What is the cost of commuting?

Last week, I wrote about a quick analysis I did trying to see if housing prices were related to commute times in cities across the country. Go check out that blog for the full details, but in short I found that yes, higher housing prices are correlated with longer commute times. 

Commutes are a fact for most working people in the United States. According to the American Time Use Survey, Americans who have to travel for work spend an average of 45 minutes per day commuting. This means that over the course of a year, the average adult who has to travel for work spends almost 275 hours on their commute, the hour equivalent of nearly seven full work weeks. 

How people commute

According to census data, the majority of workers commute by themselves in a car. 

Table 1: The majority of workers drive themselves to work

The dominance of driving has important implications for both individuals and policymakers. Because such a large share of workers rely on personal vehicles, changes in fuel prices, road congestion, or highway infrastructure affect millions of people every day. At the same time, the relatively small share of workers using public transportation or active transportation reflects the reality that these options simply are not available in many parts of the country. 

Cost of commuting

When considering how much commuting costs, there are a few things that we can measure. First are the direct costs associated with travel: gasoline, vehicle maintenance, parking, and pollution. These are largely straightforward to calculate and represent the costs that people see most clearly in their monthly budgets. 

A less straightforward cost is the opportunity cost associated with commuting. If people didn’t spend 45 minutes per day in their cars, what else could they accomplish? 

Economists think about time as a scarce resource. Every minute spent commuting is a minute that cannot be spent working, exercising, spending time with family, or enjoying leisure activities. The value of that lost time differs from person to person and understanding these tradeoffs can help us design better policies. 

Differences in commuting types

This is where the tradeoffs between commute styles become apparent. A bike or walk commute may be longer than driving, but it produces less pollution and serves as a form of exercise. Perhaps for some people the additional commute time is made up entirely because they no longer have to spend time going to the gym after work hours. 

There also exists the potential for people to do something productive with alternative forms of commuting. People who take buses or trains to work have the opportunity to read, answer emails, study, or simply relax during their trip in a way that someone driving cannot. On the other hand, public transportation often involves fixed schedules, transfers, and less flexibility than driving. This could lead to longer overall commute times, which has been shown to have some serious negative outcomes.

Policy implications

Different commute types have a lot of tradeoffs, and which one is best is certainly an individual question. Whether people like being able to get things done on a train, they like combining exercise with their commute, or they value the flexibility that comes from driving a car, there is no one size fits all solution. 

Because of this, in a world without budget constraints policymakers should try and make it so every possible form of transportation is available to everyone. Under budget constraints however, priorities need to be established. 

Those priorities should ultimately reflect the needs of the communities they serve. Dense urban areas may receive the greatest benefit from investments in public transportation, while rural communities often depend on reliable roads and highways. Likewise, improving sidewalks, bike lanes, and pedestrian infrastructure may make sense in neighborhoods where short trips are common. Rather than assuming one mode of transportation is inherently better than another, policymakers should focus on providing people with practical options that fit their circumstances.

What does policy analysis cost the federal government?

Public policy analysis is crucial for developing good policy because, among other things, it helps policymakers allocate public funds efficiently. This is another way of saying that public policy analysis helps us prevent money from being wasted. Given the vast scale of public spending, even modest improvements in how funds are allocated can justify the cost of policy analysis.

This does not, however, mean that public policy analysis is without cost. Federal, state, and local governments across the United States spend a significant amount of money on public policy analysis every year with the goal of making more effective and more cost-effective public policy.

What does the federal government spend on public policy analysis?

The agency in the federal government’s policy analysis ecosystem with  the largest budget is the United States Census Bureau, which has a current budget of about $1.5 billion. The Trump Administration has requested an increase in this budget to about $2 billion in anticipation of the lead up to the 2030 Census. The Census Bureau is not technically a policy analysis agency: it is focused mainly on data collection. The data it collects, however, is key to policy analysis across the federal government, and also for policy analysis at the state level, local level, and in private-sector organizations. Although its data has applications that go far beyond public policy analysis,  it could be chalked up as a key input for public policy analysis in the United States.

