What’s the matter with data centers?

In the past year, data center development in the United States has transformed from a topic of excitement among economic development enthusiasts to a widespread issue of concern among regular members of the public.

Honestly, I can’t go half a day without someone bringing up data centers. A phrase that seems wonky and technical has captured public imagination in a surprising way in 2026.

This isn’t just my experience, either. Google Trends data shows the exponential growth in the search term “data centers” in Ohio over the past year. Ohio residents googled the term “data centers” 10 times more in May 2026 than they did a year earlier.

What is driving this rising interest in data centers in Ohio and beyond?

One explanation is environmental impact. Many are concerned about the impact of data centers on local water supply, a concern that has made a lot of headway in social media circles.

Another is electricity prices. Many fear the increased demand for electricity to power data centers will lead to higher prices for consumers. In an era of fears of growing problems with affordability and an economy that is still living in the shadow of 9% inflation in 2022, anything that could lead to higher prices still poses an area of concern for consumers.

Members of the public are also concerned about public investment in data centers. Ohio created a framework for economic development incentives for data centers over a decade ago in the Kasich administration’s first budget bill. Two years later, the incentive was expanded to more data centers. A recent story by Signal Ohio reported Ohio’s sales tax exemption for data centers ballooned from a projected $136 million in deferred sales taxes to $555 million in 2024 and $1.6 billion in 2025. A recent poll of Ohio economists conducted by my firm Scioto Analysis found only one of 14 economists believing tax incentives for data centers were an efficient strategy for job creation.

Another factor driving public interest in data centers is community change. Many of these arguments sound a lot like community opposition to solar panels or wind turbines, with people saying they are ugly or change the character or tenor of a neighborhood. For people who see a number of data centers crop up in their neighborhood in a short period of time, this sort of change can be shocking.

A final reason people are worried about the proliferation of data centers comes from a broader concern: social change. I was on a panel with social critic Michael Clune and tech consultant Nicole Jackson hosted by workforce strategy organization ASPYR last month. One thing that stuck with me that Clune said was that polling around adoption of the internet when it appeared in the 1990s was optimistic, even as few people were using it. This trend has been reversed with artificial intelligence: the technology is seeing rapid adoption, and the public is generally pessimistic about it. A viral speech from a Ravenna City Council meeting given against approval of a data center by a former programmer captures this sentiment well.

The truth is that each of these areas of concern for the public have shades of grey. Data centers impact the environment, but not nearly as much as agriculture or transportation. They impact energy prices, though these impacts can be mitigated by construction of new power generation projects to support them. Data centers and AI will change our communities and our society, but our communities and society are changing with these particular changes or without them.

But Ohio lawmakers can decide whether they want to spend billions of dollars on projects that are likely to happen without those incentives. Last week, Representative Tristan Rader and ten other members of the Ohio House of Representatives introduced legislation to phase out the data center subsidy starting later this year, leaving new data centers to the whims of market forces. While this change would not curtail all concerns with data center development in the state, it would at the very least give a chance for data center development to come a little closer to what an efficient market would provide.

This commentary first appeared in the Ohio Capital Journal.

The Policy Problem Lurking in Our Sewage

Have you ever stopped to wonder what ultimately happens to things you flush down the toilet? If you are like me, probably not. Or, to the degree you have ever paid any mind to the fate of your waste, you probably know that it ends up at a wastewater treatment facility. But what happens after it has been treated there? The answer, in many places around the U.S., is that it gets renamed and spread onto farmland.

For decades, this practice has been widespread across the U.S. Recently, though, this once-obscure corner of waste management practice has become a live issue for state legislatures and environmental agencies across the country. As concerns about contaminants in processed sewage waste have grown, states have begun taking a much closer look at what exactly is being spread onto their land and what should be done about it.

What are Biosolids, and Where and Why Do We Use Them?

Biosolids are the official term for what remains of waste after it has been processed and treated at a wastewater treatment plant. The liquid components of sewage are separated from the solids, then the solids are treated physically and chemically, resulting in biosolids. Biosolids are also commonly referred to as sewage sludge. 

Under Environmental Protection Agency rules, biosolids may be used on forests, parks, golf courses, rangelands and more; however, it is most commonly applied to agricultural land. 

Biosolids can improve the soil, making farms more productive while costing farmers very little, since the biosolids are generally provided by waste systems for free. The practice also comes with environmental benefits. Applying biosolids sequesters carbon in soil and reduces demand for phosphorus-based fertilizer, which is non-renewable but necessary for crops.

Moreover, biosolid waste has to be disposed of somehow. Recycling waste by applying it to land generates fewer greenhouse gas emissions than other options, such as burning the waste or dumping it in landfills would. It also saves public dollars which would otherwise need to be spent on these forms of disposal.

States vary in how much biosolids they apply to agricultural land. 

Comparison: What Percentage of Biosolids are Applied to Agricultural Land by State?

Chart Data: National Biosolids Data Project

Per- and Polyfluoroalkyl Substances (PFAS) in Biosolids

Since 1993, the Environmental Protection Agency has required biosolids applied to land to be processed under standards which includes requirements on sanitizing the waste for pathogens and sets maximum levels for metal contamination. In a September 2000 report, the Environmental Protection Agency cited the main issue of public concern over the practice as being the unpleasant smells people believe biosolid application will produce in their neighborhoods.

In the decades since, however, more substantial concerns have emerged. A 2013 study tested biosolid waste nationwide and found widespread contamination of chemicals known as “PFAS.” Subsequent research found similar results. 

What is “PFAS?”

PFAS is an umbrella term for thousands of related chemicals which have been widely used in industrial processes and consumer products since the 1950s. All PFAS have a carbon-fluorine bond in their chemical makeup. This type of bond is particularly strong, and that property means these chemicals do not break down easily. This trait is what earned them the nickname “forever chemicals,” as it gives them their tendency to persist in the environment.

