Is Ohio’s school voucher experiment panning out?

On Nov. 5, voters in three states — Colorado, Kentucky, and Nebraska — rejected state private school voucher programs.

Private school vouchers are a program where a family can take public dollars to spend on private school tuition. Arguments in favor of a system like this have been made since the 1950s, when famous libertarian economist Milton Friedman argued that allowing school vouchers would improve educational outcomes for children by increasing parental choice, promoting competition among schools, and reducing government inefficiencies.

Friedman’s arguments came under fire in the decades hence, not the least of which in the public policy classic Exit, Voice, and Loyalty, where German Economist Albert Hirschman argues that Friedman overlooks an important mechanism available to parents in struggling schools: their ability to voice their concerns through the democratic process.

Policymakers in Ohio have largely embraced private school vouchers. Last year, the Ohio General Assembly expanded eligibility for private school vouchers to all families in the state, regardless of income. According to reporting from the Ohio Newsroom, this led to a quadrupling of use of private school vouchers, while enrollment in private schools has been steady.

From Fiscal Year 2023 to Fiscal Year 2024, voucher use increased by 60,000 students while enrollment in private schools increased by only 3,000 students. This means 95% of new voucher use in 2024 was by students who were already enrolled in private schools or would have enrolled in private schools anyways.

This means the policy change in Ohio to expand eligibility was largely a windfall to families that were already planning on sending children to private schools. And because of the policy change that was made, it was likely a regressive windfall that accrued mainly to well-off households. The most recent estimate of the size of total spending on private school vouchers in 2024 is about $970 million. For context, that is more than the state spent on the entire Department of Children and Youth (which runs state child welfare, child care, and early education programs) and the Department of Natural Resources combined.

What are we getting for these investments? Last year, when this expansion was being considered, my firm asked 23 Ohio economists what expanding Ohio’s school choice voucher program would do for the state economy. They were tepid about the potential change. Only six thought the expansion would increase test scores, only three thought it would decrease poverty, while 11 believed it would lower the quality of public schools.

Certainly there are arguments for limited use of school vouchers. I think especially of innovative education models or schools that are focused on technical education, schools with workplace tie-ins and trade schools. Having innovative options for families that want a niche offering could help them learn and could create new learning opportunities for students that did not exist before.

That being said, it seems like the experiment of school vouchers in Ohio may have swung a bit far. Are we getting any value from the hundreds of millions of dollars we are pouring into private schools with our taxes? If we are, I would like to see evidence of it. Because we certainly wring our hands much more about programs that cost much less.

This commentary first appeared in the Ohio Capital Journal.

What is the Evidence Act?

When I joined Scioto Analysis in 2022, I didn’t have much experience with public policy. I had a background in economics from my undergrad degree and I had a masters degree in statistics. Both of those gave me the foundational skills I needed to do policy work, but only over the past two years have I really started engaging with public policy issues. 

So, while many of you may already know about this, it wasn’t until last month’s Association for Public Policy Analysis and Management conference that I learned about the existence of the Foundations for Evidence Based Policymaking Act (the Evidence Act for short). I learned about this when I attended a panel discussion talking about a Government Accountability Office report summarizing the current state of the Evidence Act.

The conversation was pretty hard for me to follow since I didn’t have the background of what the Evidence Act was. Since then, I’ve tried to learn more about the Evidence Act and what it might mean for federal agencies. Here are some of the most important takeaways:

Evidence-Building and Evaluation

The act emphasizes the importance of systematic evidence building and evaluation plans within federal agencies. It mandates that these plans be part of the agency's strategic plans and annual performance plans. Specifically, it requires covered agencies to develop systematic plans for identifying and addressing policy questions using evidence-based methods. This includes identifying policy-relevant questions, outlining methodologies, data sources, and steps for evidence development, and consulting with stakeholders including the public, other agencies, and researchers.

Open Data and Transparency

The act promotes open government data by requiring federal agencies to make data publicly available by default. This focus on transparency aims to enhance public trust and enable greater collaboration. Title II, known as the Open Government Data Act, emphasizes that all federal agencies must make data open by default and actively engage the public in using and collaborating with open data. The Evidence Act also requires agencies to maintain comprehensive data inventories and publish them on a centralized Federal Data Catalogue, ensuring data is accessible while protecting personally identifiable information.

