballot initiative.

While the ballot initiative would raise the wage to $15 an hour by 2026, Senate Bill 254 would raise the wage to $15 by 2028. Both proposals index the new wage to inflation. 

If inflation rises at 2% per year (the federal reserve’s inflation target), that means the minimum wage will be about 60 cents lower in 2028 under Senate Bill 254 than the ballot initiative. This gap will be larger if inflation is higher.

Under the current minimum wage of $10.45 in 2024 indexed to inflation, the minimum wage in 2028 would be $11.31 if inflation increases at the reserve target over the next four years. So the Senate Bill is calling for a $3.69 minimum wage hike in 2028 compared to a $4.29 minimum wage hike from the ballot initiative. Effectively, the senate bill is calling for 86% of the minimum wage hike as the ballot initiative is.

Tipped workers would be covered by the ballot initiative, but not by the Senate bill. A long-term goal of minimum wage advocates has been to bring tipped minimum wages in line with overall minimum wages. Tipping culture has been linked to sexual harassment and wage theft and making workers less reliant on tips could help stem both of those issues in the restaurant industry.

A third difference between the two measures is legal. The ballot initiative is being proposed as a constitutional amendment. This shields it from modification by the legislature before or after it is passed. The Ohio Constitution can only be amended by a statewide vote, meaning that the ballot initiative would be the law of the land until another ballot initiative is passed to change it.

Senate Bill 254 could be modified in the Senate or in conference committee before the House. It also could be subject to line-item veto by the governor, changing the effect of the final bill before it becomes law.

A final difference between the two measures is that Senate Bill 254 includes a provision to expand the state Earned Income Tax Credit, a credit that provides tax relief to low-income families. The current tax credit is structured so that cash does not reach some of the most needy families. The change in Senate Bill 254 would expand eligibility for refundable credits, putting cash in the pockets of low-income workers.

When evaluating the two options, a clear tradeoff emerges between the two. Senate Bill 254 favors a less generous minimum wage increase in favor of a work subsidy targeted toward low-income workers. What this amounts to is a policy that will likely be less beneficial for workers at or near minimum wage with job security and much less beneficial for tipped workers with job security. On the other hand, it will benefit workers at risk of unemployment (especially tipped workers) and low-income workers not currently eligible for the earned income tax credit.

Both policies are likely to lead to more income for low-income people at the cost of some jobs compared to the status quo.

This may not matter in the scheme of politics. Voters are likely not paying enough attention to evaluate the bill as an alternative to the ballot initiative. That is, unless the General Assembly passed the bill and it was signed by the governor before a November vote. In that case, the ballot initiative would be about constitutional protection, date of phase-in, and tipped minimum wages.

That would be an interesting place to be in November.

This commentary first appeared in the Ohio Capital Journal.

Three steps for the United States to take happiness seriously

We remember 1776 for many reasons.

Certainly the event you associate this year with is the signing of the Declaration of Independence. Rebels against the crown of England put pen to paper to claim rights to self-government, life, liberty, and to pursue happiness.

Four months earlier, though, the most famous text in economic history was published: Adam Smith’s An Inquiry into the Nature and Causes of the Wealth of Nations. This book laid the groundwork for the entirety of the field of economics up until the present day. Smith’s treatments of division of labor, productivity, and market economics are the groundwork for how we understand economics today.

Less famous than these two texts was a third published that year, an anonymously-published pamphlet titled A Fragment on Government. Within this text, then legal theorist Jeremy Bentham put forth his theory of good government.

“It is the greatest happiness of the greatest number that is the measure of right and wrong…the obligation to minister to general happiness, [is] an obligation paramount to and inclusive of every other.”

Today, the ideals in the Declaration of Independence are echoed in the Constitution and debated by legal scholars daily within classrooms and every year to courts across the country. Questions of these ideals often become the province of the Supreme Court of the United States, one of the most powerful legal bodies in the history of the world.

Smith’s economic ideals reverberate through government, too, in technical workings through benefit-cost analysis required for federal decisionmaking. It also influences a powerful, non-partisan body of economists who have broad latitude to nudge the national economy, the Federal Reserve of the United States.

