Working Ohioans will lose health insurance under Medicaid work requirements

If you know anyone who works in the service industry, you should be very familiar with the problem of hour volatility. When work hours aren’t set, worker schedules can vary greatly from week to week and from month to month. This can make a steady stream of income difficult to achieve for service workers. It can also affect eligibility for public benefits.

The Ohio Department of Medicaid is currently working with the federal government to implement work requirements for Ohio’s “Medicaid expansion” population–the 760,000 Ohio residents who receive health insurance through the Kasich Administration-era expansion of Medicaid. These work requirements would apply to households at 138% of the federal poverty level and below.

Low-income households tend to be headed by people who work in the service industry. My colleague Michael Hartnett estimates that cooks and waiters are the second- and fifth-most common jobs among people in the bottom 20% of income in Ohio.

A new analysis by Brookings Institution researchers looks at how the volatility of hours for service workers will impact eligibility for benefits like Medicaid and SNAP.

One of the things they look at is the mental model that undergirds the current work requirement system. In 1976, only 26% of low-income employees worked in the service sector. By 2024, that number had risen to 38%. This means that 50 years ago, the contours of an unsteady sector had less of an impact on month-to-month hours than it does today.

These researchers used data from the Survey of Income and Program Participation to estimate that 64% of service workers worked less than 80 hours in at least one month in 2022. A third (34%) of workers who work an average of 80 hours a month had at least one month that year that they worked less than 80 hours. That means that a monthly work requirement of 80 hours would have disqualified a third of service workers at some point during 2022 from benefits like Medicaid or SNAP.

The researchers also find these volatile work hours are largely outside of the control of the workers. According to their analysis, three-quarters of service workers with irregular schedules say their schedules are at the request of their employers, not their own. This is also a high rate among non-service workers, where over 3 in 5 low-income workers with irregular schedules are conforming to employer requirements.

So what does this mean? It means tens of thousands of low-income workers in Ohio could lose their health insurance because of work hour volatility out of their control.

The labor market has changed a lot over the past fifty years, especially for low-income workers.

This has led to less certainty about hours, which makes thresholds like monthly hours not as effective for gauging whether people are participating in the labor force. There are a lot of reasons to be worried about work requirements. The fact that working people will lose health insurance because lack of control over work hours is just another one to add to the list.

This commentary first appeared in the Ohio Capital Journal.

Could higher minimum wages increase employment?

Last month, we released a study looking at how raising the minimum wage in Oklahoma would impact housing security in the state. Overall, we found that a minimum wage of $15 per hour would decrease housing cost burden across the state. 

Depending on who you are talking to, this result is either completely obvious or seemingly impossible. That’s because when minimum wages go up, there are two counterbalancing effects. Higher wages means bigger paychecks, but higher wages means higher costs for employers and jobs being cut as a result.

Much of the impact of higher minimum wages centers on this tradeoff. It’s easy to determine how much wages are going to increase, but understanding how many jobs are lost when minimum wages go up is more difficult. 

A new working paper published last month tries to help answer this question. These researchers explore some of the city-level dynamics that influence how changes to the minimum wage impact the local labor market. 

One of the most important factors they identify is the size of the city. It is well documented that workers in large cities earn more than their counterparts with the same education and experience elsewhere. 

As a result of this, minimum wages are less likely to be binding in large cities. If workers are already making $20 per hour, then raising the minimum wage to $15 does not affect them at all. In big cities, this is more common. 

The paper also looks at how specific labor market conditions in each city factor into this equation. Specifically, they explore how many employers there are and how easily workers can move between jobs. 

The situation where there is a single employer is called monopsony. This is the opposite of monopoly, where there is only one supplier. Under a monopsony, there is only one purchaser (in this case an employer purchasing labor), and because they are the sole buyer they have much more influence over prices in the market. 

In a lot of cases, the number of employers is tied to how easily people can move between jobs, but not always. Monopsony conditions are much better described by how difficult it is for people to move between jobs rather than just the pure number of employers in a region. Things like non-compete agreements can lead to monopsonistic conditions even in markets with many employers. 

If a labor market is very monopsonistic, employers could have the power to keep wages below what they would be under more competitive conditions. If this is happening, then raising the minimum wage could actually lead to increased employment. This is because potential employees would have the option to earn wages closer to their fair market value and would choose to enter the labor market as a result. 