The second-largest of these analysis-focused agencies is actually a quite specialized office: the Millennium Challenge Corporation, which has a current budget of $830 million. The Millennium Challenge Corporation is a free-standing agency formed through bipartisan legislation in 2004 to support sustainable economic growth and poverty reduction in developing countries. I first encountered the Millenium Challenge Corporation at a 2019 meeting of the Society for Benefit-Cost Analysis, where I learned that many analysts at this agency were conducting benefit-cost analyses of investments in foreign aid. Most of the funds for this agency go to programs, though, not policy analysis.

The largest of the pure policy analysis agencies in the federal government is the Government Accountability Office, which currently has a budget of about $810 million. The Government Accountability Office is the supreme audit institution of the United States federal government, in charge of ensuring federal funds are spent efficiently and effectively, investigating allegations of illegal use of public funds, reporting how well government policies are meeting their objectives, performing policy analyses, and a range of other charges.

The largest policy analysis institution within a federal agency  is the Department of Education’s Institute of Education Sciences, which is currently funded to the tune of $770 million. It is the statistics, research, and evaluation arm of the Department of Education. The Trump Administration has proposed reducing its funding to about $260 million as part of its broader effort to cut the Department of Education's budget.

Another key federal statistical agency is the Bureau of Labor Statistics, which is the principal fact-finding agency for the Federal Government in the broad field of labor economics and statistics. It is currently housed under the Department of Labor and has a budget of $640 million. The Bureau of Labor Statistics is closer to the U.S. Census Bureau than it is to the Government Accountability Office, in that it is more focused on collecting statistics that are used by other agencies for policy analysis than it is on analyzing policy itself.

Beyond these largest analysis agencies, several others have annual budgets between $120-$140 million. The Congressional Research Service ($140 million), Energy Information Administration ($140 million), Office of Management and Budget ($130 million), HUD Policy Development and Research Office ($120 million), and Bureau of Economic Analysis ($120 million) each have budgets in this range. Other key agencies like the Economic Research Service ($91 million), Congressional Budget Office ($75 million), Department of Justice Research, Evaluation and Statistics Account ($55 million), Joint Committee on Taxation ($14 million), and Council of Economic Advisers ($5 million) all have budgets under $100 million.

All accounted for, these appropriations add up to a total of about $5.5 billion.

Compare this to the total of all federal spending. The Congressional Budget Office estimates the federal government spent about $7.4 trillion in Fiscal Year 2026. That means total policy analysis expenditures made up less than $1 for every $1,000 spent by the federal government that year.

What is the right amount of spending on policy analysis?

In Results for America’s book Moneyball for Government, a bipartisan group of analysts recommend each department in the federal government set aside 1% of department funds for evaluation of public policies. If we use this for a benchmark, then the federal government’s spending on evaluation in the agencies listed above only make up about 7% of recommended spending on evaluation.

We can quibble about the definitions of evaluation versus policy analysis, data infrastructure versus core policy analysis work, and specialized policy analysis versus backbone policy analysis that supports the federal government as a whole. But no matter what way we slice it, there seems to be space for more policy analysis in the federal government, and it would be likely to pay for itself in savings to the federal government budget, especially if it were deployed in a thoughtful way.

This is not even to touch on state and local government. As underinvested as the federal government is in policy analysis, state and local governments often have even less institutional capacity and conduct less policy analysis overall. I would like to dig into policy analysis done by state and local  governments and the private sector, but this blog post is already getting a bit long, so I will leave those questions for another time. For now, suffice to say that policy analysis is not especially costly in the context of overall public sector spending.

Original Analysis: Scioto Analysis finds New York City’s Congestion Pricing Produced $2 billion in Net Benefits in 2025

Today, Scioto Analysis released a new cost-benefit analysis of New York City’s Congestion Relief Zone during its first full year of operation. 

