Intern Perspective: What’s it like to do a cost-benefit analysis?

Over the course of the last several weeks, I’ve had the privilege of interning with Scioto Analysis to conduct a cost-benefit analysis on wildlife crossings, specific infrastructure built to reduce wildlife-vehicle collisions on major roadways. I’ve always been interested in policy analysis, and I was excited at the opportunity to use some of the skills and tools I’ve learned in my coursework in a real-world setting. 

Wildlife crossings, also known as eco-bridges, are overpasses, underpasses, or tunnels that connect two sides of the same habitat to each other. When roadways were originally built in the United States, a lot of habitats and animal migration patterns were disconnected. Wildlife crossings can help animals successfully migrate throughout their habitat once again, leading to a reduction in collisions and improvements in ecosystems. 

Currently, there are a lot of federal, state, and local initiatives to build more wildlife crossings across the United States. Wildlife crossings have proven to be incredibly effective, reducing collisions by up to 90% in certain areas. With that in mind, we weren’t sure if the price tag of building wildlife crossings was worth the benefits they brought. This was a big part of our motivation in writing the cost-benefit analysis about wildlife crossings.

Like many others, I’d heard the term “cost-benefit analysis” thrown around in a lot of contexts prior to this internship, but I didn’t know all of the intricacies that went into conducting a formal cost-benefit analysis until starting this project. 

In the wildlife crossings cost-benefit analysis, we had different goals for each week of the project, and the sequence of those goals was very intuitive. We started off at a high level, researching current literature and brainstorming ways that wildlife crossings could benefit or harm society. After establishing a base of research and ideas, we got into the weeds of the project where we estimated the magnitude of impacts of crossings, monetized those impacts, discounted impacts to present-day values, and performed sensitivity analysis to estimate the precision of our estimates. Finally, we moved into the drafting stage, where we were translated all of the analysis and research we had done into a digestible format.

Because the work was so clearly split into different weeks, it was clear to me when we had a week of work that I excelled at or struggled with. I found myself really thriving during the weeks of analysis and quantitative work. It was exciting to see the research we had performed come to life in estimates, charts, and Excel sheets. Even though this was the more low-level and analytical component of the project, I still had the opportunity to be creative, too. Adjusting our inputs and assumptions to analyze different case scenarios of wildlife crossings was a lot of fun, and it helped us to draw interesting conclusions and inferences about this interesting infrastructure innovation. 

On the other hand, the weeks that were more challenging for me were the high-level brainstorming and ideas stages. It was difficult to start from nothing and create my own roadmap for a cost-benefit analysis. However, I found that using existing literature on wildlife crossings and even other cost-benefit analyses across different subjects helped me find more ideas and stay on track. 

One of our biggest priorities during the wildlife crossings cost-benefit analysis was to find a way to quantify and monetize ecosystem services, which is the term in economics for benefits communities get from healthy ecosystems. Literature on monetizing ecosystem services is a lot more scarce than other impacts in the cost-benefit analysis world, and it was pretty unfamiliar territory for me. It was a learning experience to research and analyze the impacts that wildlife crossings had on ecosystems. Diving headfirst into wildlife crossings and ecosystem services was a great way to become an expert in a new topic fast, and conducting analysis on a topic I had never researched before was a great way to make sure I was being thorough and precise with my research and analysis, not making any jumps of logic or spurious assumptions.

My biggest takeaway from the project is the importance of taking the time to ensure a strong foundation of research, evidence, and analysis. I found I was able to be much more successful and efficient not by taking shortcuts but by staying organized. During the drafting stage of the cost-benefit analysis, I noticed that I made a big oversight in the calculation of some of our estimates. Fortunately, I prepared myself well by having all of my impacts and analysis organized and easily adjustable. What seemed like a big problem ended up being a ten minute fix.  

Ultimately, we found that just one wildlife crossing can yield $13.8 million in net benefits over the course of a 70 year lifespan. For every $1 in social costs created, another $10 would be created in social benefits. We were able to make a lot of valuable conclusions about the impacts of wildlife crossings on topics varying from ecosystem services to human life. 

Reaching these kinds of results and conclusions was a very rewarding experience, and I find myself excited to work on another cost-benefit analysis in the future. I enjoyed getting the opportunity to read and research different literature, reports, and news stories, and I especially liked performing analysis on information that I slowly built up over the course of several weeks. If you find yourself researching political, economic, or social phenomenons in your free time, or you enjoy conducting analysis, drawing interesting inferences, or seeing hard work come to fruition in a meaningful project, I would definitely recommend trying to get involved with the cost-benefit analysis world! It can be incredibly satisfying to see your work and analysis turn into something impactful.