PFAS’ presence in biosolids used for land application constitutes a public health concern because PFAS exposure has been linked to increased risks of kidney, prostate, and testicular cancers, increased cholesterol, low birth weight, accelerated puberty in children, and other negative health outcomes.

Policy Responses to Biosolid Contamination Problems

The Environmental Protection Agency states that due to these risks, it is taking steps to research and remediate biosolids PFAS contamination. The agency recommends that states monitor their own biosolids for PFAS in the meantime and work to identify industrial sources of the problem. 

States across the U.S. have responded to this guidance from the Environmental Protection Agency in different ways, with some moving beyond monitoring and source identification.

Testing and Threshold Approaches

States in this category are closest to the Environmental Protection Agency’s recommendation for policy to minimize the harms of PFAS in biosolid re-use. They are testing biosolids for PFAS, and in many cases certain levels of contamination trigger source identification efforts. Some states go further, forbidding or limiting the amount of biosolids which can be applied to land if PFAS is detected above a certain threshold. 

Colorado

In 2023, Colorado’s Department of Public Health and Environment began requiring biosolids preparers to monitor for PFAS contamination. If the level of perfluorooctanesulfonic acid (one specific type of PFAS, which Colorado uses as an indicator of more general PFAS contamination) detected exceeds 50 parts per billion, preparers are required to contact Colorado’s Department of Public Health and Environment and conduct an investigation into the contamination source.

Maryland

In 2024, Maryland’s Department of the Environment began requiring wastewater treatment plants to test for PFAS contamination. If the level found exceeds 100 parts per billion, the waste is prohibited from land application. If PFAS concentrations are 20-99 parts per billion, there are limits on how much biosolid may be applied per acre of land.

In 2026, Maryland updated these rules with the passage of Senate Bill 719. The new law restricts PFAS concentrations in biosolids used for land application to 25 parts per billion. Treatment facilities are required to develop mitigation and monitoring plans if their biosolids are found to have PFAS concentrations over 50 parts per billion. The new law will take effect in 2028.

Michigan

Michigan began a policy of testing biosolids for PFAS in 2021 and amended their approach in 2022 and 2024. Biosolids with PFAS contamination above 100 parts per billion are currently prohibited from land application use. Levels between 20-99 parts per billion trigger a source investigation and limits on how much biosolid can be applied per acre of land.

Minnesota

In 2025, Minnesota’s Pollution Control Agency released a strategy requiring wastewater treatment facilities which intend to apply biosolids to land to test for PFAS. If contamination greater than 125 parts per billion is detected, land application is not allowed and the source of the contamination must be investigated. Samples with 20-124 parts per billion may still be applied, with limits on how much can be used per acre of land. These samples also require a contamination source investigation.

Rhode Island

Rhode Island has an existing permitting process for anyone wishing to apply biosolids to land. In 2025, the state enacted a law requiring PFAS test results on the biosolids be included as part of the permit application. Those holding permits will also be required to furnish test results quarterly. No specific contamination threshold for rejection was outlined in the law.

Virginia

In 2026, Virginia passed a bill which will phase in testing for PFAS in biosolids. Wastewater treatment facilities will be required to test for PFAS, and if concentrations are found to be greater than 50 parts per billion, biosolids may not be used for land application.

Wisconsin

In 2024, Wisconsin’s Department of Natural Resources issued an interim strategy which requires biosolids to be tested for PFAS. If concentrations above 150 parts per billion are found, land application is not allowed and an investigation into the source of the contamination is required. If values are between 20-149 parts per billion, an investigation into the source should be performed, but application is still allowed, with limits on how much biosolid may be applied per acre. 

Differing Thresholds by State

Bans on Biosolid Application

These states have banned the practice of biosolid land application through new state laws.

Connecticut

In 2024, Connecticut passed PA 24-59, which banned the use and sale of biosolids for land use. The state had already prohibited direct land application of biosolids, so the new law primarily addressed commercial biosolid fertilizers and soil amendments which the earlier ban did not cover. 

Maine

In 2022, Maine became the first state to ban biosolid land application entirely.

Legislation to Study Contamination

This policy choice commits states to studying the levels of PFAS contamination for a period of time before moving forward with any future potential actions.

Washington

In 2025, the State of Washington enacted a law which requires facilities to test biosolids for PFAS between January 2027 and July 1, 2028, and supply results to the state. The law requires that the data be used to produce a report to the state legislature which will include recommendations on future legislative action.

Pending Legislation

Several states have introduced legislation which could alter their policy regarding biosolid land application. Some legislatures are working to introduce moratoria on biosolids land application, while others are attempting to establish PFAS monitoring practices.

Florida

During the 2026 legislative session, Florida’s legislature approved a bill which would require wastewater treatment facilities to test for PFAS quarterly and submit results to the Florida Department of Environmental Protection. If signed into law, this bill will take effect on July 1, 2026.

Illinois

Illinois’ Senate Bill 3917 would introduce monitoring requirements for PFAS in biosolids. The bill passed both the Illinois House and Senate and is currently awaiting the Governor’s signature.

New Hampshire

New Hampshire’s House Bill 1275 would impose a 5-year moratorium on the application of biosolids for agricultural use if passed into law.

New York

Senate Bill S9115A was introduced in the New York State Senate in 2026. This bill would establish a five-year moratorium on biosolid land application and require PFAS testing and reporting for biosolids. It is currently scheduled for a vote on the New York State Senate floor.

New York’s Department of Environmental Conservation already requires sampling of all biosolid sources and sets thresholds for acceptable concentrations. If levels above 20 parts per billion are not brought to less than 20 within one year of detection, biosolid use from that source is prohibited.