Yet, despite Thomas Jefferson’s words in the Declaration about the importance of the pursuit of happiness, Bentham’s statement has not taken root in the government of the United States of America.

No federal office specializes in subjective well-being. The United States Census Bureau does not collect data on subjective well-being the way the U.K.’s Office of National Statistics does. This means that policymakers do not have access to happiness data within the United States, even as the United States increasingly lags comparable countries in international happiness studies.

So what would a good happiness infrastructure for the United States look like? I propose three low-hanging fruit the United States could grasp to make happiness a serious part of U.S. policy.

1. Collect happiness data in the American Community Survey

The U.S. Census Bureau already has an annual survey that gives representative data down to geographies of about 100,000 people. Adding a cantril ladder question or, better, four questions mimicking those asked by the U.K., would give researchers a wealth of knowledge they could use to understand happiness trends across the country, across time, and across demographics.

2. Report annually on subjective well-being

The U.S. Census Bureau’s Annual Poverty Reports are some of the most useful reports put out by the U.S. federal government every year. They give regular updates on estimates of the extent of poverty in the United States and were key to explaining the impact of the 2021 Child Tax Credit expansion on poverty in the United States. Regular reports on happiness in the United States would be valuable information for the media, the public, and policymakers.

3. Appoint a happiness economist to the Council of Economic Advisers

The federal Council of Economic Advisers gives the President of the United States ongoing advice about the impacts of her policies on the U.S. economy. If the President is to understand the impacts of her policy decisions on the happiness of U.S. citizens, she needs an expert who can give her unbiased analysis of this topic. This can start with a member of the Council of Economic Advisers.

These are just three first steps. Ideally, the impact of public policy on happiness will be evaluated and happiness analysis can be incorporated into analysis in the Congressional Budget office and the Office of Information and Regulatory Affairs.

We have a long way to go to make Jefferson’s dream of a country centering the pursuit of happiness an ideal. But there are easy first steps to take if we wish.

What is the “u-shaped curve” in happiness economics?

This week, Scioto analysis released a report on subjective wellbeing in Ohio. One of our key findings was that age was positively correlated with happiness. In other words, older respondents of our survey were happier than younger respondents. 

This result is important in and of itself, but in the context of the broader wellbeing research it is fairly surprising. The reason is because it has generally been accepted for some time now that happiness follows a U-shaped curve over the lifespan. That is, young people are relatively happy, middle aged people are relatively not as happy, and elderly people are once again relatively happy. 

This is not the first time a survey has shown that the U-shaped curve might not exist. The 2022 US Happiness Report from Gross National Happiness USA found a similar trend in their survey, and the most recent World Happiness Report said “happiness among the young (aged 15-24) has fallen sharply in North America – to a point where the young are less happy than the old.” 

When I said earlier that a U-shaped happiness curve has generally been accepted, I should have clarified that I meant among economists. Researchers from other disciplines, particularly psychology, have argued against the U-shaped happiness curve. 

While not all economists accept the U-shaped curve and not all psychologists dismiss it, there are differences in the way acolytes of the two disciplines approach data analysis. In particular, one interesting question is whether or not to include control variables in an analysis. 

Economists include control variables because they want to understand the isolated impact that age has on happiness. In other words, if we took two people who were identical except for their age, what should we expect the difference in happiness to be between them.

This is not the same as just looking at the age of our survey respondents, because there could be some other reason that young respondents were less happy. For example, young people tend to earn less money than older people. 

Psychologists have questioned the use of these control variables, because it leads to a different understanding of the data. As an example, imagine if happiness was very strongly related to income, and age only had a very small impact. If this were true, then young people might report being less happy, but if we control for income then we could still see a U-shaped relationship between age and happiness.

This might lead us to say that young people are more happy than middle aged people, they just have lower incomes. Some would argue that this misses the most important point, which is that young people reported being less happy. 

These two ideas can both be true at the same time. While our survey finds that young people are less happy, it does not necessarily mean that those people will become happier as they age.