Understanding the effects of raising the minimum wage requires understanding how  city size and labor market dynamics influence outcomes. Large cities may see less and cities with more monopsonistic labor markets are likely to have better employment, while smaller and more rural areas that still have competitive labor markets are more likely to have worse outcomes. 

This paper highlights the importance of local policy analysis. In lots of cases, we as analysts use state level averages in order to measure outcomes. While this is not inherently bad, and is often required given data limitations, it is important to recognize that we can miss some important details along the way. More policy analysis that specifically focuses on these narrow contexts could be extremely important for improving outcomes in cities across the country.

Ohio economists skeptical about abolishing property tax

On May 9th, the Ohio Attorney General approved ballot language for a signature drive to end property taxation in Ohio. Advocates for the petition argue sharp increases in property taxes make payments too difficult for taxpayers to predict, plan for, and pay.

In a survey released this morning by Scioto Analysis, 10 out of 16 economists surveyed disagreed that replacing property taxes in Ohio with higher sales and income tax rates will reduce the volatility of tax payments for Ohio households. 

Kevin Egan from the University of Toledo disagreed, writing “Sales and income fluctuate with the business cycle. Having property taxes, especially taxes directly on the value of the land (not what is built on it) is one of the first-best tax options due to the amount of land to be taxed does not change.”

Conversely, Charles Kroncke from Mount St. Joseph University agreed with this statement, but offered the caveat “This will mainly benefit wealthy older people who don't work. This will not help working class younger people who spend their income on consumer goods.”  

Property taxes are commonly used to fund public schools, and when asked about the impact on school funding 12 of 16 economists agreed that removing property taxes will decrease overall per pupil spending in Ohio’s public schools.

David Brasington from the University of Cincinnati was uncertain about the impact on school spending, writing “Ohio already has the option of school district income taxes; the effect on spending depends on how much voters decide to shift from property to income taxes.”

The Ohio Economic Experts Panel is a panel of over 30 Ohio Economists from over 30 Ohio higher educational institutions conducted by Scioto Analysis. The goal of the Ohio Economic Experts Panel is to promote better policy outcomes by providing policymakers, policy influencers, and the public with the informed opinions of Ohio’s leading economists. Individual responses to all surveys can be found here.

What does it mean to be “middle class?”

“Middle-class” is an ubiquitous term in U.S. public policy debates. A strong middle class is seen as emblematic of a strong country. Middle-class is the American dream: a steady, dependable job, a car, a single-family home.

Stories of American decline are wrapped up with tales of the erosion of the middle class. When commentators opine about the current social situation in America, they often refer to a story about the shrinking middle class of the country, using phrases like “hollowing out” to describe its fate.

But what is “middle-class?” As a policy analyst, if I am looking to solve a problem, I first need to define the problem. Having an understanding of what defines living in the “middle class” can help us understand how true these stories people tell are and, if they are true, what we can do about them.

A common historical tale told about the middle class is that of the middle class as the “merchant class.” Feudal society was defined with a strict contrast between landowning nobles and the sharecropping laboring class. Merchants were in between: wealthy because of their engagement with markets, but lacking the stature and social position of land-owning nobles.

The term “middle class” originated in the 18th century, curiously focused not on current connotations of wealth and income, but on social status. In Britain, this phrase was applied to people who were somewhere between laborers and aristocrats, with the wealth of nobles but without the titles. The founding of the United States, a nation of landowners but without nobility, made this a natural identification for many of these new citizens.

The United States’s geographically bifurcated economic system in the 19th century led to a fraught definition of “middle class” alongside it. The term came into widespread use in the North by the middle of the 19th century, especially in cities like Boston, New York, Philadelphia, Cincinnati, and Chicago. “Middle class” spoke to a number of dimensions: it was largely a protestant class making up professions such as shopkeepers, lawyers, ministers, small manufacturers, and white-collar workers. The term was utilized by members of these professions to distinguish that class of workers from poor, Catholic (especially Irish) immigrants and the “idle” wealthy elite.

The South was a different picture. The binary identification of landowner elite and slave left little room for a “middle class” and the term was not in widespread use.