The analysis estimates that the program produced approximately $3.3 billion in social benefits and $1.3 billion in social costs in 2025. This amounts to approximately $2 billion in net benefits, with the program generating $2.60 in benefits for every $1 in costs.

New York City implemented the Congestion Relief Zone in January 2025, becoming the first city in the United States to charge most vehicles entering its central business district. During its first year, traffic entering the zone declined by 11%, or approximately 73,000 vehicles per day.

To evaluate whether the program benefited New York City overall, analysts estimate impacts on mobility, road safety, environment and health, public expenditures, and businesses. 

• Reduced air pollution produced the largest estimated benefit. Lower particulate matter 2.5 exposure inside the zone generated approximately $810 million in health benefits, while reduced carbon dioxide emissions produced an additional $9.4 million.

• Drivers experienced substantial time savings both inside and outside the zone. Drivers traveling within the zone received an estimated $560 million in time savings benefits, while drivers using nearby roads outside the zone received approximately $740 million. 

• Reduced traffic congestion also saved drivers approximately 3.3 million gallons of fuel, producing $10 million in benefits. Faster bus travel produced an additional $7.6 million in benefits for riders.

• The analysis’ primary estimate finds that changes in crash patterns inside the zone produced approximately $250 million in safety benefits. However, alternative methods find that crashes may have instead imposed between $20 million and $100 million in costs, making roadway safety one of the analysis’ largest sources of uncertainty.

• The largest cost was establishing and operating the tolling system, estimated at approximately $410 million. Other costs included discouraged vehicle trips, increased crashes in nearby spillover areas, environmental mitigation spending, and a potential increase in particulate matter 2.5 exposure in the South Bronx, which may be offset in future years by the benefits produced by environmental mitigation spending.

The analysis also tests how its findings change under alternative assumptions. A simulation of 10,000 possible outcomes produced a median net benefit of approximately $1.6 billion, with 90% of outcomes falling between approximately $1 billion and $2.1 billion in net benefits. Benefits exceeded costs in every simulation. 

Though the Congestion Relief Zone produced a positive net benefit overall, its benefits are not experienced equally across the population. Drivers who continued entering the zone benefited from faster travel but paid the zone fee, while drivers who stopped making trips into the zone lost access to travel they valued. Residents inside the zone may have benefited from cleaner air, while nearby communities may have experienced increased crashes or pollution. The positive overall result therefore does not mean that every person or neighborhood was made better off by the policy.

The full report can be accessed here.

What could Ohio spend its budget surplus on?

Ohio’s fiscal year 2026 just ended on June 30th. The state ended the fiscal year with a $1.75 billion budget surplus, meaning that they collected $1.75 billion more in taxes in fiscal year 2026 than they planned to spend. According to State Representative David Thomas, about 75% of the budget surplus is already allocated, with about $350 million going to seniors in homestead relief, and about $320 million going to an extended sales tax holiday next year.

Earlier this year, my colleague Michael asked the question: should Ohio spend this extra money, or should they stash it away in a rainy day fund? Spending a budget surplus can be good– people generally prefer to get things now over later. However, saving extra funds is a safe option, as the timing of recessions, natural disasters, or unexpected budget shortfalls is always uncertain. Today, I want to ask the question: what could Ohio spend its $1.75 billion surplus on?

Ending Homelessness

In 2024, my colleague Rob wrote a blog post about what it would cost to end homelessness in the United States. He used data on the number of homeless people across the country and average monthly rent prices to make a simple estimate: how much would it cost to provide one year of housing to all homeless people in the country?

We can apply the same logic to Ohio. According to Zillow rental data, the average monthly rent for all property types in Ohio is $1,350. As of 2025, HUD’s most recent Annual Homelessness Assessment Report, there are 12,196 homeless people in Ohio. 

If we assume that each homeless person is supplied with a living space with a $1,350 monthly rent, and the state paid for 12 months of rent for each person, then ending homelessness for one year in Ohio would cost $198 million, about 11% of the budget surplus from this past fiscal year. If monthly rent prices continue to rise according to Zillow’s year-over-year change, Ohio could pay for eight years of housing with its $1.75 billion budget surplus.