What is most interesting to me is the interaction between these two conclusions. I find our evidence very compelling, especially since it is backed up by other researchers. It is extremely concerning however if there still exists a U-shaped happiness curve with respect to age for this cohort. 

That would mean that at this moment, young people are experiencing extremely low levels of happiness, and we should expect them to be even less happy as they approach their forties. We might be staring down an epidemic of low wellbeing. 

As is the case with almost every policy relevant research topic, we benefit from taking an interdisciplinary approach. There is no objectively right or wrong way to interpret wellbeing data, there are just different questions to ask. 

Policymakers should be concerned both with how people are reporting their wellbeing today, and how factors like age will influence future wellbeing. By looking at the complete picture, we can find solutions to improve outcomes today and in the future. 

Subjective wellbeing research is an exceptionally important and woefully understudied topic, particularly at the local level. More detailed information is going to be needed to fully understand exactly how wellbeing changes with age.

How important is Ohio's safety net?

Last week, Scioto Analysis released our updated Ohio Poverty Measure, which is our project to estimate the extent of poverty in the state of Ohio. One of the key inputs in our model is the value of income people receive from public benefits. 

Because our model estimates benefits as a part of people’s incomes, there are two important differences compared to the Official Poverty Measure. First, our poverty lines are generally higher than the official poverty lines. This is because the official poverty line is primarily based on food costs, which are now a lower portion of people’s actual spending than the mix of goods used to determine poverty thresholds for the Ohio Poverty Measure. 

Second, the incomes we compare against the poverty lines are often higher than those used in official poverty calculations. This is because the benefits we account for are often larger than the additional costs (e.g. medical expenses) we account for. 

Because of these adjustments, our model better explains how many people in Ohio have sufficient resources to get by. It also gives us an idea of how much our social safety net is assisting in helping people get by. 

Because of the safety net, we estimate that Ohio’s poverty rate is 8.7%, as opposed to the 12.1% poverty rate reported by the Official Poverty Measure.*

This result is supported by data from Brookings, which found that after adjusting for cost of living Ohio has one of the most generous state safety nets. Ohio is right in the middle in terms of nominal generosity, but because the Midwest is more affordable on average those safety net dollars get stretched further. 

Looking at our data, if we remove the safety net completely in Ohio (state and federal), we find that the poverty rate jumps all the way from 8.7% to 20.5%. This means that in a world without social security, SNAP benefits, housing assistance, or any other public benefit, one out of every five people would not have enough to get by. 

Looking at the geographic breakdown of this impact, we see that while poverty rates are higher across the state, they are significantly higher in areas that already struggled with poverty. Urban city centers and rural Appalachian counties had poverty rates of about 15% ~ 20% when including public benefits. Those can jump all the way up to over 40% if we take that money away. 

In the wealthiest parts of the state, poverty rates went from about 5% to over 10% if public benefits were removed from income. Although these people probably relied much less on benefits like SNAP or housing assistance, many of them still benefited from Social Security, which is by far the largest public benefit program nationally. 

Looking at some of the largest counties in Ohio, we can visually see that poverty rates almost doubled after removing benefits from the calculations. The biggest difference is in Appalachian Athens, Gallia, and Meigs Counties, which had higher poverty rates with benefits than the more urban counties. 

This experiment shows just how important Ohio’s safety net is. Whether it be because of disability, old age, local job market conditions, or need to spend time caring for family members, many people don’t draw enough income from wages alone to support their families.Because of these programs like social security, SNAP, and housing assistance, many people are nonetheless able to put a roof over their heads and food on the table. Public benefits are far more than just a political talking point, they are one of the most important tools the public sector has for making sure people are able to get by.

New survey finds young Ohioans are less happy than older Ohioans

This morning, Scioto Analysis released a report on subjective wellbeing in Ohio. Using a survey of over 600 people from across the state, we explore how people assess the quality of their own lives through questions such as “How happy were you yesterday?”

We find that among our survey respondents, there is a positive correlation between happiness and age, meaning that younger respondents were less happy than older respondents. This challenges the hypothesis that happiness follows a U-shaped curve over the lifespan, where people are the least happy during middle age.