The 20th century saw the growth of white-collar jobs, then the postwar period saw what many people would call the “Golden Age” of the middle class. The GI Bill made postsecondary education and homeownership attainable for the masses and a United States largely untouched by the ravages of war kicked into gear to rebuild the world economy, giving a steady supply of high-quality jobs to a range of Americans. It wasn’t until the 1970s that deindustrialization started to create what many today call the “hollowing out” of the middle class.

So we’ve had different definitions of “middle class” over time. From “merchant” to “not noble or sharecropper” to “hardworking moral small business owner” to “not immigrant or wealthy” to “white-collar worker” to “single-family home and a car,” it has meant many things over the years. But what does it mean to us today?

The most common way for researchers to define “middle class” today is to appeal to some sort of objective measure. The Brookings Institution has done some fantastic work on the range of different definitions used to define the middle class in the United States. In a 2018 study, Richard V. Reeves, Katherine Guyot, and Eleanor Krause define “middle class” using three definitions: cash, credentials, or culture.

“Cash” is a definition of the middle class that revolves around how much income a family has in a given year. Most definitions of middle class that revolve around cash place middle-class families above the poverty line but somewhere below the $150,000-250,000 mark. There also tends to be a bit of a gap between poverty line and the lower end of most cash definitions of middle-class, suggesting a “low-income” category that is above the federal poverty line but that falls short of most definitions of middle-class. A 2018 review of 12 definitions of the middle class by the same researchers found researchers set the low end of “middle class” at somewhere between $13,000 and $55,000 and the high end of “middle class” at somewhere between $69,000 and $230,000.

The second definition they talk about are “credentials.” This category generally encompasses two criteria: occupation and education. My colleague Michael Hartnett has done some work investigating these trends in Ohio, where he has found nurses, teachers, truck drivers, and administrative assistants dominate the middle income bands. Definitions of “middle class” around education credentials revolve around postsecondary education. People with some college, associates degrees, and bachelors degrees but without master’s, professional, or doctoral degrees tend to fall into the category of what we consider “middle class” in the United States today.

Finally, there is culture. This gets into the fuzzy space of definition, but may be closest to what we think of when we’re talking about the single-family home, car-owning, steady-job middle-class. We think of middle-class families as hard-working, trying to make their children’s lives better than their own, suburban, into sports, public-school-attending, politically-moderate, television-watching, “normal” Americans.

Which leads us to a particularly interesting definition of “middle class”: self-identification. One way to find out if someone is middle class is to simply ask them. Now this does not answer our question of whether other people would consider them middle class, but it is an important datapoint: how do people judge their own lives and lifestyle?

In general, I find the Pew Research Center definition of “middle-income households” to be the most useful definition of “middle class.” They define “middle-income households” as those with two-thirds to double that of the U.S. median household income after incomes have been adjusted for household size. With the wide range of data available on income, this allows us to analyze how issues impact middle-class households.

That being said, I think subjective measures deserve more attention. Ultimately, our goal is not just to get people into an income band, but also to “feel” like they are a part of the middle class, with all of its material and social benefits. By using subjective measures of middle class, just asking people if they are in the middle class, we can get a better idea of what it means to be middle class and how we can help more people achieve it.

Welcoming Policy Analyst Emily Cantrell

Hello! My name is Emily Cantrell, and I’m thrilled to join Scioto Analysis as a policy analyst. I am passionate about research on public policy that addresses poverty and inequality, with a focus on leveraging data science and computational methods. Alongside my work at Scioto Analysis, I am also completing a PhD in Sociology and Social Policy at Princeton University. 

I was born and raised in Ohio, and my interest in social policy has deep personal roots. My brother has developmental disabilities, and growing up I experienced firsthand how public policies designed to support people with disabilities can shape a family’s daily life in profound ways. While programs and policies like Medicaid, the Individuals with Disabilities Education Act (IDEA), and the Americans with Disabilities Act (ADA) have been incredibly important for my brother’s care and education, shortcomings in such policies can also create barriers for those who rely on them. These experiences fueled my commitment to policy work that is not only data-driven, but also grounded in the realities of those affected.