Where State Policies May Go Next

In the absence of a uniform federal standard, states are likely to continue developing their own approaches tailored to their local political environments, degree of local PFAS contamination concerns, and competing waste management and agricultural priorities. The result is a rapidly evolving policy patchwork across the U.S.

A core concern for those choosing to ban or heavily restrict biosolids application is what to do with all the biosolids waste otherwise. In Texas, House Bill 1674 was introduced in 2025. The bill would have regulated the use of biosolids as land fertilizer, but it ultimately failed in part due to opponents’ concerns about how much other disposal methods would cost wastewater utilities. Maine’s experience has proven those concerns are not unfounded. Since their outright ban of biosolid land application in 2022, nearly all biosolid sludge in the state now goes to one landfill. The landfill has applied for an expansion to accommodate the influx, but the process has been stalled by an appeal from a conservation organization concerned about the waste’s PFAS concentration polluting a nearby river.

Owing to these concerns, source control efforts may become more important to regulatory responses in the coming years. Michigan, Minnesota, Colorado, and Wisconsin all have policies which focus on identifying the sources of the PFAS contamination. If these states are successful at reducing PFAS in waste before it reaches wastewater treatment plants, they may be able to mitigate the public health risk without banning land application of biosolids, and other states may be able to follow suit in setting their own policies.

What is the difference between descriptive and inferential statistics?

Policy analysis is a data-driven field, but in this work we come into contact with a large number of people that don’t have as much experience working rigorously with data. Lobbyists, advocates, communications professionals and many more all play a role in the cadre of people who are involved in getting our policymakers the necessary information to make better informed decisions.

I wanted to take a moment today to talk to some of my colleagues who are working towards the same goal of evidence-based policymaking, but don’t necessarily have the formal background in statistics and explain one of the most common challenges that data analysts face.

Descriptive vs. inferential statistics

When most people hear the word “statistics,” they think of the specific numbers that we use to describe things in the world. However, I want to talk about statistics as a particular type of mathematics: the practice of doing statistical analysis. When we do statistics, it is almost always in one of two major categories of analysis: descriptive or inferential.

Descriptive statistics are used to summarize and describe data that we already have. If I tell you the average income in a county, the unemployment rate in a state, or the percentage of students who passed an exam, I am describing observed information. These statistics help us understand what happened, but they do not necessarily tell us why it happened or whether the same thing would happen somewhere else.

Inferential statistics go a step further. They use data from a sample to draw conclusions about a larger population. This is important because collecting information from every person, business, or household is often impossible. Instead, we gather information from a smaller group and use statistical methods to estimate what is likely true for the broader population.

How samples help us understand populations

Suppose you wanted to know the average height of every adult in Ohio. Measuring all 9 million adults would be extraordinarily expensive and time consuming. Instead, you might randomly select a few thousand people and measure them.

If the sample is chosen properly, the average height in the sample will probably be very close to the average height in the full population. It will not be exactly correct, but it will usually be close enough to answer most practical questions.

The same principle applies throughout policy analysis. Pollsters survey a few thousand voters to estimate public opinion. Economists analyze a subset of households to estimate poverty rates. Researchers study a group of patients to learn about the effectiveness of medical treatments.

The key idea is that we are rarely interested in the sample alone. We use the sample because we want to learn something about a much larger population. 

Notice how quickly the lines between descriptive and inferential statistics can get blurred. Calculating the average height of a group is a descriptive task, but intentionally calculating the height of a representative sample is inferential. This might seem a bit pedantic, like arguing about whether we should be saying this data or these data, but I would argue that this distinction is essential to understand.

The challenge of generalization

One of the most common mistakes people make when interpreting research is assuming that a finding from one population will automatically apply to another. This works when we’re careful about how we set up our analysis, but it can quickly fall apart and lead to incorrect analyses when we interpret results too broadly.

Imagine a workforce development program that increases employment among participants in one city. That result tells us something useful about that specific program in that specific location. However, it does not necessarily tell us what would happen if the exact same program were implemented somewhere else.

The local economy may be different. Participants may have different educational backgrounds. Employers may have different hiring practices. Transportation systems, housing costs, and labor market conditions may all vary. Even when the underlying program is identical, the environment surrounding it may not be.

This problem appears constantly in policy research. A tax policy that works well in one state may produce different outcomes in another. An educational intervention that improves test scores in one district may have little effect elsewhere. Researchers often refer to this issue as external validity. In other words, how well do we think results from one study can be generalized beyond the group that was originally examined.

This doesn’t mean that we can’t learn anything from past studies or programs that have been implemented in other locations. Often in policy analysis this is the only kind of data we have access to. What it does mean is we need to think about what the unique factors are that might influence these outcomes, and we need to do sensitivity analysis to try and figure out what might happen if some of our assumptions don’t hold.

Understanding the difference between descriptive and inferential statistics is important for anyone who works with data regularly. It is essential to be able to identify whether a piece of information is a simple statement of the way things are, or if it has been carefully constructed to enable us to make generalizations about some population.

What are the top anti-poverty programs in the United States?

Each September, the United States Census Bureau publishes a “Poverty in the United States” report on the current status of poverty across the country. In the report, the Census Bureau reports both the Official and Supplemental Poverty Measures across all fifty states. 

We’ve written about the difference between the official poverty measure and the supplemental poverty measure many times in the past at Scioto Analysis. The Official Poverty Measure estimates poverty rates using pre-tax income and family composition. The Official Poverty Measure can be somewhat limited in its scope since it doesn’t make any adjustments to income other than for inflation, though it does provide a consistent definition of poverty over time. 