Jacob Strang is a policy analysis intern at Scioto Analysis and a third-year economics and political science student at Ohio State University.

Scioto Analysis releases cost-benefit analysis of wildlife crossings

This morning, Scioto Analysis published a cost-benefit analysis about the impacts of building wildlife crossings in areas with high amounts of wildlife-vehicle collisions. Using conservative estimates, we estimate that a strategically-located wildlife crossing would provide $14 million in net benefits over the lifespan of the structure. This would come about by preventing 60 injuries, one fewer passenger fatality, and 1,200 fewer wildlife deaths.

Wildlife-vehicle collisions currently pose a large risk to humans, animals, and the environment. Each year, one to two million crashes occur between vehicles and wildlife in the United States, causing an estimated 200 passenger fatalities, 26,000 injuries, and more than $8 billion in economic costs including vehicle damage and medical expenses. However, wildlife crossings, which are bridges, tunnels, culverts, fencing, and other infrastructure that allows animals to safely cross roadways, have shown to significantly reduce wildlife-vehicle collisions. 

Across wildlife crossing projects in Washington and Colorado, wildlife crossings have been observed to decrease collisions by more than 90%. By targeting high collision areas over a 70-year lifespan, we estimate that one strategically-placed wildlife crossing can produce the following from reducing collisions:

• $7.5 million in prevented passenger fatalities

• $2.5 million lower medical expenses

• $1.6 million in prevented vehicle damage

• $1.5 million in animal lives saved

Beyond benefits from reduced collisions, we also estimate that one wildlife crossing would result in $2.1 million in improved ecosystem services. By enabling animals to cross roadways safely, wildlife crossings can improve habitat connectivity. Large mammals such as deer, elk, and moose can return to their regular migratory patterns that were interrupted by road construction, which improves biodiversity and ecosystem strength.

We conducted 10,000 simulations of wildlife crossings with different variables and costs to test our model. We find that the benefits outweigh the costs of building a wildlife crossing in a strategic location in 99.7% of instances. In the middle 90% of our simulations, net social benefits are between $11 million and $147 million. Wildlife crossings can be a valuable, and in many cases, a low-cost infrastructure project for state and local governments to carry out to improve the safety and environmental well-being of their communities.

Scioto Analysis also made a calculator available for policymakers and planners interested in estimating the effects of wildlife crossings in their communities. The calculator is free for download here.

What would design changes do to Ohio’s Child Tax Credit?

Earlier this week, my colleague Rob Moore testified before the Ohio House about our recent memo looking at the potential impacts of Ohio’s new Child Tax Credit proposal. During this testimony, he was asked two questions by state lawmakers. The first was what the impact of removing the program’s proposed phase-in would be. The second was what the impact of changing the amount of the credit would be. 

We love talking about active policy proposals and giving people more information to make smart decisions with, so we decided to take a stab at answering these two questions. 

What if we relaxed the phase-in?

Many policies have phase-in and phase-out schedules that can help soften any potential labor market impacts. This is especially important for the phase-out, because otherwise you create a benefits cliff where people can actually lower their total income by increasing their earnings in the labor market. 

On the phase-in side, the opposite effect occurs where the marginal impact of increasing earnings in the labor market is increased, since low-income workers can simultaneously increase their wages and their benefits.

The obvious difference between phase-in and phase-outs is that people on the phase-in part of the income distribution are in more need of money. In its current form, families whose total income is less than $22,500 would not earn the full amount of the credit. That is $1,000 above the federal poverty line for a family of two.

Our current model estimates that families in the first income quintile would receive an average benefit of just under $650 per qualifying child. Removing the phase-in would increase the average per-child benefit by over 50%. 

One limitation of our model is that we assume that benefits accrue to an “average” recipient, so these increased payments only have a linear impact on our estimated outcome. However, it is reasonable to expect that because this money is going to the people who need it the most, it would have a larger marginal impact. 

We expect that removing the phase-in would result in an additional $87 million going to families in the first income quintile. This is almost a 20% increase in the expected cost of the program, but it could lead to large returns for the state in the long run.

What happens if we change the benefit amount?