Data Accessibility and Security

Balancing data accessibility for statistical purposes with the protection of confidential information is a core focus of the Evidence Act. Title III, the Confidential Information Protection and Statistical Efficiency Act of 2018, addresses this by expanding secure access to data for statistical purposes while protecting confidentiality. Agencies have to focus on expanding secure access to data, including categorizing data based on sensitivity, conducting risk assessments, and using de-identification techniques to protect privacy.

Strengthening Agency Capacity

The Evidence Act seeks to bolster federal agencies' capacity for evidence-based policymaking by establishing key roles and promoting data expertise. Agencies are required to designate senior Evaluation Officers responsible for implementing evidence-building plans and assessing the agency's evaluation capacity. Additionally, the Evidence Act mandates the appointment of statistical officials within agencies to advise on statistical matters and serve on the Interagency Council on Statistical Policy.

I hope going forward that these types of changes are not isolated to the federal level. State and local governments should look at some of this guidance and see if they have the capacity to take on some of these mandates voluntarily.

It can be expensive, but these are the types of investments that can generate major returns on investment. Avoiding inefficient rules and regulations while promoting equity, effectiveness, and efficiency is hard to do without first looking at the evidence.

New Report Details Poverty and Economic Insecurity in Franklin County

On Wednesday, the RISE Together Innovation Institute and Scioto Analysis released a groundbreaking report, Poverty and Economic Insecurity in Franklin County, shedding light on the systemic challenges facing residents of one of Ohio's most populous counties. The report reveals that while Franklin County is a hub of economic prosperity, nearly 40% of households still struggle to afford basic necessities like food, housing, and healthcare.

The study outlines the drivers of economic instability, including rising costs, low wages, and the enduring effects of systemic racism. It also provides recommendations for policymakers, community leaders, and organizations to promote economic mobility and security for all residents.

"This report lays out the reality: poverty is not inevitable; it's the result of policy choices,” said Rob Moore, Principal for Scioto Analysis, “by adopting solutions that address poverty and its causes, we can ensure a future where every Franklin County resident has the opportunity to succeed."

Key findings include:

• High Cost of Living: The median home price in Franklin County has jumped 54% since 2017, while rent for a two-bedroom apartment has increased by 47%.

• Economic Disparities: Black residents are more than twice as likely as white residents to live in poverty.

• Child Poverty: Nearly 1 in 5 children in the county live below the Federal Poverty Level, amounting to an estimated 59,000 children in the county living in poverty.

The report also highlights successful programs, such as the Earned Income Tax Credit (EITC) and Supplemental Nutrition Assistance Program (SNAP), while calling for initiatives to boost childcare access, housing stability, and job quality.

For more information or to download the full report, visit the RISE Together Innovation Institute website.

What's the matter with ALICE?

A few years ago, I first heard about a research project conducted by United Way affiliates called ALICE.

ALICE stands for Asset-Limited, Income-Constrained, and Employed. The goal of the ALICE project is to estimate the number of people in every community who fit into this category. Why does this category matter? Because people with low levels of assets, limited income, and employment still often cannot afford what we call “necessities.”

How do we know if someone lives in an ALICE household? Well, it’s very similar to how we determine if someone is below the federal poverty level: researchers set a threshold based on some conception of what qualifies as sufficient income. If a household has income below that threshold, they are an ALICE household. If their income is above that threshold, they are not.

The way ALICE researchers set the threshold is by adding up the local cost of a range of goods. This list comprises housing, child care, food, transportation, health care, technology, taxes, and other miscellaneous goods. This differs from the methodology of the federal poverty threshold, which uses the cost of food times three as its threshold. It also departs from the supplementary poverty threshold methodology, more popular among poverty researchers, which uses average spending amounts to anchor its threshold.

According to the 2022 ALICE report, 54 million of the United States’s 129 million households fell below the ALICE threshold. This means more than three times as many households are ALICE households than there are households in poverty.