The Supplemental Poverty Measure expands the definition of poverty to include noncash benefits, account for taxes and necessary expenses, and consider geographic cost-of-living differences. The Supplemental Poverty Measure presents a more comprehensive view of poverty and economic well-being across the country than the official poverty measure by adjusting for fiscal and geographic impacts on household resources and needs. Because it accounts for pre-tax income and noncash benefits, the Supplemental Poverty Measure is also useful for evaluating the effectiveness of anti-poverty programs in the United States.

The Census Bureau’s poverty report makes evaluating the effects of anti-poverty programs straightforward. Each year, they report on the change in the number of people in poverty after including each anti-poverty program in the Supplemental Poverty Measure’s calculation. In 2024, these programs resulted in at least 29 million people lifted out of poverty, 8-9% of the total U.S. population.

I decided to look into which anti-programs are the most effective tools at reducing poverty across the country. According to the Census Bureau, the top five most effective programs in reducing poverty in 2024 were Social Security, refundable tax credits, SNAP, Supplemental Security Income, and housing subsidies.

Social Security lifts 29 million Americans out of poverty

According to the Census Bureau, Social Security was responsible for lifting 28.7 million people out of poverty in 2024. This makes Social Security by far the most impactful anti-poverty program in the country by sheer magnitude. The most significant proportion of Social Security recipients are retirees– about 20.1 out of the 28.7 million people who were moved out of supplemental poverty are aged 65 years or older.

In 2024, the U.S. federal government spent about $1.5 trillion on Social Security, accounting for 15.9% of total federal spending. Despite the success of Social Security in providing supplemental income to retirees and disabled persons and reducing poverty, the program continues to grow more expensive each year. 

The Social Security Administration projects that by 2035, federal taxes will only be able to fund about 75% of the cost of Social Security. Given how many people the program affects, it’s no surprise that worries about the long-term feasibility of Social Security have grown more serious over the past several years.

Refundable tax credits lift 6.8 million Americans out of poverty

In 2024, the Census Bureau reports that refundable tax credits lifted about 6.8 million people out of poverty in the United States. Refundable tax credits include the Earned Income Tax Credit and the refundable portion of the Child Tax Credit. 

In a blog post I wrote earlier this year, I discussed the difference between grants and tax breaks in fighting poverty. I found that tax breaks are often much more efficient than grant-based anti-poverty programs. The administrative burden for tax-based anti-poverty programs is lower than other anti-poverty programs, as administering funds through the tax code is less costly than funding state and local welfare programs through grants.

Another reason refundable tax credits are so good at moving people out of poverty is that they allow households to spend money how they see fit instead of requiring the government to understand the individual needs of different households to achieve economic security. This is a similarity that refundable tax credits have to Social Security: these programs provide direct cash assistance to households, which allows them to use the money as they see fit.

SNAP lifts 3.6 million Americans out of poverty

The third most effective anti-poverty program according to the Census Bureau is SNAP. If you don’t know, SNAP stands for the Supplemental Nutrition Assistance Program, and it provides monthly funds for low-income households to purchase food. In 2024, SNAP was responsible for lifting about 3.6 million people out of poverty.

SNAP is different from Social Security or refundable tax credits because it provides in-kind benefits, which are non-cash benefits that are provided to meet specific needs (in this case, food). However, in many ways, SNAP functions similarly to cash-based anti-poverty programs by freeing up a significant portion of monthly income for other household expenses.

According to the ALICE survival budget for Ohio, the state where I live, a single adult’s monthly survival cost for food is $443, nearly 20% of their total monthly budget. The average SNAP benefit for a family in 2023 was about $332, relieving a significant portion of food spending for other household expenses.

Supplemental Security Income lifts 2.5 million Americans out of poverty

According to the Census Bureau, Supplemental Security Income is responsible for lifting about 2.5 million people out of poverty in 2024. Supplemental Security Income provides monthly cash payments to low-income older adults and people with disabilities.

Despite the program’s effectiveness in reducing poverty, The Brookings Institution estimates that just 50% of people who are eligible for Supplemental Security Income receive benefits. This may be partially due to the program’s complicated application and approval process. In particular, Supplemental Security Income has extremely strict limitations on assets. To qualify for Supplemental Security Income, countable resources may not exceed $2,000 for an individual or $3,000 for a couple, a metric that has not been updated since 1989.

Housing subsidies lift 2.1 million Americans out of poverty

According to the Census Bureau, housing subsidies are the fifth most effective anti-poverty program in the United States, lifting about 2.5 million people out of poverty in 2024. Housing subsidies in the United States generally fall into three main categories: rental housing assistance, assistance to state and local governments, and assistance for homeowners. To reduce poverty, the main goal of housing subsidies is to make housing affordable for low-income households. Two of the major forms of housing subsidies for low-income households are section 8 vouchers and public housing. 

Section 8 vouchers are rental subsidies which allow renters to pay a lower, more affordable monthly rent payment to private landlords. Renters typically pay about 30% of their gross monthly income on rent, and the government pays the difference between the amount charged by the landlord and the amount paid by the renter. Public housing refers to subsidized residential properties that are owned by the government to provide more affordable places for low-income households to live.

Housing subsidies can be a great way to reduce poverty since they focus on the most significant portion of most households’ monthly budgets. However, the shortage of affordable housing continues to grow across the country, and most housing subsidies are associated with long waiting lists and stigma.

What is a negative income tax?

Recently, we released a study on inequality in Ohio and some of the policy options that impact it. One topic was the Negative Income Tax, an idea first proposed by Milton Friedman as an alternative to the in-kind transfers we typically associate with the social safety net. 

How does a negative income tax work?