As mentioned above, our model assumes that most of the outcomes have a linear relationship with the size of the credit. That means if you increase the size of the credit by 50%, you would see a 50% increase in the costs and the benefits. 

The most notable exception to this is the added administrative costs associated with this program. It will take some overhead from the state to make sure that this credit actually ends up with the people who qualify for it. 

However, these administrative costs are orders of magnitude smaller than the other costs and benefits due to the low cost of managing tax benefits, so administrative costs are not significant when the credit amount changes. Technically there are economies of scale at play, and it would be less efficient to increase the proportion of fixed costs by offering a lower amount, but these are negligible. 

What would change with the credit prices is how effective this program is at improving outcomes for low- to middle-income families. As it currently stands, the Child Tax Credit would generate over $700 million worth of value for the state, and families would be able to pay for a couple of months worth of groceries with it. 

Decreasing the benefit amount to $500 would lead to $350 million of benefits compared to the status quo of no credit, and it would reduce the average benefit per child from $815 down to $407. On the other hand, increasing it to $1,500 would lead to over $1.4 billion in benefits for the state compared to the status quo, and it would increase the average per-child benefit to $1,223.

There are many options for policymakers interested in tweaking the state child tax credit. What our model tells us is this: relaxing the phase-in would deliver more benefits to low-income households, decreasing the benefit amount would decrease (but notably not eliminate) overall benefits, and increasing the benefit amount would increase overall benefits.

Scioto Analysis Principal Rob Moore Testifies at Ohio House Committee on Child Tax Credit

Yesterday, Scioto Analysis Principal Rob Moore testified before the Ohio House Ways and Means Committee on a new child tax credit for the state of Ohio.

According to the cost-benefit analyses conducted by Scioto Analysis, the proposed child tax credit is estimated to yield more than $700 million in long-term benefits.

Children raised in poverty frequently suffer from food and housing insecurity, have higher rates of physical and mental health issues, and have higher chances of working low-wage jobs. This child tax credit is estimated to lead to $500 million in higher future wages for the children of recipient families.

Additionally, the proposed child tax credit is projected to save the state $190 million in preventable crime. With more financial stability, families are less likely to incur child protection expenditures, a savings for Ohio to the tune of $120 million. The state is also predicted to save $65 million on future healthcare spending as moving people out of poverty can increase their health outcomes. 

Overall, each $1 spent on the earned income child tax credit is predicted to create $6.64 in social benefit. This short-term expenditure proposed in this budget is likely to have significant long-term benefits for the recipient children. 

These estimates are conservative, though 90% of 10,000 simulations saw a net positive social impact. On the higher end, the estimated benefit of this tax credit is roughly $2 billion. 

HB96, the budget bill that includes the tax credit, has been introduced to the Ohio House of Representatives and is currently within the Ohio House Finance Committee. The bill will need to pass through the House, be introduced and passed in the Senate, and signed by Governor DeWine before it can be enacted.

If the bill passes, eligible recipients should expect to receive their benefits after filing taxes in 2026. 

You can download Rob’s full testimony here and view the hearing at minute 36:27 here. 
You can read Scioto Analysis’ full memo results here.

Introducing Bennett Lovejoy

Hello! My name is Bennett Lovejoy, and I’m delighted to be the newest policy analyst at Scioto Analysis. 

My path to public policy was far from linear—it started in the cornfields of Iowa. While studying English at the University of Iowa, I joined the Iowa Public Opinion Lab (IPOL), where I analyzed public opinion data on issues ranging from agricultural policy to abortion access. This role introduced me to the powerful role data can have in public policy.

Intrigued by the use of data visualization for argumentation, I continued my work in research, particularly with nonprofit and other tax-exempt organizations. During my time at the Iowa Nonprofit Resource Center, I worked with a team of researchers to update the Iowa Nonprofit Principles and Practices. I gained an understanding of the mechanisms that governed nonprofits and how they operated within the public-private ecosystem. Too often, well-intentioned policies fell short because they were built without input from the communities they impacted. 

During the pandemic, I worked as an Americorps Legal Intern with Iowa Legal Aid’s Housing Department which helped stay evictions under the federal eviction moratorium. In the case of one client who was on dialysis, our work may have helped to save his life. Without stable housing, he was unlikely to make it to his weekly dialysis treatments. 

I was struck by how deeply a single policy could impact this member of my community. The experience opened my eyes to the different tools needed to create real change: policy, advocacy, and direct-service. 