“For years, self-reported happiness across the world followed a ‘U-shaped curve,’ with younger and older adults happier than middle-age adults,” said Scioto Analysis Principal Rob Moore, “this analysis is the newest in a series of studies finding lower levels of happiness for young adults in North America.”

Previous studies that found lower levels of happiness among young people were the 2022 US Happiness Report and the 2024 World Happiness Report.

When asked about what things make people happy, the majority of respondents reported that relationships and social connections were key contributors to happiness. Other important factors were health, and creativity/hobbies. 

Respondents that reported fairly high levels of happiness overwhelmingly indicated that relationships and social connections were drivers of happiness. This could suggest that among younger people, feelings of loneliness are contributing to the disappearance of the U-shaped happiness curve.

Scioto Analysis partnered with Ohio State University’s Environment, Economy, Development, and Sustainability program to conduct this study. Data collection and analysis was conducted by students in the program.

What are “baby bonds?”

Last month, Scioto Analysis released its most recent calculation of the Ohio Poverty Measure. This measure calculates the total income of Ohioans, including benefits and after taxes and unavoidable expenses, and compares it to basic consumption needs.

This is how we usually measure poverty: through a lens of income. A problem that some economists have with this approach is that they believe that escape from intergenerational poverty also has to do with accumulation of wealth than income.

There is reason to believe this. While income disparities in the United States have been increasing, wealth disparities are even more pronounced.

A 2020 Pew Research Center analysis found that while the average high-income household makes about seven times as much as the average low-income household, the average high-wealth household has assets 75 times as large as a low-wealth household. This means the U.S. wealth gap is seven to eight times as large as the U.S. income gap.

Wealth does a few things for families. First, it provides a family safety net for working people. That “emergency fund” you keep in your bank account in case you experience a bout of unemployment? That’s wealth. Wealth helps working people weather the ups and downs of employment endemic to a market economy.

Second, wealth can be a source of income. Large amount of wealth invested can yield dividends that can provide income to a household. It also can be drawn on during retirement as an ongoing source of income.

Wealth also provides the safety that allows people to take risks. Starting a business or investing in a job that is unlikely to have large returns in the short-term is a lot easier to do if you have wealth to fall back on.

But how do we promote wealth, especially for families that are already living paycheck to paycheck and can’t afford to set money aside with pressing needs to pay for now?

Last month, Economist Darrick Hamilton published a commentary in TIME Magazine addressing this topic. His answer is a policy called “baby bonds.”

A baby bond works by paying someone a lump sum once they reach a certain age. This often comes from an investment made at birth that grows in value over time. These can be restricted in use or not.

A great example is Connecticut’s program, which automatically invests $3,200 for any child that qualifies for Medicaid at birth. Between age 18 and 30, the recipient can claim the fund and spend it on buying a home in Connecticut, starting or investing in a Connecticut business, paying for higher education or job training, or saving for retirement. The state of Connecticut estimates a typical bond will have a value of $11,000-$24,000 by the time it is claimed.

By targeting these funds to children who are Medicaid recipients, the Connecticut program focuses on low-income children. But a program like this could be targeted in a number of different ways: by being claimed by low-income households, targeted toward low-income zip codes, or even universal eligibility. Medicaid has the simplicity, though, of being a high-uptake program for low-income families that the state has good data on.

Income interventions are important for fighting poverty today, but wealth-based interventions like baby bonds could be a valuable tool for disrupting intergenerational poverty. State and local lawmakers interested in helping fight poverty in the long-term should consider policies like baby bonds if they are looking for creative ways to fight wealth inequality.

How does a policy analyst impute missing public benefits data?

Last week, Scioto Analysis released our updated Ohio Poverty Measure, a report that we’ve been working on since November. In this measure, we use publicly available data to understand the state of poverty in Ohio. Our methods are based on a wide range of other state and city poverty reports, all of which are heavily influenced by the Census Bureau’s Supplemental Poverty Measure.