As an undergraduate at Denison University, I designed my own major in Human Development and Social Policy, creating an interdisciplinary course of study to explore how economic inequality shapes children’s lives. Two courses that inspired me were an economics course on income inequality with Dr. Andrea Ziegert and a psychology seminar on child development and public policy with Dr. Gina Dow. Outside the classroom, I spent a summer working at a Cleveland-based non-profit that helps families navigate public benefits and community resources. There, I saw how gaps in child care access created barriers to parents' employment and economic stability. These experiences culminated in my senior thesis on the relationship between early childhood poverty and socioeconomic outcomes. For the analytical portion of my thesis, I assessed the relationship between early childhood care and education programs and kindergarten readiness using local data from Newark, Ohio. I loved the research process so much that I decided to pursue it as a career. 

To build my skills in policy and program evaluation and gain experience in full-time research, I worked for two years at Child Trends, a child and family policy research organization based near D.C. In that role, I helped evaluate quality assurance programs for child care and contributed to a variety of other projects related to early childhood. This work affirmed my passion for social policy research, and led me to pursue a PhD at Princeton, where I have developed expertise in data science and computational methods. In my dissertation, I investigate the capabilities and limitations of machine learning models that predict life outcomes, such as "predictive risk models" used in child protective services. Drawing on U.S. and Dutch survey data as well as Dutch administrative registry data, I examine the (un)predictability of hundreds of life outcomes, evaluate the effectiveness of different data sources and modeling techniques for predictive performance, and explore what these findings reveal about limits to the predictability of the human life course.   

Midway through my PhD, I worked as the committee assistant to the Health and Human Services Committee in the New Mexico House of Representatives during their 2022 legislative session. That experience deepened my interest in state-level policymaking and ultimately led me to connect with Rob Moore and the Scioto Analysis team. I am excited to apply my expertise in data science and social policy to support evidence-based policymaking in Ohio and beyond.

Are we wrong about Redlining?

In poverty and inequality circles, redlining is often one of the first things mentioned when talking about why racially-correlated urban inequality exists today. When I previously wrote a blog post about mapping poverty in my hometown of Saint Paul, I mentioned the impact of redlining on current patterns of inequality in the city. 

Redlining as most people know it refers to a series of maps produced by the Home Owners’ Loan Corporation in the 1930’s, where neighborhoods were graded by their social, economic, and physical characteristics. The neighborhoods marked in red on these maps line up with neighborhoods that today have low incomes. 

However, new research on these maps shows that this commonly-held belief about these maps might be wrong. 

Before digging in any further, I want to make it clear that this research does not suggest that there isn’t historical discrimination that led to the spatial trends we see in urban areas. Instead, this research tries to show that the Home Owners’ Loan Corporation maps are not the main culprit. 

What are the Home Owners’ Loan Corporation maps?

During the Great Depression, the Home Owners’ Loan Corporation was established as part of the New Deal in order to help prevent a total housing market collapse. Their goal was to purchase failing mortgages from banks and restructure them so people could stay in their homes.

The Residential Security Maps and their color-coded neighborhoods were created in an attempt to measure the riskiness of neighborhoods instead of individual borrowers. To determine the riskiness of a neighborhood, the evaluators looked at features like housing quality, access to public transportation, economic characteristics of the people who lived there, and the race of the people who lived there.

It is true that almost all majority black neighborhoods in the cities evaluated by the Home Owners’ Loan Corporation were given the lowest grade, but it is important to remember that in the 1930s, discriminatory housing practices largely kept people of color out of urban areas. The majority of the lowest-rated neighborhoods were almost exclusively white immigrant neighborhoods with poor housing conditions. 

In the predominantly black neighborhoods during those years, housing conditions were even worse. This means that even if those neighborhoods were not predominantly black, they would have still received the lowest rating because of their housing conditions. 

In fact, during the time that the Home Owners’ Loan Corporation was purchasing mortgages, they did a fairly admirable job of providing assistance indiscriminately. This is not to say that they were actively supporting disadvantaged communities, but they purchased a proportionate amount of loans from the lowest rated neighborhoods.

This begs the question though, if these maps were not made with discriminatory housing practices in mind, what were they for?

Unfortunately, there isn’t a satisfying answer to this question. By the time these maps were completed, the Home Owners’ Loan Corporation was largely out of the mortgage business. The maps weren’t shared with private lenders, and although they were shared with the Federal Housing Administration (which did participate in discriminatory housing practices), that agency had its own set of maps, and the available historical evidence suggests that they didn’t use the Home Owners’ Loan Corporation maps. 