Like many English majors, for a time I planned on attending law school—until the kind but unanimous advice from every attorney I knew convinced me otherwise. I was drawn to the strength of oral arguments, and the soundness of logic, but I couldn’t shake the feeling that the human stories central to each case were too often obscured by technicalities. 

While I considered my options, I worked full-time at a youth center in downtown Columbus as a member of their training and advocacy team. It was there, inside the unassuming brick-front building lined by yellow Gingko trees, that my worldview radically changed. I spent two years learning from people trying to navigate systems that weren’t designed for them. Their stories—of discouragement in education, of discrimination in workplaces, and of the precarious safety of online spaces—fundamentally changed how I saw public policy. 

After two years at the youth center, I came across Ohio State University’s Translational Data Analytics Master’s Program — and I was reminded of those first graphs I saw at IPOL. Could this be the program that could finally teach me the necessary skills to make good on my dream? 

Partially. 

I joined the program and was introduced to statistics and data manipulation. These pieces started to build my strange little toolkit. I could tell a story, I could empathize, and now I could quantify. 

But I realized that I could take it from there. 

I dropped out of the program and started doing the work. Graduate school taught me that there was nothing I couldn’t teach myself for free. 

So I started writing about poverty, uncovering the assumptions that underlie our social safety net. Immersed in the social security administration’s technical papers, I wrote about what I learned in a series of blog posts. I couldn’t help but notice the incongruity between what I read on paper and what I saw in my work.

I’ve worked with individuals and families recovering from intimate partner violence, facing imminent evictions, living off the land, struggling to receive disability benefits, and more. I’ve seen the way outdated systems can block access to vital resources and how this erodes a sense of economic empowerment and trust. I aim to work in that crucial gray space where personal narratives become data, and data reshapes policy.

A single datum represents an entire person—their trials, successes, fears, and dreams. We access the real power behind data when we study multiple perspectives to gain a well-rounded understanding of an issue. By embracing complexity instead of rounding out the edges of difficult problems, we can craft policies that are both evidence-based and deeply human. 

We have the ability to ensure everyone has safe and comfortable housing, supportive community, fresh foods, and clean water. I’m incredibly grateful to work with the team at Scioto Analysis to provide the most comprehensive, informed information possible to policymakers.

Why is housing getting so expensive?

It is no secret that housing costs are increasingly becoming a burden for people in metropolitan areas across the country.

The U.S. Department of Housing and Urban Development defines a household as being “housing cost burdened” if its earners spend more than 30% of their income on housing costs, whether that be rent, mortgage payments, property taxes, homeowners insurance, utilities, mobile home costs, and condominium association fees.

If we use the Columbus, Ohio Metropolitan Statistical Area as a case study, we see the percentage of people who are housing cost burdened rose by 4 points from 2019 to 2023, a 16% relative increase over that time period. This means housing costs have been eating up a growing proportion of household income over that time period.

Figure 1: Housing cost burdened rate growing in Columbus MSA from 2019 to 2023

The generally accepted explanation for why housing costs have been on the rise over the past few years is that the housing market is a competitive market subject to standard rules of supply and demand. The inability of supply to keep up with demand will drive costs of housing up as more dollars are chasing fewer homes.

This seems to be the case in Columbus. The metropolitan area added 60,000 homes from 2019 to 2023. Despite this growth in housing supply, demand outpaced it–63,000 more homes were occupied in the Columbus, Ohio Metropolitan Statistical Area in 2023 than in 2019. This means there were 3,000 fewer vacant homes in 2023 than in 2019. More dollars chasing fewer homes means higher prices.

To explain this trend, I have seen a couple theories bubble up about why supply is being limited in Columbus.

Theory 1: Speculative Investors

One theory goes like this: there are big-time investors who see a chance to make money in the housing market. They see housing prices increasing, so they buy up housing and sit on it, hoping to sell it for a higher price later and make a profit on their investment.

The problem with this theory is that we have not seen a significant increase in homes that are owned but unoccupied in the Columbus metropolitan area. According to American Community Survey data, the number of homes owned and unoccupied has actually gone down by about 270 homes (about a 9% decrease) from 2019 to 2023. So if anything, the prevalence of speculator investors seems to be a decreasing problem over time.