To understand how a negative income tax works, it is helpful to first consider how a regular income tax works. Above a certain threshold, the government collects a percentage of your income. If the tax rate was 10% for income above $10,000, then someone making $20,000 has to pay $1,000 in taxes. Negative income taxes work in reverse. Instead of paying a percentage of your income above a threshold, someone receives a match for the percentage of their income that falls below some threshold.

For example, let's say that below $10,000 there is a negative tax rate of 50%. Someone with no income at all would receive $5,000 from the government, and someone earning $5,000 would receive $2,500. Once a person’s income rises above the threshold, the subsidy disappears and the ordinary tax system takes over.

One of the main goals of a negative income tax is to create a smoother transition between receiving assistance and earning additional income. Many traditional welfare systems phase out benefits abruptly as income rises, which can create situations where earning an extra dollar leaves someone little better off or even worse off overall. This phenomenon is called the benefits cliff, and it is a major policy issue. Because the negative income tax phases out gradually, individuals continue to gain financially from working more and earning higher wages.

Why do economists like negative income taxes?

One of the most clear benefits of the negative income tax is its simplicity. Rather than operating dozens of separate programs with different eligibility rules, benefit formulas, and administrative requirements, a negative income tax consolidates assistance into a single cash transfer system tied directly to income. It would take little additional work from the IRS, and everyone who files their taxes would automatically be enrolled.

Supporters also argue that cash transfers give households more flexibility than in-kind benefits. Instead of policymakers deciding exactly how assistance must be used, recipients can allocate resources according to their own needs. One household may prioritize rent, another childcare or transportation costs. Economists generally believe people managing resources at the household often have better information about their own needs than centralized benefit administrators or legislators do.

Costs of a negative income tax

Like any large transfer program, a negative income tax could be expensive. The total cost depends on how generous the benefits are, where the income threshold is set, and how quickly payments phase out as earnings rise. The good thing is that policymakers could easily control how much this costs since they have historic tax data to look at, but it is likely that this would be a big line item in any public budget.

One option to finance a negative income tax would be higher taxes elsewhere. That could mean raising income taxes, consumption taxes, or corporate taxes depending on how policymakers chose to structure the system. While this would allow the program to exist alongside much of the current safety net, higher taxes also create their own economic costs.

Another option would be replacing existing welfare programs with a negative income tax. This was closer to Milton Friedman’s original vision. Instead of operating many separate assistance programs, the government could consolidate portions of the social safety net into one direct cash transfer system. This could potentially reduce administrative complexity and give recipients more flexibility in how they spend assistance.

However, replacing existing programs also creates tradeoffs. Some households may benefit more from direct cash transfers, while others may rely heavily on targeted programs like housing assistance, Medicaid, or food assistance. A single cash-based system may be simpler, but it may also provide less support for people with unusually high medical expenses, disabilities, or other specialized needs.

Ultimately, a negative income tax is another option policymakers have for reducing inequality. Like most economic policy questions, there is no solution without costs. Policymakers must decide which tradeoffs they are most willing to accept and which goals they want the system to prioritize.

Why is SNAP enrollment falling?

Earlier this year, we wrote about some of the federal changes arriving to SNAP between 2025 and 2027. Prior to this year, states covered half the administrative costs associated with SNAP and none of the benefit costs. With the passing of the One Big Beautiful Bill Act, however, states are now required to pay 75% of administrative costs and a portion of benefit costs. According to the Georgetown Center on Poverty and Inequality, state budgets for SNAP are more than doubling due to changes from the One Big Beautiful Bill Act. 

At the start of this year, my colleague Michael predicted that changes to SNAP benefits would be one of the biggest economic stories of 2026, and we’re now seeing these changes take effect. Between the July 2025 passing of the One Big Beautiful Bill Act and the start of the year, the use of federal food assistance is down in every state and Washington, D.C. Overall, nearly 3.5 million people have lost access to SNAP benefits. 

The states that have been hit the hardest by changes to SNAP are Georgia, Arizona, Florida, California and Texas. All five of these states are among the top fifteen most populous states in the country. This means that proportionally, it makes sense for these states to have lost the most SNAP enrollees. However, at Scioto Analysis, we’re most interested in state and local policymaking. Today, I looked into each of these five states to try to understand the key driving factors in SNAP reduction for each state.

Georgia

Between July 2025 and January 2026, more than 460,000 residents in Georgia have lost access to SNAP. Even after this reduction, roughly one in six Georgia residents (~1.4 million people) receive SNAP benefits each month, slotting Georgia in as the sixth-highest state in the nation for SNAP participation.

In addition, Georgia lawmakers have proposed stricter legislation that would require citizenship verification, annual recertification, and heavier limitations on which items Georgia SNAP recipients can buy at the grocery store. These new restrictions could result in even fewer Georgia residents maintaining access to SNAP benefits due to administrative churn or stigma.

Arizona

Between July 2025 and January 2026, the number of SNAP recipients in Arizona plummeted by about 42%, from nearly about 880,000 to just 510,000. As of April, that number dropped to a meager 235,000 recipients. Across nine months, around nine percent of the total population in Arizona lost access to SNAP.

Since last year, Arizona has been struggling with a massive backlog of SNAP applications, with some families still waiting for updates after initially applying in August. Arizona was a state that moved quickly to overhaul their SNAP application process, meaning that other states may start to experience similar backlogs. The Arizona Department of Economic Security attributes the backlog to a new program that complies with new payment error rate requirements.

The combination of stricter SNAP regulations and rising grocery prices have stretched family budgets across the state. Some food banks in Arizona have reported a 17% increase in traffic compared to this time last year.

Florida

In Florida, about 270,000 have lost access to SNAP benefits between July 2025 and January 2026. SNAP recipients temporarily faced weeks of SNAP benefit suspension due to the government shutdown last fall. Florida residents briefly saw their full benefits reinstated in mid-November before facing Florida’s implementation of new federal SNAP requirements at the start of December. The groups most impacted by the volatile SNAP eligibility in Florida are seniors and veterans.