Universal Pre-K

Last year, Scioto Analysis released a cost-benefit analysis about universal prekindergarten in Ohio. In the report, we estimated that each year, universal prekindergarten would cost taxpayers about $8,308 per participant enrolled. Adjusting for inflation, we estimate that universal prekindergarten would cost taxpayers $8,865 per participant in 2026.

Under a high-enrollment scenario, universal prekindergarten in Ohio would result in an additional 29,000 children in prekindergarten per year. This means it would cost about $257 million to place 29,000 Ohio children in universal prekindergarten for one year, about 15% of Ohio’s $1.75 billion budget surplus. If Ohio were to use its whole surplus on universal prekindergarten, the state could fund it for six years.

Child Tax Credit

In March 2025, Governor DeWine proposed a child tax credit that would give Ohio families up to $1,000 per child under the age of seven each year. The credit would be calculated as 5% of a taxpayer’s income up to the maximum $1,000. For combined filers, families would be eligible for the child tax credit with a combined income of $94,000 or less. For single filers, income must not exceed $69,000. The child tax credit was included in one version of House Bill 96, the biennial state budget, but was ultimately rejected by the General Assembly.

A child tax credit typically helps families pay for childcare, healthcare, groceries, diapers, rent or other necessary costs. Scioto Analysis has conducted analysis on child tax credit policies for Ohio and Minnesota previously. Child tax credit policies are associated with higher future earnings for children, lower healthcare expenses, and less crime. Governor DeWine’s proposal was expected to cost the state $450 million, about 25% of Ohio’s budget surplus. This means that with the budget surplus, the state of Ohio could pay for nearly four years of Governor DeWine’s proposal.

Universal Free School Meals

Last year, Scioto Analysis released a cost-benefit analysis about universal free school meals in Ohio. We found that universal free school meals in Ohio would result in net social benefits of $520 million. The economic benefits include food cost savings for families, wages for cafeteria staff, increased lifetime earnings for students, reduced healthcare costs, and time saved by families on meal preparation.

Under a proposed Senate Bill from 2025, the state of Ohio estimated that universal free school meals would cost about $300 million per year. This means that the state of Ohio could pay for universal free school meals with about 17% of their budget surplus from this past fiscal year.

What does this mean for Ohio’s budget surplus?

If Ohio were to provide housing to its entire homeless population, implement universal prekindergarten, create a refundable child tax credit, and provide universal free school meals, the state would still have about $545 million leftover from its budget surplus.

The best use of a surplus is ultimately up to policymakers, but these four policies might be a good place to start.

Quick analysis: Do high housing prices cause longer commutes?

This is going to be a quick analysis where I attempt to answer a question in what basically looks like the first pass I’d do for a larger research project. The goal is not to conclusively answer the question I pose, but rather to show how you could go start exploring a question like this and get at least some evidence and see if this is worth exploring further.

Research question

Hypothesis: Higher housing costs are associated with longer commute times. I came across this idea in a newsletter I got this week that was looking at commute times in cities around the world. The author included the following sentence which got me thinking: “Transportation researchers often say a city's average commute tells you almost as much about its housing market as its roads.” 

The logic behind this hypothesis is that when housing costs are high, people have fewer options for housing in their price range. Less housing choices may lead to more people living farther away from where they work, which leads to longer commute times.

Methods

I’m going to use American Community Survey data for every metropolitan and micropolitan statistical area in the United States. I’ll collect their average commute time and their average housing cost and I will check the scatter plots to see if there are any obvious trends. Then, we can do a simple linear regression to see if those trends are statistically significant.

Because of the COVID-19 pandemic, I’ll do the regression on two samples: the 5-year ACS estimates from 2024 and from 2019. I suspect that if there is a trend, it is likely to be much stronger before the pandemic made working remotely significantly more common. 