Figure 2: The number of homes owned and unoccupied in the Columbus, Ohio Metropolitan Statistical Area fell from 2019 to 2023

There are some caveats to this analysis. We do see a significant rebound in the number of homes owned but unoccupied in 2023, suggesting speculative activity could be increasing. That seems strange given the increase in interest rates, making housing investments less profitable than they were during the 2010 heyday of dirt cheap housing interest, but there may be something at play here.

The other caveat is that these overall numbers do not tell us about the geographic distribution of housing speculation. There could be concentrated speculation taking place that would not be captured by looking at overall numbers across the metropolitan area.

Whatever the trend is here, speculation is dangerous business for the speculator. Buying an asset and letting it sit with no one occupying it is taking a loss until you sell that property. Smart speculative activity should be creating more housing by providing rental housing at least. While this may allow big speculators to increase prices if they exercise enough market power, rental prices are also very “sticky” so there is a countervailing force against that trend. Speculators are not likely a primary cause of increases in housing prices in central Ohio.

Theory 2: Short-term rentals

Airbnb has become a bit of a bogeyman to many in the housing space. What a company like Airbnb represents is a market innovation that repurposes housing supply for another use: short-term stays. The theory is if Airbnb and other short-term rentals become more abundant, they crowd out the supply of housing for long-term purposes. Basically, if a home wasn’t being used as an Airbnb, it would be available for rent, thus increasing the supply. According to this theory, Airbnb is a threat to housing supply.

I’ll admit, I have been skeptical of this argument in the past. Full disclosure: Scioto Analysis may not exist if it weren’t for Airbnb. When I was starting this practice in 2019 and begging clients to give me a chance to do policy analysis for them, I was staying at friends’ houses or my parents’ and renting my home to visitors to our city. Airbnb was a significant source of our income that year. Some of the steps the city of Columbus took to clamp down on Airbnb activity that year made it a lot harder for me to have that as an income stream and was part of what caused me to move late that year. So I have a bit of a personal issue with burdensome regulation of the platform.

That being said, we have seen a significant uptick in homes used for seasonal, recreational, or occasional use (the category that encompasses homes used solely for Airbnb) from 2019 to 2023. There were about 2,600 more homes that are vacant due to these reasons in 2023 compared to 2019, a 65% increase.

Figure 3: About 2,600 more homes were vacant for seasonal, recreational, or occasional use in 2023 compared to 2019.

The big driver: supply shortages

When isolating homes that are for sale or for rent, we can see an even more dramatic decrease in available housing supply. The total number of houses for sale and for rent decreased by nearly 8,100 units from 2019 to 2023–a 36% total decrease.

Figure 4: The number of houses available for rent or to purchase in Columbus, Ohio Metropolitan Statistical Area has fallen by 36% over five years.

The most straightforward way to slow this problem of rising prices is to increase supply. That means reducing zoning and permitting barriers to construction of new homes. Over this time period, about 650 more homes built per year would have kept vacancies level. Lowering barriers to building could increase supply and ease the increase in prices.

There is another way, though: helping with the demand side. Housing subsidies like vouchers or cash transfers like child tax credits can be ways to provide relief on the demand side of the equation. The benefit of the latter is that cash transfers could also provide relief for households to pay for other necessities like food, transportation, and child care. Giving people money won’t solve every problem, but it will solve the problem of not having money.

Ohio economists optimistic about potential of cannabis, gambling, and tobacco taxes

In a survey released this morning by Scioto Analysis, 15 of 17 economists agreed that increasing taxes on cannabis, gambling, and tobacco will help reduce the negative externalities associated with those markets. In his most recent executive budget recommendations, Governor DeWine has expressed his support for increasing taxes on these goods in order to help fund a child tax credit.

Will Georgic from Ohio Wesleyan wrote in his comment: “The only consideration that keeps me from "strongly agreeing" with this statement is if the taxes are high enough to push this type of consumption into unregulated markets. We will certainly see a reduction in legal cannabis consumption, legal gambling, and tobacco consumption relative to what would be observed without the tax increase. The only question is whether consumers will break the law to avoid these taxes.”

The economists who disagreed with this statement questioned whether or not increasing taxes would actually change consumer behavior. As Kay Strong wrote: “These products have low price elasticity of demand. Raising their "price" will have a small effect on reducing demand but a large revenue return for government.”

Economists were more split on the distributional impacts of this policy. When asked about the statement “Increasing taxes on cannabis, gambling, and tobacco will disproportionately harm low-income households,” nine economsists agreed, six disagreed, and two were uncertain. 

