Minnesota Child Tax Credit generates over $930 million in annual net benefits

A cost-benefit analysis released today by Scioto Analysis finds that Minnesota's Child Tax Credit is projected to generate over $930 million in present benefits to the state. Minnesota’s Child Tax Credit is one of the most generous state Child Tax Credits in the country, providing $1,750 in benefits to low-income families with qualifying children. 

“Child tax credits do so much more than just giving resources to families that need them, they are significant investments in the future state economy” said policy analyst Michael Hartnett. “the decreased pressure on the social safety net going forward more than pays for the upfront costs of this program.”

Key Findings:

• Increased Future Earnings: The largest benefit, estimated at $670 million, comes from projected increases in the future earnings of children receiving the tax credit. The added resources from the tax credit support better educational opportunities and health outcomes, leading to higher lifetime earnings.

• Crime Reduction: The tax credit is expected to prevent over $460 million in future criminal justice and victim costs by reducing the likelihood of future crime. This benefit accrues to the whole state, meaning that even those who don’t receive the tax transfer still experience long term gains.

• Excess tax burden: Funding the program to make up lost tax revenue costs the state economy around $180 million.

A Monte Carlo simulation of all the possible outcomes finds that about 86% of the time, the child tax credit will have benefits that outweigh costs. The range of likely outcomes is heavily right-skewed, meaning that there are more extreme positive outcomes than there are extreme negative outcomes. High-end estimates of the value of the child tax credit reach $2 billion in annual net benefits.

Ohio public school spending is economic development

This year’s budget debates have focused heavily on the future of education funding. 

Early in the budget cycle, Ohio House Speaker Matt Huffman came out strong for K-12 budget cuts, saying the current education budget in Ohio is “unsustainable.” The governor’s budget has come under some fire from education advocates for shifting resources away from public schools.

Some in the public have debated whether education funding creates benefits for students or whether it is just wasted by administrative spending in school districts. A couple of years ago, a working paper tackling this question was circulating through academic circles.

Finally published last year in the American Economic Journal, this study was a comprehensive meta-analysis of impacts of school spending on student outcomes. They found when school spending increases by $1,000 per pupil for four years, test scores increase by 0.0316 standard deviations and college matriculation increases by 2.8 percentage points.

Okay, so school spending does help students test better and go to college more often. But is this worth it? This is a question my firm asked in 2023. We conducted a cost-benefit analysis to see whether this increased spending would pay off for the state as a whole.

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The general model looks like this: if a student is at a school with more resources, they will have higher test scores and a higher chance to go to college. Both of these will lead to higher future wages for that student. These wages and the reduced social spending on that student in the future due to higher wages will both increase the size of the state economy. If schools have less resources, students will test worse, go to college less, make less over their lifetime, and the public will spend more money supporting them with social spending down the road.

To analyze what Ohio would look like under different scenarios, we compared Ohio’s current spending (about $14,000 per pupil at the time) to a decrease in per-pupil spending that would put Ohio at about Indiana’s per-pupil spending (about $10,000 per pupil). We also compared an increase to per-pupil spending at Pennsylvania levels (about $16,000 per pupil).

Overall, we found that increased school spending paid off for the state. Conservative simulations of increases in per-pupil spending to Pennsylvania levels put wage and social savings benefits of spending increases outweighing the costs of spending by $23 billion. On the other hand, reducing spending to Indiana levels led to decreases in wages and social spending outweighing savings from the program by at least $30 billion.

Overall, the analysis is clear: from a long-term perspective, increases in school spending within standard limits of what we have seen in the United States lead to economic benefits that outweigh costs. Decreases in school spending lead to economic development costs that outweigh savings benefits. We studied these over a range of discount rates, too, finding that even very short-sighted policymakers should not favor spending cuts if they care at all about future economic development.

To put it short, school spending is an investment. And according to the evidence we have available, it is an investment that will pay off for Ohio.

This commentary first appeared in the Ohio Capital Journal.

Rising housing prices make renters hit the bottle

Last month, the National Bureau of Economic Research published a new working paper examining the relationship between housing expense inflation and household alcohol and tobacco purchases. The researchers used county-level changes in housing regulations to identify how exposed certain groups of renters and homeowners were to the high rates of housing price inflation during the pandemic.

They found that in counties with more stringent housing regulations and greater exposure to housing inflation, renters increased their beer consumption by approximately 15.2% relative to homeowners with lax regulations and less housing inflation when comparing 2022 to 2019. This increase is particularly noteworthy because one might expect that households facing increased housing costs would reduce spending on non-essential items like alcohol due to constrained disposable income.

Instead, the researchers suggest that these increases in beer consumption may be due to the use of alcohol as a coping mechanism for the added financial stress caused by rising housing expenses. They also observed that the increase in spending was primarily on low-cost beer, rather than on wine or liquor. This observation further supports the idea that renters might be turning to more affordable options as a way to manage their financial burdens.

To conduct this research, the authors used a difference-in-difference-in-difference approach. This method compared changes in alcohol and tobacco purchases between renters and homeowners, before and after the housing expense surge, across counties with varying levels of housing regulation. The housing regulation index was constructed using data from the Wharton Residential Land Use Regulation Index.

This approach lets the researchers compare between renters and homeowners across counties. They choose to separate renters from homeowners because renters more regularly enter into new housing contracts, and homeowners are relatively insulated from the increase in housing inflation. They still experienced inflation in the prices of other goods, but people with fixed rate mortgages before the pandemic would have experienced much lower housing cost increases.

This paper offers new insights in a few important ways. First, it looks at how high inflation after the COVID-19 pandemic has impacted people's health habits, especially alcohol and tobacco use. Second, it explores the two-way relationship between financial struggles and substance use by examining how local housing regulations affect housing costs. Finally, it sheds light on how local housing regulations impact the housing market and the financial decisions of households.

The research also highlights the importance of considering the broader public health implications of housing policies. By demonstrating a link between housing affordability, financial stress, and alcohol consumption, the study suggests that addressing housing market supply constraints could not only alleviate high housing costs but also improve household financial security and influence health-related behaviors. The negative impacts of alcohol consumption are well established, and this research underscores the need for policymakers to consider the unintended consequences of housing policies on public health.

This study shows how seemingly disparate aspects of public policy can be intertwined. Housing policy is health policy. These researchers have shown how economic pressures can influence health-related behaviors and how housing policies can be a tool to address public health concerns. This study serves as a reminder of the far-reaching effects of economic stress and the need for policies that support both affordability and well-being.

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.