Some have claimed that the ALICE framework should be a replacement for the federal poverty level. This means that policymakers should shift their focus from the 12% of households struggling the most to the 42% of households who do not have income that meets the ALICE threshold.

Policymakers have been making this shift for decades now. At last month’s Association for Public Policy Analysis and Management Fall Research Conference, a number of sessions focused on the changing nature of the federal social safety net. One of the biggest takeaways I took from these sessions is (1) that the social safety net is larger now than it was a few decades ago, and (2) that the social safety net has shifted from supporting people at 0-50% of the federal poverty line to supporting people at 100-150% of the federal poverty line.

A big reason for this is welfare reform in the 1990s. In 1993, a household claiming Assistance for Families with Dependent Children (AFDC, colloquially known as “welfare”) could bring their total income over the federal poverty level with wage income at 0% of the federal poverty level.

Let the implications of that sink in: before welfare reform, the United States had guaranteed income for households with children.

That changed with welfare reform. With Assistance for Families with Dependent Children transformed into Temporary Assistance for Needy Families (TANF), the program was block granted and significantly reduced. What took its place was the Earned Income Tax Credit, a program that primarily gave cash to households with children with working parents.

The growth in the social safety net since then has been documented by Dr. Katherine Michelmore of the University of Michigan, who recently won the David N. Kershaw Award for this work. The growth in support for families has been on the backs of expansions of the Earned Income Tax Credit and Child Tax Credit in the decades since welfare reform.

This has led to a change in overall support for families. Overall, families are receiving more support today than they did in the early 90s. But this support has shifted from families at the lowest level of the income distribution to families who are at or a little bit above the federal poverty level.

This is because Assistance for Families with Dependent Children did not require a household to work to receive support. If a household had children, they needed support, so Assistance for Families with Dependent Children gave it to them. Today’s social safety net does not work that way. Both the Earned Income Tax Credit and the Child Tax Credit require wage income to claim. This means our current social safety net currently supports low-income people who are near or above the federal poverty level, not people who are in deep poverty.

Despite the current structure of our social safety net, the fixation in safety net policy is still on people who are low-income but not below the federal poverty level. Policymakers’ attention to the “benefits cliff” is a great example of this. In Ohio, eligibility for food assistance was recently expanded significantly. Who was it expanded to? You guessed it–people at 130-200% of the federal poverty level. This likely means hundreds of millions of dollars of new annual support for low-income families, but none for families below the federal poverty level.

Helping low-income people should be no crime from a social welfare perspective. What worries me is who is left out of that conversation. ALICE purports to widen the scope of our safety net by demonstrating how 42% of families are struggling rather than only the 12% of families who are below the federal poverty level. But it sets its ALICE threshold for a family of four in Franklin County, Ohio with two parents, a preschool-age child, and an infant at over $91,000. The federal poverty level for that same family is about $28,000. Can we seriously say that a family of four making $90,000 should be lumped into the same category as a family of four making $27,000? Or that a policy that helps the family making $90,000 is just as socially beneficial as a policy that helps the family making $27,000?

This isn’t to say that ALICE is a bad framework: it helps us learn something, especially about the cost of goods that many families need to survive. But it also lumps a wide swath of the population together that are dealing with varying problems and can make us lose sight of those who are struggling most.

Great 20th Century Political Philosopher John Rawls put forth his famous “difference principle” in his 1971 classic A Theory of Justice. He argued that social and economic inequalities “are to be to the greatest benefit of the least-advantaged members of society.”

Helping people who are struggling is good. Supporting people who are dealing with problems like benefits cliffs is socially beneficial, but losing sight of people who are struggling the most is a huge problem in our current safety net. Our fixation on the population of low-income people above the federal poverty line has come at the expense of people in deep poverty. The longer we continue to follow this path, the more people who are the least-well-off will continue to suffer.

Does paid family leave pay off for society?

Earlier this week, I wrote about the differences between academics and policy analysts and how policy analysis needs to be more comprehensive than research to be relevant to policymaking decisions. I used the example of the papers I heard presented at the APPAM Fall Research Conference about paid family leave and how those were narrowly focused on labor market outcomes. I may have left that session wanting to hear more about the whole range of benefits and costs associated with paid family leave, but there are people out there who are doing the kind of work I find most interesting. 