To calculate the Ohio Poverty Measure, we primarily used data from the American Community Survey. The American Community Survey is one of the most useful datasets because it has a higher sample size than the Current Population Survey, which is used to calculate the Supplemental Poverty Measure. This makes it the best way to estimate what poverty looks like at smaller geographic resolutions. Though the American Community Survey has such a wide reach, it does have a few important drawbacks.

The most important limitation of the American Community Survey is that it doesn’t ask as many questions as other surveys do. It succeeds in providing detailed information about things like employment and income, but it doesn’t ask about things like medical expenses which we need to know for our poverty report. 

For this information, we turn to information in the Current Population Survey. The Current Population Survey is similar to the American Community Survey, but it asks a smaller number of people a larger number of questions. Here, the tradeoff is sample size for more detailed responses. 

While we could have used the Current Population Survey as the base data for our analysis like the Supplemental Poverty Measure, we’d be relying on a much smaller sample to make claims about all of Ohio. Since we performed our analysis at the Public Use Microdata Area level (the smallest identifiable geographic area in these datasets), this would subject our results to sampling error. 

So how do we use data from the Current Population Survey to fill in the missing data from the American Community Survey? Formally, this process is called “data imputation,” and there is a great deal of statistical research on the topic. 

There are many ways to conduct data imputation. One simple example is simply assigning every person the average value of a missing variable. In our context, this would be bad since something like medical expenses will be zero for many people and quite large for a small portion of people, though it does have the desirable characteristic that the imputed data will have the same mean as the original data. 

For the Ohio Poverty Measure, we follow the same steps for imputing missing data that other poverty reports before us have. We use a two-step modeling process to first determine who is likely to have non-zero missing values, and then isolating that group we try to determine what the value is. 

To do this, we build two regression models from the Current Population Survey data. The first is a binary outcome regression that predicts the probability of an individual response having a non-zero value. The second looks only at those responses that have non-zero values and predicts the size of the missing variable. 

We then take these two regression models and use the American Community Survey data to get predicted values for the probability of a non-zero value. We then estimate the total size of the missing variable. 

Then, we rank the American Community Respondents by their predicted probability of having a non-zero missing value. We want to make sure that the same percentage of people in the American Community Survey have non-zero values as in the Current Population Survey, so we only count the most likely people until the proportion in the American Community Survey matches that in the Current Population Survey.

Making predictions is one of the most important parts of policy analysis. We often think that the predictions are the outputs of our work, not part of the input. However, with some clever statistical thinking, we can give ourselves access to really amazing data like the American Community Survey, even if it doesn’t have exactly all the information we need. As long as we can find a good way to impute it, we can take advantage of everything else it has to offer.

Is it “cost-benefit analysis” or “benefit-cost analysis?”

At Scioto Analysis, we are doing a multi-year project where we are demonstrating how a good cost-benefit analysis is conducted. 

As part of this series, we have conducted cost-benefit analyses on the state Earned Income Tax Credit, school closings for COVID-19, AmeriCorps, urban canopy programs, water quality programs, an Ohio child tax credit, legalization of medical marijuana, and daylight saving time. We are currently conducting a cost-benefit analysis of a minimum wage increase for Ohio.

We also are members of the Society for Benefit-Cost Analysis, the international association of analysts across the public, private, and academic sectors working to improve the theory and practice of benefit-cost analysis and support evidence-based policy decisions.

That’s not a typo: we conduct “cost-benefit analysis” and are members of the Society for “Benefit-Cost Analysis.”
So what is the difference between “cost-benefit analysis” and “benefit-cost analysis?”

Nothing.

These two phrases are used interchangeably in the world of cost-benefit analysis and are often used by different people, but refer to the same phenomenon.

The main differences between the two phrases are where they are used. “Benefit-cost analysis” is common in academia and in federal regulatory decision making. “Cost-benefit analysis” is more common outside of these sectors in the United States and in non-U.S. contexts.

But why do two phrases refer to the same practice? Below are some of the explanations I have heard over the years. I will be clear: I can’t vouch for any of these. I don’t know how true any one of these are, but they have nonetheless been offered to me as explanations for why people say “benefit-cost analysis.”