Results

Both scatter plots appear to have a slight positive trend, though the effect size is likely quite small. Both linear regressions confirm this first glance and find that there is a statistically significant positive correlation between housing costs and commute times. The fact that both of these results are positive and statistically significant does provide some early evidence that our hypothesis is true. The regression using the most recent 2024 data suggests that commute times increase by an average of one minute for every $500 of increased monthly housing costs. Note that the median housing cost in the 2024 data was about $1,000 per month and the max was about $3,000, so there is not a ton of variation on average.

Figure 1: 2024 housing and commute times by metropolitan area

Looking at the 2019 data, we find that commute times increase by an average of one minute for every $250 of increased monthly housing costs. It is hard to tell because the two plots aren’t overlayed but the slope of that two lines is essentially twice as steep implies basically double the impact. This supports the prior hypothesis that the COVID-19 pandemic weakened the correlation between housing costs and commute times. An alternative hypothesis as to why the effect is weaker today than in 2019: rising housing prices nationwide has made the baseline cost for housing higher, so marginal increases in housing prices have less of an effect on commutes than they used to in a 2019 world of lower baseline housing costs. 

Figure 2: 2019 housing and commute times by metropolitan area

Limitations

This is not a causal analysis. This is not enough evidence to claim that higher housing costs cause people to have fewer options when it comes to where they live and therefore live farther away from their work. Notably we aren’t controlling for any other factors that might influence how long commutes are or how high housing costs are.

Another limitation is that I’m just looking at average housing costs and commute times across metropolitan areas. I don’t believe this is necessarily the best way to capture the relationship that I’m interested in, and maybe it would be better to look at individual level data. 

Preliminary conclusions and next steps

Based on these regressions, I would say there is definitely evidence to support this hypothesis and it would be worth exploring further in a more concrete way. A good next step would be to see if there are any historic examples of natural experiments that could be exploited to better identify a causal effect.

What will happen if we adopt permanent daylight savings time?

Last week, the U.S. House passed legislation that would make daylight savings time permanent across the country. If it passes in the Senate, then it would mean the annual tradition for most of the country of switching clocks back and forth twice a year would be officially over.

Back in 2023, we studied what the impacts of daylight savings time were in Ohio. The biggest takeaway from that study was that for Ohio, either permanent daylight savings or standard time was preferable to switching back and forth, but which of those you preferred depended on what the impacts were. Permanent daylight time led to lower crime thanks to more light in the evening, permanent standard time led to energy savings since the sun lines up better with human energy use.

While the average economic outcome might be the same for permanent daylight savings and permanent standard time, a closer look at the issue will tell you that not everyone has the “average” opinion, and a lot of people have reason to prefer one option over the other.

Why some people might oppose permanent daylight savings time

Although the evidence points to the fact that changing clocks twice a year causes a lot of problems to society as a whole, there are people who think that changing to permanent daylight savings time is a negative outcome. For instance, the American Academy of Sleep Medicine strictly prefers permanent standard time and its earlier sunrise. Their position is that across the country, standard time better lines up with our circadian rhythm which in turn leads to many health benefits. AASM has gone as far as to “condemn” this new legislation. 

Another major concern about switching to permanent daylight savings time is that in Northern states, the sun rising so late during the winter might present other challenges. In Minnesota for example, the sun won’t rise until 9:00 am during some times of the year. This is a difficult proposition for children and teachers who will start school in the pitch dark if school times stay the same.

The extent of many of these concerns depends on geography. States farther north experience shorter winter days, making the effects of permanent daylight savings time more noticeable than they would be in southern states. Similarly, the western portion of each time zone already experiences later sunrises than the eastern portions. A one-size-fits-all national policy inevitably produces different outcomes depending on where people live.

Other policy options

One thing local governments could do if they are forced to adopt permanent daylight savings time is move the start times of their schools. Schools could even go as far as twice a year changing the start time by an hour, moving back in winter and moving it forward again in spring. 