In February of this year, Prenatal-to-3 Policy Impact Center released a cost-benefit analysis of a paid family leave proposal in Pennsylvania. They looked at all the different ways this program could impact people’s wellbeing, and condensed it into an incredibly readable report. Here are some of the key takeaways they found: 

Positive Net Benefits

The Pennsylvania proposal would offer a 20-week paid family leave program funded by a 1% payroll premium split between employers and employees. These researchers found that it would generate an estimated $379 million in annual net benefits. Over a lifetime, the net benefits per birth year reach $1.7 billion. When compared to the costs of this program, this represents a benefit-cost ratio of $18 of benefits for every $1 of costs. 

Positive Economic Impact

The program is projected to increase maternal employment, boost earnings for families with infants, and reduce infant care costs. Additionally, businesses would benefit from decreased job turnover, increased productivity, and more female leadership. This reinforces the idea that paid family leave is beneficial not just for families but for the broader economy as well.

Public Health Benefits

The analysis links paid leave to improved food security, reduced infant hospitalizations and mortality, increased breastfeeding rates, better postpartum health for mothers, and a decrease in severe child maltreatment. These public health benefits underscore the policy's potential to promote public health and reduce long-term healthcare costs.

Reduced Societal Costs

The program is expected to lead to significant cost savings in healthcare, special education, criminal justice, and subsidized child care. By supporting families during critical periods, paid family leave can help mitigate long-term societal costs. We’ve seen in other contexts how this kind of short-term assistance can help bridge the gap for some people and enable them to achieve better long-term economic stability.

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Any policy that can return hundreds of million dollars in annual net benefits is worth considering for policymakers. This is not to say that paid family leave is a silver bullet that will solve all our problems by any means, but it does seem to make things better for a lot of people. 

Some people may still be negatively affected by a paid family leave program. I’ve written before about how different firms feel the costs and benefits of these programs differently. Policymakers who are excited about the topline benefit number should also be thinking about how they can use these social dividends to help support the people and businesses that might be individually worse off as a result of this policy. 

This type of comprehensive policy analysis provides the most useful information to policymakers. Doing this type of analysis, however,  would be impossible without the contributions of academic research. Analysts need academics to create this rich literature so we can understand all the impacts a policy might have, and policymakers need analysts to wade through all that research and pull out these comprehensive insights to guide their decisionmaking process.

Good policy analysis reveals the big picture

Last month, I attended the Association of Public Policy Analysis and Management’s annual conference. This was a meeting of people from seemingly every academic discipline whose research was at all related to public policy. More than many other conferences, APPAM is a great opportunity to learn about new developments in some of the fields that impact our work as policy analysts.

The first session I attended was focused on the impacts of paid family leave programs. Paid family leave is the policy that allows new parents to take paid time off work when they give birth to or adopt a child. Usually people who take paid leave do not earn 100% of their paycheck while on leave, but it still puts more money into the pockets of people to take time off work to bond with their children.

All of the presenters asked slightly different questions about paid leave policies, but there was one conclusion that was consistent across all three papers: paid family leave policies do not do a good job of keeping new mothers attached to the labor force. 

Viewed through the narrow lens of labor economics, it seemed as though paid family leave was an ineffective policy. These papers touched on some ancillary impacts, but these presentations were largely focused on the labor market. 

This is one of the most important differences between academic research and policy analysis: academics can focus on getting the most accurate results possible on a single aspect of a policy, while analysts need to synthesise all the information they can find about a particular policy to look at it holistically. 

In the case of paid family leave, we as analysts should not just be interested in the labor market impacts, but in every way this could change peoples lives. I would have loved to hear more discussion about the poverty impacts of paid family leave, or more about how paid family leave impacted the firms that had to respond to this new policy.

Another important consideration is how this might lead to intergenerational effects. With any policy that impacts children, especially newborns, we need to consider not just what the effects are today but what sort of investment we are making in the long run. A robust paid family leave policy could potentially contribute to better developmental outcomes for children, improved health for both parents and children, higher wages in the future, and other positive impacts that come from a small boost delivered early on. 