Why are these maps correlated with modern disadvantaged communities?

The history lesson above is fascinating, but it doesn’t explain why the Home Owners’ Loan Corporation maps are strongly correlated with modern day low-income neighborhoods. If they weren’t used to perpetuate discrimination, how did we get here?

The answer lies in understanding what the maps were actually measuring: housing quality. 

Remember, when the maps were made, predominantly black neighborhoods were rare in America’s cities. Racist housing policies largely kept people of color out of the cities altogether. 

This changed after World War II when we saw one of the most dramatic realignments of people in the United States. The simultaneous impacts of the Second Great Migration and White Flight completely changed the demographics of American urban environments. 

The discriminatory housing practices that kept people of color out of the cities vanished and were replaced with new discriminatory housing practices that kept them out of the newly built suburbs. White families from the cities started leaving en masse, leaving vacancies for black households. 

Before this migration, black urban neighborhoods had a lot of economic diversity. It largely didn’t matter how much money black households had, the racist housing policies made sure they all lived in the same neighborhoods. 

During the migration, black households had a new opportunity to choose where they lived. Black urban neighborhoods became economically separate, and wealthy black households had the opportunity to move into the higher-rated neighborhoods.

Another key factor was that in the lowest-rated neighborhoods, far more families left for the suburbs than moved in to replace them. This led to housing abandonment and disinvestment in these neighborhoods that helped perpetuate the economic discrimination. 

So, the reason these maps line up with racial and economic disparities today is because during this massive upheaval of American cities, poor black families moved into neighborhoods with poor housing conditions that were vacated by white families who moved elsewhere. The housing markets in these neighborhoods largely collapsed due to high vacancy rates caused by fewer families moving in than previously lived there, which coupled with racist economic development policies led to disinvestments in these neighborhoods.

What does this mean for policy today?

If you believe everything I’ve said up to this point, you might still wonder why this distinction matters. At the end of the day, the Home Owners’ Loan Corporation maps line up with disadvantaged neighborhoods today, and isn’t that really what matters?

While I agree that addressing today’s problems is the most important issue, understanding how we got here can help us come up with permanent solutions. 

One takeaway I get from this new understanding is that the low-income neighborhoods in our cities today were not the result of black people being forced into those places by federal agencies. They were forced into those neighborhoods because of their wallets. 

We know this because wealthy black households moved into higher rated neighborhoods during the Second Great Migration. The households that moved into these lower rated neighborhoods did so because that was what they could afford to do. 

Another reason it might be good to not allow these maps to influence policy today is because while the low rated neighborhoods do largely line up with low-income neighborhoods today, it’s not a perfect match. If policy were designed to only focus on the lowest rated neighborhoods from the 1930s, we’d be ignoring a lot of disadvantaged communities that don’t fall exactly in those neat lines.

Finally, focusing too much on these maps makes it so we do not accurately acknowledge the players that were responsible for racist housing policies in that era. There were bad actors who made it extremely difficult for black families to find adequate housing, and we should accurately tell that part of our history. 

I would encourage anyone who has read this far to go and read the full article that helped me understand this. There is much more to this story and Alan Mallach does an excellent job communicating it. It’s always an unusual experience to have an idea that you have accepted as a fact be challenged this way. It took me a while to come around to the idea that I had been wrong about what these maps meant.

Why do we keep subsidizing big homes for wealthy families?

This month, researchers Michael P. Keane of Johns Hopkins University and Xiangling Liu of Hunan University released a paper in the National Bureau of Economic Research’s working paper series on reforming taxation of housing.

This working paper revolves around a problem in housing economics called imputed rent.

Say you rent your home. That means you pay a landlord for the ability to live in your home. Since you are paying someone, they receive that payment as income. Like any other income, this income is subject to income taxes.

What if you live in your home? Well, you pay your mortgage, the same way a landlord does. But you don’t pay a landlord for rent. You “pay” yourself to live in the home that you own. You get the same good (housing) as any renter gets, but since you own the home, you pay yourself from it. But here’s the catch–that payment is tax free. Because no money changes hands, you are exempt from income taxes.

What this amounts to is an implicit subsidy to own a home rather than rent. This theoretically pushes people who would be renting to own due to the subsidy, in particular encouraging upper-income households to buy larger houses than they would otherwise. It also ties up capital in homeownership. Rather than investing in companies, individuals spend their incomes on homes, artificially inflating their value and decreasing the value of businesses.