One alternative that isn’t readily available but might make the most sense is to just let states pick what timezones they want to be in. The timezone map is already full of oddities, and this would give states the flexibility to choose specifically when they want their sunlight to be. If you wanted to take this a step further, let states choose in half-hour increments! It’s only weird until people get used to it.

Any sort of significant restructuring to how we keep our time is an opportunity for creative changes to other parts of our daily schedules. Our society relies on clocks to tell us where to be when, and we’ve had to adjust our bodies to respond to changes in the numbers. The point of all of this is at least in theory to help line up our lives with the sun. Maybe settling on a fixed time for the whole year can be an opportunity to rethink when certain things start.

Should Ohio freeze utility rates for a year?

Earlier this month, Ohio state Rep. Desiree Tims introduced a bill to freeze utility rate increases in Ohio for the next year.

Tims’s bill comes in a time of increasing concern about affordability. According to the Energy Information Administration, the residential price of electricity per kilowatt hour in Ohio grew 29% from 2019 to 2024.

Utility prices are an element of family budgets where the state has a clear lever for controlling costs.

Since electricity companies are regulated entities, they negotiate with the state public utilities commission to set prices for their ratepayers. The state has the ability to freeze these prices for a year if it wishes to do so.

In a lot of ways, this sort of policy change is like a sales tax holiday, which the state of Ohio has embraced in the past, or a gas tax holiday, which legislators have bandied about recently to offset gas price increases endured by Ohio in the wake of the U.S. war with Iran.

It represents a temporary suspension of a cost for ratepayers to provide relief in the short-term.

In the short term, a utility freeze would help low-income households.

According to the United States Department of Energy, the average low-income household in Ohio spends about 11% of their income on utilities compared to 3% for a median-income household and 1% for a high-income household.

While a utility freeze would be less targeted than an earned income tax credit, it would likely disproportionately benefit low-income households.

Energy insecurity can lead to unsafe health outcomes for households.

As many as 1 in 10 U.S. households report keeping their household temperature at an “unsafe” level due to energy prices.

A utility freeze could help households stabilize their finances without forcing them to cut back on heating or cooling their homes.

Stability in energy prices could even lead to stabilization in home environments that could have spillovers into educational outcomes, like what we’ve seen with federal energy assistance programs.

While low-income households spend a larger percentage of their income on energy, evidence from the Energy Information Administration suggests the highest-income households consume energy at nearly twice the rate of the lowest-income households.

This means upper-income households could be saving twice as many dollars from a utility rate freeze as low-income households, reducing the inequality impact.

The biggest problem with a rate freeze is that it could lead utilities to defer maintenance of existing infrastructure, which could impact reliability of electricity service and grid modernization efforts.

This could lead to more blackouts, especially as growth in energy demand is leading to more users taxing the power system.

Ultimately, this policy represents a bet that utility increases over the next year will not be used for essential maintenance, efficient grid modernization, or reliability upgrades that outweigh the short-term benefit the freeze would have for household incomes, especially for low-income households.

If Rep. Tims is right, this could mean better outcomes for households at little cost to the public.

If she is wrong, the short-term benefits would come at the cost of a quicker degradation of the grid, which means more blackouts and could lead to higher costs in the long run. 

While there is part of me that wants to see if her bet is correct, there is another part of me that thinks a more targeted policy, like an earned income tax credit expansion, would achieve the same boost to household income without the potential costs to the grid.

We’ll see if the General Assembly has an appetite for either.

This commentary first appeared in the Ohio Capital Journal.

Where are Ohio’s data centers headed?

Data centers have been one of the biggest news stories of the past year. Last month, my colleague Rob wrote about this trend: everyone is talking about data centers. For community members, worries about the environment, electricity prices, and societal change have become widespread. For developers and investors, the exponential growth of data centers across the United States is generating excitement.

In Ohio, data center construction has received a lot of pushback from local residents. Many people don’t want to see more data centers being built near their homes. For instance, in Columbus, residents are urging local lawmakers to require large data centers to submit water conservation plans and create decommissioning plans. Some data center proposals have been rejected across the state and many communities are considering pausing data center development entirely (a proposal we asked Ohio economists about a few months ago). 