Additionally, policy analysts need to keep our research focused on policies that could reasonably exist. For example, it isn’t feasible for policy makers to implement a paid family leave program that would pay individuals 300% of their salary for a full year. It’s obviously not sustainable, so policy analysts shouldn’t waste time exploring it. It might be an interesting question for an academic to approach though, just to see if there are any interesting insights we can glean from such an outlandish policy idea.

Policy analysis is inherently interdisciplinary. To see the whole picture on certain issues, analysts need to account for information from multiple perspectives. I enjoyed all the papers I heard about paid family leave at this conference and I feel much better prepared to account for the labor market impacts of paid family leave. Still, my analyst brain was left wanting a much broader discussion about all of the potential impacts.

Ohio economists: mass deportations could undermine economy

In a survey released this morning by Scioto Analysis, 14 of 20 Ohio economists surveyed thought that mass deportations of undocumented immigrants would significantly reduce Ohio’s gross domestic product. President-Elect Donald Trump has stated that he intends to carry out mass deportations of undocumented immigrants across the country soon after taking office. If this policy is carried out, local police will be responsible for detaining and holding immigrants in conjunction with state and federal law enforcement agencies. 

Rachel Wilson from Wittenberg University agreed that Ohio’s economy would shrink, writing “Not only will it be reduced by immigrants production but because of their missing demand. Immigrants do not come and work in a vacuum. They spend the money they make creating additional demand for goods and [services]. They often have a high marginal propensity to consume from each dollar they earn.”

Of the remaining six economists, three were uncertain about the impact on gross state product and three disagreed deportations would significantly reduce gross state product.

“The share of undocumented workers is small in Ohio,” said Bob Gitter of Ohio Wesleyan University, “Deportations would not have a significant impact.”

In addition to significantly reducing Ohio’s economy, 15 out of 20 economists disagreed that mass deportations would ease the burden on Ohio’s safety net. Some policymakers argue that undocumented immigrants take advantage of publicly funded social services while not paying taxes to support those services, but economists in the survey disagreed.

Kathryn Wilson from Kent State says “Estimates are that undocumented immigrants pay more than $250 million in taxes within the state of Ohio per year. There would be some reduction in education expenses within Ohio if there were mass deportation, but most social safety net programs are not available to undocumented immigrants. I expect that the loss in tax revenue would more than offset the reduction in costs within Ohio.”

The Ohio Economic Experts Panel is a panel of over 40 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.

3 recommendations to improve federal policy analysis

Earlier this month, the Office of Management and Budget released its draft report to Congress on the many cost-benefit analyses the federal government performed during fiscal year 2023. The report covers a lot of topics, touching on major regulations federal agencies analyzed over the past year and their impacts at the federal and state levels. 

Later on in the report, there is a chapter that includes recommendations to reform the regulatory process. Below are the three recommendations that we at Scioto Analysis are paying the most attention to in the coming years. 

A Government-Wide Approach to Improve Benefit-Cost Analysis

Currently, government agencies do much of their analytical work independently. However, as our methods for identifying impacts improve, we are seeing that regulations in one agency might impact outcomes in another. Because of this, the authors of this report suggest that federal regulators begin to take a more collaborative approach to analyzing federal regulators. By working across agencies, analysts can overcome common analytical challenges, share valuable insights, and enhance the overall quality of regulatory decisions.

Quantifying the Social Cost of Greenhouse Gases Using the Best Available Evidence

In the realm of environmental policy, understanding the true cost of greenhouse gas emissions is crucial. The report advises that agencies should rely on the most up-to-date scientific evidence when estimating the social cost of greenhouse gas emissions. This recommendation is timely, considering the rapid advancements in climate science and the evolving understanding of how these emissions impact the planet.

The 2023 memorandum from the Interagency Working Group on Social Cost of Greenhouse Gases serves as a guiding document, encouraging agencies to exercise professional judgment in selecting the most appropriate estimates for their analyses. By grounding regulatory decisions in the best available evidence, we can ensure that policies are both scientifically sound and effective in mitigating the adverse effects of climate change.