These researchers investigated what would happen if an alternative tax system existed. Under this system, all homeowners would be treated as landlords and people who lived in their own homes would be required to report the imputed rent they paid and received by living in their own home as income. Just like landlords, homeowners would be allowed to deduct property taxes, maintenance, depreciation, and mortgage interest. After all this was deducted, however, they would have to pay taxes on the value of rent they are paying themselves.

This would mean a lot less money in the pockets of homeowners and a lot more money in the pockets of the government, right? Not necessarily. Keane and Liu model what would happen if this extra government revenue was used to finance an across-the-board income tax cut for all taxpayers. This tax cut would offset part of the new tax for homeowners.

…or rather, all of the new tax for homeowners. Keane and Liu simulate this policy change using data from the Panel Study of Income Dynamics. They estimate that this new tax would finance a 9.15% income tax cut across the board. This means every 11th dollar raised by income taxes would go back to households. It also leads to a rare policy change that would lead to a Pareto improvement, meaning no one will be left worse off by the policy and some (many in this case) will be left better off. Under their simulation, every homeowner paying taxes on their imputed rent will have their new taxes offset by the tax cut and the average household will see incomes raised by 0.79% after taxes. This means the average household will get an additional $610 every year under this policy.

Furthermore, housing prices would fall modestly under this policy due to a moderation in demand for housing. Keane and Liu estimate this would mean a 0.7% reduction in average housing price, which would amount to about $2,400 for the average U.S. home.

An interesting wrinkle to Keane and Liu’s simulation is that they actually don’t find that the homeownership rate would decrease, which on its face seems counterintuitive. Why would it depress prices on homes but not the homeownership rate?

Their simulation finds the answer for this: people would reduce the size of their homes. The current uneven playing field between owning and renting doesn’t actually get more people to own homes. It mainly incentivizes ownership of larger homes.

The researchers also use their model to estimate the impact of two proposals to eliminate the mortgage interest deduction.

How the mortgage interest deduction works is that a household can subtract the amount they pay in mortgage interest from their reported income. So this means a wealthy household paying $24,000 in interest and $9,000 in property taxes could effectively save $12,000 from the mortgage interest deduction.

The mortgage interest deduction is an unpopular policy among economists because it essentially functions as a subsidy to high-income households. Since low- and middle-income homeowners usually take the standard deduction, they do not benefit from this deduction. So this means the more wealthy you are and the larger your house is, the more you benefit from the mortgage interest deduction.

Keane and Liu estimate what would happen if the mortgage interest deduction were eliminated and replaced with an across-the-board income tax cut. According to their simulations, replacing the mortgage interest deduction with an income tax cut would drop income tax rates by 4.7%, housing prices by 1.66% (about $5,600 in 2023), and would discourage homeownership slightly, dropping homeownership rates from 64.9% to 64.3%. Incomes would rise by 0.76% (About $590 for the average household) and it would also be a Pareto improvement: all households would have more income under this policy. Benefits for this change would be highest for low- and middle-income households and higher-income households would purchase smaller homes under this scenario.

They also put forth an alternative: replacing the mortgage interest deduction with a revenue-neutral refundable tax credit. This means all homeowners could claim the credit and upper-income homeowners would not have higher benefits than lower-income homeowners. Under this scenario, ownership rates increase from 64.9% to 68.7%, home prices fall by 1.3% (about $4,400), average income rises by 0.58% (about $450 for an average household), and low- and middle-income households gain the most. High-income households end up worse off under this scenario due to not benefiting from the income tax cut in the other mortgage interest deduction elimination.

A lot of money is tied up in subsidies to high-income households to pay for larger homes. Eliminating these subsidies can lead to scenarios that make everyone better off, free up investment for more productive uses, and help low- and middle-income households. We’ll see if Congress pays attention to this well-known problem during its tax reform work this year.

Rent Eats First: The Problem With Our Housing Cost Burden

In September 2024, the U.S. Census Bureau published a news release titled “Nearly Half of Renter Households Are Cost-Burdened, Proportions Differ by Race.” Their analysis found that 49.7% of all American renters are spending at least 30% of their income on rent—a threshold commonly used to define “housing cost burden.”