Earlier this week, the Ohio Environmental Protection Agency announced it would not move forward with a permit that would have allowed data centers to discharge wastewater into natural water sources after receiving more than 7,000 comments on the proposal from the public. With all of this talk about data centers, I wanted to take a deeper dive into the existing and proposed data centers across the state. What kind of facilities already exist, and where are they located?

Where are data centers in Ohio?

Using data from DataCenterMap and Cleanview, we compiled a list of all existing and proposed data centers across Ohio. We found that there are 151 existing data centers in Ohio and an additional 143 data centers that have been proposed or are in development. We count each building as a separate data center, meaning that for larger data center campuses that include multiple facilities, each building is being counted separately. Below is a map of where the existing and proposed data centers are across Ohio. Seven planned data centers are omitted from the map due to insufficient data about the locations of these facilities. 

Data centers are most concentrated in Licking County and Franklin County in Central Ohio, where there are 66 and 64 existing and planned data centers respectively. Franklin County has a lot of existing facilities, a total of 50, while 38 of the 66 facilities in Licking County are planned or proposed. 23 of the 38 planned facilities in Licking County are from Amazon Web Services alone. Between 2015 and 2024, Amazon invested $6.2 billion in its Amazon Web Services data center infrastructure in Licking County. 

The next largest county for data centers in Ohio is Fayette County, home to Washington Court House and Jeffersonville. Fayette County doesn’t have any existing data centers, but has 23 planned and proposed data centers. All 23 of these data centers are from Amazon Web Services, for a total of nearly 1,800 megawatts of power (which would power 1.44 million homes, 125 times the number of households in Fayette County). Early last year, Amazon purchased 590 acres of land in Fayette County for just over $100 million to build these 23 facilities. They are expected to be completed within the next one to four years.

Similar to Fayette County, Portage County, home to Kent, Ravenna, and Streetsboro, has no existing facilities but 14 planned data centers. All 14 facilities will be part of one campus in Shalersville run by Bitdeer, a cryptocurrency mining and artificial intelligence cloud infrastructure company. The Bitdeer campus will be at least 650 megawatts (which would power 520,000 homes, 8 times the number of households in Portage County) and 257 acres of land. Bitdeer is expected to open its first two facilities in 2029 and the remaining 12 facilities in 2031.

How fast are data centers growing in Ohio?

The first data center in Ohio opened in 1966. Through the twentieth and early twenty-first century, data center growth was steady. Starting in 2017, data center growth became exponential. For reference, the first large language model (the first GPT model from OpenAI) was released in 2018, and ChatGPT released publicly in 2022.

If anything, the next several years is likely an underestimate, as there may be more data centers that will be planned and developed in Ohio in the next several years that aren’t yet announced (unless Ohio residents vote for a ban on data center development).

In addition to the total number of data centers in Ohio, the size and energy capacity of data centers is increasing exponentially as well. The number of hyperscale facilities, defined as data centers that take up at least 100,000 square feet of physical space and an energy capacity of 100 megawatts or more, is growing rapidly.

By 2034, Ohio is projected to have at least 36,000 megawatts of data center energy capacity across the state. 36,000 megawatts could power 28.8 million homes, which is 6 times the number of households in Ohio, and 21.5% of the number of households across the entire United States.

As data center growth continues, development will be a balance of competing interests: demand for digital services and artificial intelligence, environmental concerns, and local community priorities. Navigating these trade-offs requires us to weigh immediate capital gains against broader societal external costs and long-term resource availability. Evidence-based policy analysis is the key to understanding these tradeoffs moving forward.

This fall, Scioto Analysis will be releasing a cost-benefit analysis of existing and planned data center projects in Ohio to give policymakers and communities a clearer picture of the economic and environmental impacts of data centers across the state.