We’ve seen with the first Trump administration and the Biden administration how quickly measures like the social cost of carbon can change. We might expect another dramatic change in this value come January. Hopefully agencies can cut through the politics and rely on the best evidence with these measures.

Distributional Analysis Under Incomplete Information

Public policy often involves navigating tradeoffs, particularly when it comes to balancing distributional equity with maximizing net benefits. At Scioto Analysis, we talk about the equity-efficiency tradeoff every time we do an analysis. In such cases, agencies are encouraged to consider statutory provisions, public input, and their own historical practices in making these decisions.

Consistency is key. Maintaining a standardized approach in how distributional concerns are addressed can lead to fairer outcomes. Additionally, the report suggests exploring compensatory measures, such as grants or spending programs, to mitigate undesirable distributional effects. This approach not only promotes equity but also ensures that the benefits of regulation are broadly shared across society.

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These recommendations reflect some of the most important challenges facing policy analysts going forward. Understanding how to collaborate with experts from other fields, using the best available evidence, and developing solid empirical methods to perform distributional analysis are key topics that are going to drive this discipline forward. These conversations are happening at the federal level, and hopefully we can apply these same techniques to state and local policies as well.

Stop using acronyms!

How do you feel when you are reading a story you find very interesting, only to hit a snag? It’s a word you don’t know. No, it’s not a new word. It’s a jumble of letters that certainly means something to someone, but not to you. You only know one thing about this jumble of letters: they stand for a phrase that would mean a lot more if it was just written in full.

Yes, you have come across an acronym: the scourge of comprehensible writing everywhere. In a place where an author could have given you a phrase, instead she gave you a vocabulary exercise.

Some technical writing takes the acronym to the extreme. I have seen government or think tank reports that include an entire glossary at the beginning that can be referenced by readers who are not fluent in their particular dialect of Bureaucratese. The author then wipes her hands and continues to write with any range of esoteric acronyms, knowing that the reader can “simply” scroll to the top of the document and locate the abbreviation she doesn’t understand before scrolling back down to the portion of the text she is reading.

The best practice of acronymizing within a report is to write out the full word when using it for the first time, then adding a parenthetical that includes the acronym you intend to use throughout the rest of the document. Theoretically, this allows the reader to internalize the acronym and then recognize that usage throughout the rest of the report.

There are two problems with this approach.

First, if you have to include a parenthetical to explain what an acronym refers to, you are effectively introducing a reader to jargon. If a reader does not fully process the acronym the first time, she will find herself confused as she continues to read and will either have to turn back to locate the first use of the acronym to see what it is referring to or will plow through reading, hoping the acronym isn’t important. If multiple acronyms are used, this gets even worse.

Second, if a reader only reads part of a report, book, or essay, she will not have the context of the first use of the acronym. This is likely to happen in lots of technical writing. If someone is consuming a study, report, or any other type of nonfiction, she often does not have the time to read every word of it and is best served by picking out the parts of it that are most useful to her. If she skimmed over the first establishment of an acronym, later uses of it will just lead to more confusion.

But we do like acronyms. Some of them have infiltrated our language to the point that they have become words in their own right. Some acronyms are commonly used or actually are more clear than the full phrase. For instance, wouldn’t you rather read “DNA” than “deoxyribonucleic acid?”

So what is a writer to do? At Scioto Analysis, we conduct technical writing for a general audience and we write in a field, public policy, that looooves acronyms. So how do we handle these stumbling blocks of clear writing? Below are some tips for guidance for using acronyms in a way that is productive and facilitates the primary goal of writing: clear communication.

1. Know your audience

The very most important consideration when using an acronym is audience. If you are writing primarily for an audience that is very familiar with how the tax code impacts people in poverty, you might be good to use the phrase “EITC” in place of “earned income tax credit.” If you think a minority of writers might not be familiar with the phrase, it is probably better to spell it out.

Remember that writing is not the same exercise as speaking. Sure, you may say “EITC” exclusively when you are referring to the earned income tax credit, which is okay when people you are speaking to can clarify the meaning of the term. When writing, the reader cannot ask for that type of clarification from you. So unless you want that reader to do extra work to understand the meaning of what you are writing, spelling out the phrase will make it more clear than using an acronym.