The report also found that 56.2% of Black or African American renter households exceed this 30% threshold, representing about 4.6 million people. For many of these households, rent is the largest and most inflexible monthly expense—leaving less room for food, healthcare, childcare, transportation, or savings. In short: rent often eats first.

But where does this 30% benchmark come from? Who decided that this ratio was a reasonable measure of affordability?

The answer lies in a set of policy decisions that began in the mid-20th century. In the 1960s, economist Mollie Orshansky at the Social Security Administration developed the federal poverty thresholds based on Department of Agriculture food budgets. At the time, American families spent about one-third of their income on food, so Orshansky multiplied the minimum food budget by three to estimate what a “poverty-level” income should be. Around the same time, U.S. housing policy began adopting a similar one-third rule to judge how much a household could afford to spend on housing. The 30% standard was formally codified in federal housing programs in the early 1980s.

While the 30% threshold is still widely used today by HUD and other agencies, critics note that it may no longer reflect the financial realities of modern households. The cost of housing, transportation, and healthcare has far outpaced income growth in many regions, and a flat percentage may not capture the full picture of affordability, especially for low-income households.

For example, in 2025, the federal poverty line for a family of three is $26,650, or about $2,220 per month. With the national median rent at $1,406, that family would be left with just over $800 to cover all other expenses. In such cases, even spending less than 30% of income on housing can result in financial strain.

So what can be done?

Instead of using a fixed 30% calculation, policymakers could evaluate whether households have enough money left over to cover expenses after rent has been paid. This is called “Residual Income Measures,” and it’s a different way of configuring a budget. The system we have now plans on rent eating first. We look at someone’s monthly income and divide that by three to figure an acceptable monthly housing cost. Residual Income Measures looks at what is left over after all other basic necessities have been met. 

Additionally, we could localize housing cost burden. For example, rent in San Francisco, California is very different from rent in Little Rock, Arkansas. Because of the difference in localities, affordable housing should be considered within its regional context. Two individuals each making $60,000 in San Francisco and in Little Rock will have very different socioeconomic situations. Taking into account regional differences in calculating housing cost burden could improve our calculations. 

Policy solutions for addressing housing cost burden are a different conversation entirely. For example, expanding Section 8 Housing Vouchers could help families who make too much money to be considered “in poverty,” but still navigate daily life with incomes near the poverty line without government assistance. Right now, waitlists are long. Expanding eligibility for housing assistance can fill in some of the gap that exists for families who make too much to qualify but still struggle to cover monthly expenses. 

Additionally, lawmakers could incentivize affordable housing development. Increasing the supply of affordable housing can help meet the growing demand for affordable housing. This can be accomplished by zoning reforms and through nonprofit partnerships. Nonprofits often work directly with individuals most afflicted by high housing costs, and can directly connect the people in need to the new affordable housing developments. 

In the end, long-term solutions may benefit from integrating housing policy with broader anti-poverty programs. Housing cost burden doesn’t occur in a vacuum. Coupling housing support with wraparound case management has the potential to achieve long-term gains. This could look like a single application or website where an individual can apply for Medicaid, SNAP, TANF, and Section 8 vouchers.

What does universal pre kindergarten do for parents?

Last month, we published a cost-benefit analysis of the impacts of a potential universal pre kindergarten program in Ohio. We estimated that a universal pre kindergarten program would lead to between $220 and $750 million of benefits for Ohio, largely in the form of higher future earnings for the children who would be enrolled. 

When we did this analysis, we focused our analysis on impacts that would be realized down the road. Benefits such as future earnings, reduced future crime, and lower participation in special education programs are all beneficial, but they don’t help people today.

When we look at future benefits, we always make sure to discount them so that we capture how much people are willing to pay today for those benefits in the future. There is a lot of debate about how exactly to do this, but it's important to remember that when we are conducting a cost-benefit analysis, we are trying to capture how much people today care about investing in the future. 

A big part of the reason we care about benefits that don’t get realized until far into the future is because there has been a growth of evidence of these long-term benefits in economic research. Recent advances in the study of intergenerational impacts led by the legendary economist James Heckman have shown how the benefits of providing assistance to very young children can compound over time and create huge benefits for years to come. 

However, new research shows that universal pre kindergarten may not just be a long-term policy, it can deliver meaningful short-term economic benefits as well.