To represent “professionalized” practice

One explanation relayed to me by my colleague Michael Hartnett from the most recent Society for Benefit-Cost Analysis conference was that cost-benefit analysis had a push for mainstream acceptance in the 1970s, before Ronald Reagan required cost-benefit analysis of all federal regulations. Economists were trying to standardize the practice and promote it as a systematic form of applied economic analysis. In order to differentiate the practice from more sophisticated approaches to evaluating policy, “benefit-cost analysis” was put forth as a way to refer to the systematic practice.

To emphasize the importance of benefits

“Cost-benefit analysis” seems to focus on costs before benefits because…it comes first in the phrase. By placing the word “benefit” first, “benefit-cost analysis” assuages the fear of people who think conducting this analysis is overly focused on costs policy to the detriment of its benefits.

This explanation sounds a little silly, but it does fit with some worries people have. The loudest voices against cost-benefit analysis are often advocates who are afraid costs of policies they champion will outweigh their benefits. This theory is that by placing benefits first, those people will have their fears assuaged.

To reflect the formula of “benefits minus cost”

The central formula of cost-benefit analysis is calculation of net present value, or 

Present Benefits - Present Costs = Net Present Value

By placing “benefit” first in the phrase, we capture that central formula in the technique. This explanation is similar to the previous one: it is about trying to get people to understand how the system works. Seems a little weak for the confusion created, though.

Linguistic cadence

This is an especially interesting one: that the phrase “cost-benefit analysis” rolls off the tongue better than “benefit-cost analysis,” so “cost-benefit analysis” will persist no matter how much people try to get others to use the latter. The argument has to do with word emphasis within the phrase. I don’t know how true this is, but it is interesting.
Overall, the battle between “benefit-cost analysis” and “cost-benefit analysis” seems a lot like the battle between the phrases “this data” and “these data,” classic linguistic squabbling, sometimes between elites and mundane use, rarely important. While I will not be soon to give up my membership at the Society for Benefit-Cost Analysis, we’ll probably continue using “cost-benefit analysis.” Why? Because that’s what policymakers tend to use, and we’d rather have them understanding the analysis than reading articles like this.

Ohio Poverty Measure finds over 260,000 Ohioans pulled out of poverty by Social Security

Today, Scioto Analysis released its updated Ohio Poverty Measure, an indicator specifically tailored to estimate the extent of poverty in the state of Ohio. Using this measure, we find that in 2021, 8.7% of Ohioans lived in poverty. This is lower than the 12.1% poverty rate according to the Official Poverty Measure and higher than the 8.1% poverty rate according to the Supplemental Poverty Measure, the two main poverty measures calculated by the United States Census Bureau.

Among public benefit programs, we estimate social security has the largest impact of any public benefit program in Ohio, lifting over 260,000 Ohioans out of poverty in 2021. The measure also finds SNAP benefits, formerly known as “food stamps,” had a substantial impact on poverty, reducing the statewide poverty rate by nearly two percentage points.

The Ohio Poverty Measure is the most accurate measure of poverty in the state, using methodology inspired by the California Poverty Measure, New York City Poverty Measure, Oregon Poverty Measure, and Wisconsin Poverty Measure. The Ohio Poverty Measure was first calculated by Scioto Analysis in 2021, using data from 2018. This report constitutes the first comprehensive update of that data, giving estimates of poverty from 2021.

The Ohio Poverty Measure estimates the impacts of government assistance, the tax system, and expenses based on geographic cost-of-living differences. Including these adjustments makes the Ohio Poverty Measure more precise than both the Official Poverty Measure and the Supplemental Poverty Measure.

According to the Ohio Poverty Measure, Black Ohioans are 75% more likely than White Ohioans to be experiencing poverty, with 14% of Black Ohioans experiencing poverty compared to only 8% of White Ohioans. 

Additionally, we also find stark geographic disparities in poverty rates. Ohio residents living in urban core geographic areas and rural Appalachian communities experience poverty at much higher rates than those across the state as a whole. Ohio residents living in suburbs surrounding Ohio’s largest cities experience poverty at much lower rates than residents across the state as a whole.