2. Understand the acronym

Another important consideration is which specific acronym you want to use. An acronym like “CDC” or “EPA” are commonly used in public policy and many people outside of the public policy world understand what they mean. Using them is probably less frustrating for a reader than an acronym like the “OECA,” which stands for the Office of Enforcement and Compliance Assurance, or even worse, the “AOC” when it stands for “Architect of the Capitol,” a federal agency in charge of stewarding public landmarks on Capitol Hill.

Probably the worst offense of all is coining your own acronym. Forcing a reader to learn new vocabulary that did not exist until they read your paper is a capital offense for an analyst who is trying to make concepts more understandable for the readers, not less. 

3. Avoid the acronym whenever possible

It is okay to repeat phrases. I repeat: it is okay to repeat phrases. If you say “Department of Transportation" throughout your report instead of “DOT,” even many people who are employed by the Department are not likely to be offended by hearing its name. Most people will not think twice hearing a phrase that is important to your report, analysis, or research over and over again: that is what they expect when they read something on that topic. What people will be offended by is if they have to read a phrase that they do not understand and flip (or more commonly “scroll”) from page to page to figure out what you are talking about.

People in government love to hate acronyms. People outside of government love to hate acronyms in government. Give these people one less thing to hate. Stop using acronyms.

What would “mass deportations” do to Ohio’s economy?

The dust has settled on the 2024 presidential election and we now know that Donald Trump will once again be President of the United States.

Trump has promised many things for his second term in office: deregulation, tax cuts, an end to Russia’s war with Ukraine, tariffs on all goods from other countries. The step he could take that could have the most immediate impact on both human rights and Ohio’s economy, however, would be on immigration.

Trump has promised to conduct mass deportations of unauthorized migrants, rounding up immigrants in workplaces, schools, homes, and places of worship to send them back to their countries of origin. Local law enforcement will be a key player in determining how “mass deportations” will be carried out in the state of Ohio. 

Municipal police departments, county sheriffs offices, and the state highway patrol will have to decide how much to defer their work from policing violent crimes and property crimes to carry out federal immigration policy. What decisions local law enforcement make around prioritization could have a significant impact on Ohio’s economy.

Earlier this week, Ohio Capital Journal Reporter Marty Schladen wrote about the important role immigrants play in Ohio’s economy. Immigrants in Ohio are taxpayers, consumers, business owners, doctors, software developers, professors, cooks, health care workers, and college students.

An analysis done by researchers at the American Enterprise Institute, Brookings Institution, and Niskanen Center released before the election shines some light on what the new administration’s immigration policy could do to immigration. Trump’s immigration plan is estimated to reduce both authorized and unauthorized immigration, increase removals from the interior, increase adjudication of current cases leading to more removals, and encourage others to leave on their own. 

These researchers estimate this would mean as many as 740,000 fewer immigrants in the United States in the first year of Trump’s presidency. Weighted for Ohio’s foreign-born population as reported in the American Community Survey, that could mean as many as 9,700 fewer immigrants in Ohio in about a year. 

The AEI/Brookings/Niskanen study reports this massive reduction in the number of immigrants in the United States would cost the country 0.1 to 0.4 percentage points in GDP in 2025. In Ohio, weighted for Ohio’s foreign-born population, that would mean somewhere between $330 million and $1.3 billion in lost gross state product. 

For comparison, the Ohio Department of Development estimates 21 counties in Ohio have a gross domestic product of $1.3 billion or less. So if these policies are carried out as planned, Ohio could lose a small county’s worth of its economy in fewer consumers, business owners, and workers. On a per capita basis, this means a cost of $28 to $110 per person in the state. So you can consider this a head tax of $28 to $110 per person to pay for having fewer immigrants living in this state.

Just because something shrinks the economy doesn’t mean it is bad. We might decide it appropriate to institute policies that trade off economic growth for reductions in poverty and inequality, improvements in environmental quality, or more time for people to spend with their children or elderly parents. But what exactly are we buying for this immigration crackdown? After all the national conversation on this topic, I still don’t have an answer to this question.

This commentary first appeared in the Ohio Capital Journal.