A new working paper looks at the effects of universal prekindergarten programs across nine states, specifically focusing on the short term employment impacts. The findings show that universal prekindergarten programs not only increased enrollment in early education, but also boosted labor force participation, employment, and hours worked, especially among mothers of young children. These programs reduced child care constraints, enabling more parents to work or work more hours.

Interestingly, the paper found that the benefits weren’t just limited to parents of young children. Other women, such as informal caregivers or those considering starting families, also saw employment gains, demonstrating the fact that universal pre kindergarten creates positive externalities. 

Importantly, the size of these effects varied by location. There were larger economic gains in areas with higher enrollment and stronger program quality. In other words, well-designed and well-attended universal pre kindergarten programs don’t just help children, they function as a form of economic stimulus, boosting household earnings and labor force participation in the near term.

The authors of this paper also calculated how much additional tax revenue would come as a result of these employment gains. Amazingly, they estimate that there might be enough additional tax revenue to cover the upfront costs of the program entirely. This fiscal impact means that universal prekindergarten might not represent much of a short term burden at all, since it would not have to take away money from other short term public programs.

One limitation of this study is that it did not value time spent at home, effectively ignoring the major cost of the program. While labor market time is valuable for employers and employees, it requires parents to give up nonmarket time, which is valuable on its own.

So, while the long-term benefits of universal pre kindergarten are substantial and well-documented, this new research reminds us that the economic case for universal prekindergarten isn't just about the future, it's also about helping families and communities today.

How do cigarette taxes impact household budgets?

When Governor DeWine announced his budget proposal earlier this year, there were a lot of major changes that were fascinating for policy analysts. At Scioto Analysis, we’ve written about things like stadium subsidies, library funding, and especially the Child Tax Credit.

Another important part of the Governor’s budget was an increase in taxes on cigarettes, marijuana, and gambling, collectively referred to as “sin taxes.” My colleague Rob Moore wrote about the benefits of taxing these goods from an economic perspective, but he also acknowledged the most frequently cited downside: sin taxes are regressive. On average, lower-income people spend a larger percentage of their income on these goods, and as a result they bear most of the burden of these increased taxes. 

When faced with higher prices, people will usually consume less of a good. From a theoretical perspective, we often ignore the actual mechanism of people participating less in a market. The effect of one out of 100 people quitting smoking is the same as all smokers reducing their smoking by 1%. 

In practice though, people have different responses to increased prices. Some people who have a low willingness to pay for these goods will stop consuming them altogether, people with a moderate willingness to pay may adjust their consumption by a little, and people with a high willingness to pay will not change their consumption and instead just manage the higher prices. 

The third group is the one I want to highlight today. 

A new working paper released this month looks into the question of how households change their consumption habits when faced with higher cigarette taxes. They use two approaches to answer this question. First, they surveyed current smokers and asked them how they would respond to a hypothetical price increase. Then they looked at actual consumption data and quantitatively assessed how people responded when taxes went up. 

The survey responses aligned with what we expect to happen in practice. Some people said that if faced with higher prices they would try to quit, some people said they would try to reduce their smoking habits, and some people said they would just deal with the higher prices. 

The most interesting findings came from the quantitative analysis. When households (particularly low-income households) are faced with higher cigarette prices, they tend to reallocate their spending away from what the authors describe as “human capital forming expenditures.” These authors suggest that households offset nearly 70% of the increased cost of cigarettes with reduced human capital expenditures. 

Previous research on cigarette taxes has shown that higher taxes lead to better human capital outcomes, such as better health and higher education, despite lower spending on these goods by households. This seems to suggest that the benefits of overall reduced consumption of cigarettes outweighs the increased cost and resulting reduction in human capital investment. 

One commonly suggested policy option for reducing the regressivity of taxes is to take the additional revenue and rebate it back to the households that bear the burden. In this case, the state could use the revenue generated from a tobacco tax and use it to subsidize the human capital-developing goods that people consume less of as a result. Think financing college scholarships for low-income households with tobacco tax revenue.

Regressive sales taxes present challenges for policymakers and families. This paper highlights undesirable consequences that come from increasing the costs of goods that have negative externalities. However, benefits still may outweigh the costs depending on the exact structure of the tax, and with some careful planning many of the downsides can be offset.