One justification economists who disagreed offered was the potential health savings associated with reduced consumptions of these goods. Michael Jones from the University of Cincinnati wrote “Increasing taxes on cannabis and tobacco will reduce the overall usage of these products among low-income households. Individuals who eliminate tobacco use see significantly better health outcomes and quality of life.”

Economists who agreed with this statement pointed to the fact that sales taxes are regressive, and often disproportionately impact the budgets of low-income households.

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.

How would Ohio’s child tax credit impact typical Ohio families?

Since Governor DeWine made his budget recommendations earlier this month, we have been talking nonstop about the potential Child Tax Credit he proposed. Earlier this week, we even released a brief memo where we fed this proposal into our cost-benefit analysis model to see what some of the impacts might be.

From a broad economic perspective, this proposal seems like it could dramatically improve people's lives in Ohio. However, people’s day-to-day experiences do not always reflect the broader economic perspective. For some people, this credit won’t dramatically change their outcomes. 

So, we wanted to help answer the question about what this might actually look like for types of families. To do this, we created a few example families. Using wage data from the Bureau of Labor Statistics, we figured out how much these hypothetical families would receive from this proposal and what that would actually mean for their income. 

Columbus: Married couple with two children

Our first hypothetical family is married with two qualifying children living in the capital city. One parent works full time as a construction laborer, making the median hourly wage of $18.62 per hour. The other parent spends most of their time taking care of their two kids, but manages to find time to pick up a few shifts at the grocery store every week to help make ends meet.

Combined, they bring home a total of just under $46,000 per year. This puts their household income just under the 40th percentile for the city and below 150% of the federal poverty line, fitting into what many people would define as lower-income. 

Because they file their taxes jointly and their income is between $22,500 and $75,000, they are eligible to receive the full amount of the credit. That means they get $2,000 back on their taxes, which can be thought of as a 4% increase in their annual household income. 

Assuming this family approximately follows the Department of Agriculture’s moderate food cost plan, this would be about enough money to pay for two months of groceries.

Youngstown: Single parent with one child

Our second hypothetical family is a low-income single parent raising their child in Youngstown, located in the Appalachian region of the state. This parent is able to almost work full time as a fast food cook, but one day each week their sister is unable to provide childcare, so our parent has to stay home. 

Because they are unable to work full time, this parent makes just under $19,000 per year.  This means that under this proposal, they do not qualify for the full amount of the credit. Instead, when tax season rolls around they should expect to get a little over $800.

Even after this credit, this family is only at 93% of the poverty line. Like the married family from Columbus, this additional $800 represents about a 4% increase in this parent’s annual income. Additionally, if we assume this family follows a low food cost plan, this would also be about 2 months worth of groceries. 

Dayton: Single parent with two children

Our final hypothetical family is made up of a single parent who works full time as a licensed nurse and their two children. As a nurse, they make $28.04 per hour which adds up to a yearly income of a little over $58,000.

Because this person is filing by themselves, they fall above the threshold where the benefit starts to phase out. At their specific income, this parent would only qualify for about $560 per child. Because they have two qualifying children, their expected credit would be $1,124. 

This is not quite enough money to pay for two months' groceries assuming a moderate food cost plan, but it is a little more than what the fair market rent for a 2-bedroom apartment in the Dayton Metropolitan Statistical Area is according to the Department of Housing and Urban Development.

Marietta: Married family with three children

The final hypothetical family lives in Marietta on the West Virginia border. Both parents work, one as a restaurant server, the other in retail, and they have had three children born in the past five years. They both make the median hourly wage for their jobs, $13.41 and $12.75 respectively. They both took a little time off from work recently after their third child was born, but now they are both back to working full time.

This family's total income is a little over $54,000, just a hair under 150% of the federal poverty line. This means that they qualify for the full amount of the credit, and will get an additional $3,000 to supplement their income.

It is often the case with living expenses that there are economies of scale. In other words, while going from two to three people in a household is a 50% increase in the number of people, the resources needed to maintain the same standard of living needs to increase by less than 50%.

Because of this, our hypothetical Marietta family experiences some of the biggest returns from this credit. Using a moderate food cost plan, they could afford groceries for almost three months with this credit.

Akron, single parent with one child:

This hypothetical parent works as a preschool teacher in Akron. Fortunately, their child gets to attend the preschool with them, so they can still work full time. They earn the median hourly wage of $14.20 per hour.