Florida also faces one of the highest payment error rates in the country, at around 15%. This error payment rate exceeds the new federal requirement to keep payment error rates below 6% to avoid harsh financial penalties. To help reduce payment errors, Florida legislators have proposed funding a new artificial intelligence system designed to reduce payments made in error. The proposal would include $4 million to implement machine learning techniques aimed at identifying root causes of incorrect SNAP eligibility determinations and recommending improvements to solve such problems. 

If implemented correctly, artificial intelligence solutions to incorrect eligibility determinations could save Florida millions of dollars in funding in the long-run. However, there are many risks associated with the increased use of artificial intelligence in benefits programs, such as reinforcing inequities related to race, gender, immigration status, or other demographic categories.

California

From July 2025 to January 2026, 260,000 SNAP recipients lost their benefits in California. One policy change unique to California from the One Big Beautiful Bill Act that may have contributed to this initial drop is a utility-related loophole that the act closed at the start of November. In the past, California gave families a tiny annual energy payment which triggered a special utility deduction that lowered their counted income and boosted their monthly food assistance. Now, households without a senior or a person with a disability can no longer use this workaround, which has shrunk monthly benefits and removed thousands of people from the program due to new paperwork hurdles.

One of the provisions in the One Big Beautiful Bill Act limits SNAP eligibility to U.S. citizens and lawful permanent residents. California has the largest unauthorized immigrant population in the country at about 2.9 million people, accounting for about 20% of the nation’s total unauthorized immigrant population, and the second-largest refugee population in the United States. This means that changes to SNAP eligibility for immigrant populations disproportionately impacts California residents. The immigrant related eligibility changes went into effect in California at the start of April, and a total of 72,000 humanitarian immigrants are expected to lose SNAP coverage in addition to the 260,000 residents who already lost coverage.

Texas

From July 2025 to January 2026, around 250,000 SNAP recipients lost coverage in Texas. More recent state data shows that around 500,000 fewer residents are eligible for SNAP in Texas between April 2025 and May 2026. Many residents who lost SNAP benefits lost coverage due to stricter work requirements.

However, a more unique issue that Texas residents are facing regarding SNAP benefits relates to harsher immigration crackdowns over the past year. Similar to California, Texas has much higher immigrant populations than the rest of the country. Many Texas households include undocumented immigrants who were already ineligible for SNAP benefits but had children or family members in the household who could claim SNAP benefits. Texas is one of at least 27 states who forwarded SNAP information to the Department of Homeland Security. Because of this, many children or family members of undocumented immigrants have foregone federal assistance to mitigate risks of deportation.

Looking Ahead

Moving forward, many states plan to introduce the stricter SNAP regulations outlined in the One Big Beautiful Bill Act starting in June and for the rest of 2026. As state and local governments continue to implement these regulations, SNAP coverage is likely to worsen across the country even more.

Do school cell phone bans work?

Last summer, Governor Mike DeWine signed the Ohio state budget into law. The budget made Ohio’s income tax flat, made a number of changes to the state property tax system, included provisions to decrease state Medicaid support if the federal government reduced its support, and put $600 million toward a new Cleveland Browns stadium. But probably most important to Ohio’s 1.8 million school-age children was that this bill banned cell phones in schools.

Cell phone use in general and social media use in particular has come under scrutiny by people in the public health field, especially those focused on mental health. Researchers argue there is a relationship between heavy social media use and depression, anxiety, loneliness, and suicidal ideation. A survey released last year by the National Center for Education Statistics reported that most of the public school leaders they surveyed believed cell phone use was hurting student academic performance.

These opinions have led to policy adoption in states across the country. According to a Newsweek report in January, over half of U.S. states have enacted statewide school cell phone bans. These bans occur in states across the country, with states in the Northeast, Midwest, South, and West enacting bans, though bans are most prominent in the South, with every Southern state besides Mississippi enacting a statewide ban.

In my daily life, I hear about this ban. My fiancée is a high school geometry teacher at Columbus City Schools, and she has to enforce this ban day to day. It has been a challenge to say the least.

Since some of these bans have been in place for a few years now, we are starting to get some evidence back about their effectiveness. 

I was interested in this topic after I saw a recent National Bureau of Economic Research working paper on the topic. This change studied a 2023 policy in Rio de Janeiro that banned cell phone use in schools. The researchers compared the test scores of students in schools that adopted the policy before and after the district-wide change to test score changes among students in schools that already had a no cell phone policy before the districtwide policy was put in place. The researchers estimated the cell phone ban caused a 0.06 standard deviation increase in test scores, an increase comparable to small-group math instruction programs studied in Norway and free school meal programs in New York City.

These results reflect a study on English cell phone bans a decade earlier that compared schools that enacted bans versus schools that did not, finding a 0.07 standard deviation increase in test scores among schools that enacted bans compared to schools that did not.

A study by Swedish researchers on data from Swedish schools in 2020 gave more pessimistic results. They used the same approach researchers used in the study on English schools but found no effect. This may have been due to more strict environments in place in Swedish schools already: a schoolwide ban is unlikely to have an impact if teachers are already effectively banning cell phone use at the classroom level. A 2024 study of an Australian cell phone ban also was unable to find any academic or social results from cell phone bans.

A 2024 study out of the Norwegian Institute of Public Health found social and academic benefits from cell phone bans in Norway, particularly for girls. The researcher found smart phone bans led to less health care take-up for psychological symptoms and disease, less bullying, higher GPAs, and more likelihood to enroll in high school among girls. She also found less bullying among boys caused by the bans.