This means that our hypothetical parent earns $29,536, putting them a little under 150% of the federal poverty line. Because of their income, this qualifies for the full amount of the Child Tax Credit.

The extra $1,000 is roughly the same as receiving a 4% raise. Assuming this family follows a low food cost plan, this is almost enough money to cover three months of groceries. Alternatively, this is a little more than the fair market rent for a 2-bedroom apartment in their neighborhood just West of downtown

Akron, married couple with one child: 

Our second hypothetical family from Akron is married and just had their first child earlier this month. One parent is staying home with the newborn full time, while the other is working two jobs to try and make ends meet.

Their first job is part time at a coffee shop, where they work 20 hours a week at the median salary of $11.47 per hour. This parent just got their hours cut at their second job where they work at a fast food restaurant. Now they only get 10 hours per week at $11.09 per hour.

Added together, this family’s household income is $17,696, only 66% of the federal poverty line. Because they don’t earn at least $22,500, they only qualify for about $760 from the Child Tax Credit. Still, this represents about a 4% increase in their income. 

Assuming this family is following a low food cost plan, this amount will cover a little over one month’s worth of groceries. Alternatively, this is about one month’s worth of fair market rent for their one-bedroom apartment Northwest of downtown.

—

I hope these examples help conceptualize what the impact of this child tax credit might feel like for some of the people who receive it. It’s certainly not a silver bullet by any means, but it does put a lot of money in the pockets of Ohioans.

Scioto Analysis estimates proposed child tax credit would grow Ohio’s economy by $740 million

This morning, Scioto Analysis released a memo presenting an analysis of the impact of the child tax credit in Ohio’s proposed FY 2026-2027 budget. As currently constructed, we estimate the Child Tax Credit would provide nearly $450 million in direct benefits to Ohio families. These direct benefits primarily accrue in low- and middle-income families, but the indirect impacts this may have on Ohio’s economy would be beneficial to nonrecipients as well. 

“There is a robust body of research that shows how investments made in early childhood are beneficial both to the families who receive them and the broader community” said Scioto Analysis Principal Rob Moore. “Children who grow up with access to more resources have an easier time in the short term, which often translates to better wage, health, and criminal justice system involvement outcomes later in life.”

Children who have more resources in formative years tend to have higher wages, better health, and less criminal justice system involvement. Using previous literature on the relationship between tax credits and outcomes for households, we estimate the current proposal will lead to the following annual impacts:

• $740 million in net benefits for the Ohio economy

• $450 million in direct credits to qualifying Ohio families.

• $500 million in higher future earnings for children receiving the credit

• $190 million in reduced costs associated with future crime 

The report draws from research conducted by the Columbia University Center on Poverty and Social Policy, indicating that financial support for families with children leads to better health outcomes, higher educational attainment, and lower crime rates. This study was awarded the 2023 award for “Best Original Article” in the Journal of Benefit-Cost Analysis due to its methodological rigor and policy relevance.

While raising revenue to fund this credit will exact a cost on the economy due to foregone tax revenue, the potential long-term gains for Ohio’s economy and communities are substantial. After running 10,000 simulations of the proposed child tax credit under different circumstances, we found the benefits of the child tax credit outweigh its costs in 90% of those simulations. 

“Investing in Ohio children is a good bet for growing Ohio’s economy in the long run,” said Scioto Analysis Principal Rob Moore.

Where does the term “Caucasian” come from?

Still in the first 100 days of the second Trump presidency, we have seen a lot of changes, particularly around data availability and collection of data related to diversity.

As my colleague Michael Harnett wrote about before, lack of collection of data on a population can severely hamper our ability to understand the population in question. Many are worried that changes in the federal administration will lead to reduced collection of demographic data that will then make it harder for researchers and analysts to understand demographic trends among the public.

The Biden Administration had even improved some demographic collection practice for the Census Bureau, greenlighting “Middle Eastern or North African” and “Latino” as options for future censuses. This was a change a decade in the making.

But just as the federal government was beginning to modernize demographic categories—finally recognizing groups like Middle Eastern or North African as a distinct racial identity and using a phrase like “Latino” that is more commonly used among people from that racial group—current decisions by the new federal administration could potentially unravel the existing demographic collection infrastructure. Instead of refining how surveyors collect data to better understand the demographics of the U.S. population, they are now facing a chance that demographic data could be obscured or never collected in the first place.