The first prominent study on U.S. cell phone bans focused on a cell phone ban in Florida. Researchers found a significant increase in suspensions among students right after bans were put in place and that these were higher among Black students. They also found that this suspension spike dissipated after a year, suggesting there was an adjustment period associated with the new policy. They found test scores increased in the second year after buildings adopted the policy and that attendance also improved in those schools. The researchers suggested this may be a mechanism for increases in test scores: less cell phone use in school leads to high attendance, which then increases test scores for students.

Last month, a nationwide study of U.S. cell phone ban data compared students in schools across the country that banned cell phones versus schools that did not. The results were not particularly rosy. In the first year of adoption of a cell phone ban, student discipline rates increased and well-being fell. While these bans led to less cell phone use, the researchers did not find increases in test scores, attendance, or attention in class. There was some silver lining to the study, though: by year three, the disciplinary spike had dissipated, and student well-being rebounded and ended up even better than it had been before the cell phone ban.

There are few things I take away from these studies. One is that while test score increases have not been observed in all contexts where bans were put in place, there are a number of contexts where test scores did improve after cell phone bans. This does seem to suggest that cell phone bans can be a tool for increasing student academic performance, though not a perfect one.

Another takeaway I have is that transitions matter. Some interventions might be needed in the first year of a cell phone ban to ensure that disciplinary incidents do not rise too quickly. But on the other side of a first year of implementation, cell phone bans can lead to better attendance and better academic performance for students.

If there is anything that makes me most encouraged as a policy analyst around cell phone bans, it is that the truly negative impacts of cell phone bans, disciplinary increases, are temporary, while the benefits have seemed to last. This suggests that if schools are willing to deal with some short-term disruptions, cell phone bans could be an effective tool at increasing student academic performance and well-being.

Ohio economists: gas tax holiday costs outweigh benefits

In a survey released this morning by Scioto Analysis, 11 of 19 economists indicated that a three-month gas tax suspension would not provide meaningful financial relief to Ohio residents.

Since January, Ohio has experienced the most dramatic increase in gas prices out of all fifty states. In response, members of the Ohio House of Representatives have been considering a potential three-month suspension of the state’s motor fuel tax. The tax is $.385 per gallon for gasoline and $.47 per gallon for diesel. Tax revenue from Ohio’s fuel tax most commonly goes toward funding state infrastructure.

Most respondents disagreed that a gas tax suspension would provide meaningful financial relief to Ohio residents, with 2 economists uncertain and 6 economists agreeing. Bob Gitter of Ohio Wesleyan University explained, “If you buy a tank of gas every week you would save $6. Over a three month period that would be about $80. Low-income people could use a break but $80 would not, in my view, by meaningful financial relief.” Among those who agreed, economists expressed that while a gas tax suspension may provide financial relief, the policy would likely hurt Ohio’s economy in the long-run.

15 of 19 economists disagreed that the long-term economic benefits of a three-month gas tax suspension would outweigh the long-term economic costs of reduced state infrastructure funding. According to David Brasington of the University of Cincinnati, “Gas tax holidays usually end up causing deferred maintenance, which makes roads more expensive to repair than if normal maintenance had been done. It's like skipping a few dentist visits: it will save you some money upfront, but the resulting cavities will be more expensive to repair.” Of the remaining economists, 1 economist agreed and 3 were uncertain.

9 of 19 economists agreed that more of the benefits of a three-month gas tax suspension would accrue to consumers than fuel retailers. Curtis Reynolds of Kent State University expressed, “There is some research showing that gasoline taxes at the state level have high incidence to consumers (price increases almost one-for-one with the tax) so most of the benefits should accrue to customers in the form of lower prices.” Of the remaining economists, 5 were uncertain and 5 disagreed. These economists expressed uncertainty over whether fuel retailers would pass on the entire tax reduction onto consumers under a short-term gas tax suspension.

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.

Ohio has more money than expected. Should it save it or spend it?

Last week, it was reported that Ohio collected $1.2 billion in taxes more than it expected. More revenue means more flexibility for policymakers, more resources for public services, and potentially more room for tax relief. There will inevitably be debate about what the best way to use this extra revenue is, so I wanted to briefly talk about some of the potential options. 

Why do we save money?

This is a rhetorical question, but I want to take a second to spell it out formally because it highlights an important economic concept. Consumption smoothing is the idea that people have a tendency to consume fairly consistently, even though incomes may be inconsistent. 

A very simple example of this is that we need to eat every day, even though most people don’t get paid every day. Humans aren’t able to consume two weeks worth of calories at once and internally dole out that energy over time. We need to spread out our consumption over different periods than our incomes because even if we can predict both accurately, they almost always operate on different time frames. 

On a longer time horizon, saving for long-term expenses like retirement allows us to smooth consumption over the course of our lives. Most people earn the majority of their income during a few decades of working age adulthood, but they still need to consume before entering the workforce and after leaving it. Saving allows us to transfer purchasing power from periods where our income is high to periods where our income is low. In many ways, government budgeting works similarly. Tax revenues can fluctuate significantly from year to year depending on economic conditions, while the demand for public services tends to remain relatively stable.

That creates a challenge for states. During economic booms, tax revenues can increase unexpectedly. Similarly, revenues can fall during recessions which happen to coincide with a higher demand for services. If governments dramatically expand spending during good years and then suddenly cut programs during downturns, residents experience the same instability that individuals try to avoid in their own lives. Saving during prosperous years can help smooth that cycle out. 

So, what should Ohio do?

Impacts of spending this extra revenue

The value of the state spending money is entirely dependent on what they choose to spend it on. Some forms of spending create lasting economic benefits while others primarily provide short-term political rewards. Investments in infrastructure, education, or maintenance can potentially raise future productivity and improve quality of life for years after the money is spent. In those cases, spending today may generate returns that exceed the benefits of simply holding onto the cash.

There is also an argument that taxpayers themselves may be better positioned to decide how to use the money. If the surplus exists because taxes collected exceeded what was necessary to fund government services, then returning some of that money through tax relief could allow households and businesses to make spending and investment decisions based on their own priorities rather than political ones.

At the same time, temporary revenue spikes can create long-term obligations if states use them to fund permanent spending increases. Hiring large numbers of new employees or creating ongoing programs with one-time revenue sources can leave governments vulnerable when revenues eventually return to normal levels.

Impacts of saving this extra revenue

One of the biggest drawbacks of saving this revenue is that people have a preference for things today over things in the future. Saving is really just deferring our consumption to a later date, so it seems on its surface that it would be better to get the consumption out of the way and see if it can lead to long-term benefits. Using this lens, it becomes clear that saving this money would be a better decision if there were something in the future it could be spent on that would be even more valuable than anything we could spend it on today.

The state could put this surplus into its rainy day fund which functions similarly to an emergency savings account for households. Recessions, natural disasters, or unexpected budget shortfalls are inevitable at some point, even if their timing is uncertain. Building reserves during strong economic periods gives policymakers flexibility when conditions worsen. It can also reduce the need for abrupt tax increases or spending cuts during downturns, both of which can intensify economic pain.

According to research by Pew, Ohio’s rainy day fund could currently finance the state’s obligations for 47.4 days, putting it basically right at the national average. This extra cushion has become more important as a result of unreliable funding at the federal level. The pendulum swings between administrations make it much more difficult for states to predict how much money they might be able to expect for certain programs. Big changes to SNAP and Medicaid are two prominent examples that state governments might want to have extra reserves to help cover.

Ultimately, the best use of a surplus depends less on whether the money is spent or saved and more on whether policymakers understand the tradeoffs involved. A surplus is not free money. It represents resources that can either improve current consumption or provide stability and flexibility in the future. The difficult part is deciding which of those goals matters more today.

Who is moving out of California?

A few months ago, I wrote a blog post about who is moving in and out of Ohio. The results were mostly a story about age and income: older and younger Ohio residents move out of Ohio most frequently, presumably for either job opportunities or retirement. This kind of analysis is easy to do using data from the American Community Survey, which asks respondents about their current state of residence and their state of residence in the previous year. With this information, we can look at the demographics of who is moving in and out of different states.

I first looked at Ohio because that’s where a lot of our analysis is focused on at Scioto Analysis (and I live in Ohio myself). Another state that is equally interesting is California. It’s the most populated state in the country, and California is vastly different from Ohio. Additionally, between 2020 and 2024, the population in California decreased, just one of seven states that experienced a population decline across the United States.

Where are California residents moving?

According to American Community Survey data, around 315,000 households moved out of California between 2023 and 2024. Among these movers, the most common destinations were Texas, Arizona, Washington, Nevada, and New York.

The average age of residents moving out of California among the top ten destinations is between 31 and 47, with the youngest movers going to New York and the oldest moving to Nevada. The average age of California residents who remain in the state is about 40, meaning that the age of residents leaving California is not substantially different from those who are staying.

The average income of California residents who move out of the state is between $43,000 and $93,000 among the top ten state destinations. The states with the lowest average incomes for California emigrants are Oregon, Idaho, Arizona, and Nevada, with incomes ranging between $43,000 and $59,000. Three of these states border California, with Idaho only two states away. This trend might imply that lower earners who cannot keep up with the high cost-of-living in California choose to move out of the state to lower cost-of-living areas. Such residents who choose to relocate might be choosing nearby states to keep relocation costs low or stay relatively close to home.

On the other hand, the highest earners are moving to Florida, New York, and Washington, with average incomes ranging from $80,000 to $93,000. New York and Florida are both common states for workers to move to, and it’s likely that mid- or late-career professionals are moving for better job opportunities or retirement prospects. 

The trend of California emigrants moving to Washington, however, is more unique, and it is most likely explained by the significant tech hub located in both states. Over the past several years, it has become common for tech workers, especially from Los Angeles or the Bay Area, to move to Washington. This is primarily because tech workers from California can earn similar wages in Washington without paying state income taxes. California has the highest individual state income tax in the country, while Washington has zero state income taxes.

Who is moving into California?

According to the American Community Survey, around 184,000 households moved into California between 2023 and 2024. Among these movers, the most common destinations are Texas, New York, Arizona, and Washington.

The average age of movers to California among the top ten states is between 32 and 46, with the youngest movers coming from New York and the oldest movers coming from Oregon. Similar to migrants out of California, individuals moving into California do not have drastically different ages than pre-existing California residents.

The state with the lowest average income, by a wide margin, is Oregon, with an average income of about $43,000 for individuals moving from Oregon into California. This trend is most likely due to Oregon’s proximity to California. However, Oregon also had the highest average age of movers into California. This seems counterintuitive; I would expect most older individuals to be older professionals moving for job opportunities or retirement. However, the relatively low income of individuals from Oregon moving to California might imply that many of these movers are relocating for personal or family reasons.

Movers to California from Illinois and Virginia boast the highest average incomes, with both states exceeding $100,000. This trend is likely explained by these states’ proximity to major cities. Chicago’s location in Illinois and Washington D.C.’s proximity to Virginia could mean that movers out of these states are high-earners in lucrative industries. These movers could be looking for better job opportunities or similar job opportunities with better weather in California.

One distinct difference between individuals who move to a state like Ohio and individuals who move to California is income. Movers to California have incomes nearly double that of movers to Ohio. Regardless of who is moving to California, they must have incomes high enough to keep up with the high cost of living in the state.