This threat isn’t just about which categories appear on a form. It’s about how researchers, analysts, policymakers, and the public think about identity, and whether the categories we do reflect the reality of identity in the United States today or reinforce other incorrect classifications.

One example of a term that lingers in public discourse is “Caucasian.” In 1977, the Carter Administration’s Office of Management and Budget Directive 15 established “white” as the official racial designation throughout the federal government, endorsing it over its rival “Caucasian.” It is a sign of our times that this original directive is not available for public viewing as of February 2025, though it was publicly available as recently as December 2024 according to the Wayback Machine.

Even though the federal government has endorsed the use of “white” over “Caucasian” for nearly half a century now, the word still appears in legal documents, medical research, and everyday conversation. Where did this phrase come from and why is it still so persistent today despite falling out of official favor five decades ago?

The term “Caucasian” used to refer to how we refer to people who are ethnically “white” dates back to the late 18th century, when a German historical school with a special focus on race was trying out different classifications of human races. An especially popular classification was to split humanity into three major races: “Caucasian”, “Mongoloid,” and “Negroid”, roughly corresponding to what our Census Bureau would term “White,” “Asian or Pacific Islander,” and “Black.”

But why “Caucasian?” The term comes from an association with the Caucasus Mountains, a range that defines the northern borders of modern-day Georgia and Azerbaijan with Russia between the Black and Caspian seas. What does this area have to do with white people?

This goes back to a historical theory that is very different from the present-day consensus. The prevalent view among European scholars in the 19th century was that the human species began in the Caucasus Mountains. This was based partially on Caucasus being the purported landing place for Noah’s Ark.

“Caucasian” was first applied to a broad swath of humanity by German historian Christoph Meiners in his 1785 book The Outline of History of Mankind. In this book, Meiners grouped Europeans, Middle Easterners, North Africans, and Indians into one racial group, arguing its main characteristic was beauty driven by virtue. In his words, “the more intelligent and noble people are by nature, the more adaptable, sensitive, delicate, and soft is their body.”

Ten years later, German Anthropologist Johann Friedrich Blumenbach popularized the term in his studies of human craniology. He categorized human “varieties” into five categories: Caucasian, Mongolian, Malayan, Ethiopian, and American. Despite creating these categories, he acknowledged the capriciousness of this approach to categorization, saying “All national differences in the form and colour of the human body...run so insensibly, by so many shades and transitions one into the other, that it is impossible to separate them by any but very arbitrary limits.”

While many of Blumenbach’s views would not be seen as correct today, he was considered an anti-racist in his time, opposed slavery as a practice when it was still in legally sanctioned in nearly every country across the world, and argued against common claims of his day that certain races were imbued with an “inherent savagery.”

Despite these views, Blumenbach made the phrase “Caucasian” popular and much of his work was later co-opted by scientific racists who told a very different story about mankind than Blumenbach did.

Meanwhile, in the new United States of America, the terminology of “white” was a big part of the early law of the country. The designation was one of the original categories of self-reporting in the decennial census of the fledgling nation. Naturalization was limited in the Naturalization Act of 1790 to “free white persons.”

The term “Caucasian” became a lynchpin for citizenship debate in the 1920s, with immigrants from Japan and India losing Supreme Court cases where they tried to argue they should be considered “caucasian” under current immigration law. The U.S. draft for World War II often designated enlistees as “Caucasian” and legal briefs and arguments around the case of Brown v. Board of Education in the 1960s often used the phrase. Even the Equal Employment Opportunity Commission, formed by the 1964 Civil Rights Act, used the phrase “Caucasian” in demographic reporting.

It was not until the Carter Administration designated “white” as the preferred term in 1977 that the federal government officially moved away from the phrase “Caucasian.” But we still see it in use today, especially in medical research contexts.

I actually remember first hearing the phrase, and I remember the allure of it. Today’s understanding of race is so messy: a social phenomenon roughly associated with phenotypic characteristics. There was a certain allure to this new word I heard: this feeling of scientific precision that race doesn’t give us. I don’t think I am alone in feeling this draw.

But it’s a falsehood. “Caucasian” is a phrase that misplaces current social phenomena in an 18th Century German story about a sparsely-populated mountain range in central Asia. Correct understanding of social phenomena means understanding the true roots of these categories. So just use “white.” It’s easier, everyone knows what you’re talking about, and you’re not rooting your language in a veneer of scientific accuracy that is built on a shaky pseudoscientific foundation.