Ohio Economists think university consolidation would threaten Ohio’s economic future

In a survey released this morning by Scioto Analysis, 11 of 14 economists agreed that reducing the number of universities in Ohio would reduce long-term economic output in the state.

Earlier this year, candidate for Ohio governor Vivek Ramaswamy proposed shutting down some public colleges and universities in Ohio if he believes they are subpar. Over the past decade, enrollment at Ohio’s public universities has declined by 14%. According to data from the United States Census Bureau, Ohio has an aging population, and more students are turning away from college due to financial reasons.

11 of 14 economists agreed that reducing the number of universities in Ohio would reduce long-run economic output in the state. According to Albert Sumell of Youngstown State University, “A number of economic studies have shown the biggest determinant of long term economic growth is the percentage of the population with a bachelor's degree - which is directly related to the number of universities.” The remaining 3 economists were unsure of the impact of public college closure on long-run economic output in Ohio.

Economists were perfectly split on whether reducing the number of universities in Ohio would generate savings for the state of Ohio, with 6 economists agreeing, 6 economists disagreeing, and 2 economists uncertain. According to David Brasington of the University of Cincinnati, “There would be direct cost savings, but only about 6% of the state's general revenue fund currently goes to higher ed anyway.” The economists who disagreed explained that closing public universities in Ohio is short-sighted, and while it may generate savings in the short-run, it doesn’t address the long-term issues with higher education in Ohio.

12 of 14 economists agreed that reducing the number of universities in Ohio would reduce Ohio’s bachelor degree attainment rate. Kevin Egan of the University of Toledo explains, “80% of the Univ. of Toledo students live off campus and commute to UToledo. This is similarly true for many of the Ohio public universities. Closing any one of them will decrease the local cheaper public university option and some will not attend college.” The remaining 2 economists were unsure of the impact of public college closure on bachelor degree attainment in Ohio.

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

What makes policy analysis credible?

Earlier this month, I wrote a blog post reflecting on Eugene Bardach’s A Practical Guide for Policy Analysis: The Eightfold Path to More Effective Problem Solving. I wrote about the “Grandma Bessie Test,” a strategy Bardach recommends to make sure we understand the conclusions from our work well. To conduct the “Grandma Bessie Test,” we try to explain our data, findings, and conclusions to an intelligent but non-expert audience (like our Grandma Bessie) in about one minute.

Bardach’s eightfold path is a book that we reference a lot at Scioto Analysis. In some ways, it’s the very bread and butter of our work as policy analysts. The eightfold path is an eight-step framework to help us understand public policy and conduct policy analysis more effectively. The eight steps in the framework are as follows:

1. Define the Problem

2. Assemble Some Evidence

3. Construct the Alternatives

4. Select the Criteria

5. Project the Outcomes

6. Confront the Trade-Offs

7. Decide!

8. Tell Your Story

As Bardach develops these eight steps throughout his book, an important theme of credibility emerges repeatedly. Today, I want to focus on an important theme that Bardach references throughout his book: what makes an analysis credible, and what makes one not credible?

What makes policy analysis credible?

When we’re doing policy analysis, we should be thinking about credibility multiple times throughout the analysis. Credibility means selecting trusted sources, creating well-researched methodologies, and fact-checking our analysis at the end. The following considerations can help ensure our work is credible.

Defending against Politically Inspired Criticism

Defending against politically inspired criticism is the first strategy Bardach recommends to protect credibility. If we think there might be parties who will oppose or attempt to undermine our work, then we should touch base with them before publishing our analysis. 

We can do this by having conversations or conducting interviews with parties who might be opposed. To bolster our analysis, we can include quotes from opposing parties. In addition, we should always be thorough with our analysis: we shouldn’t conduct analysis that heavily favors one side without considering the other.

Ultimately, conducting evidence-based analysis with data and statistics may be the best way to defend against politically inspired criticism. If our findings are grounded in data and statistics, then critics cannot so easily sweep these findings aside as mere personal bias.

Preparing for Premature Exposure

The second strategy Bardach recommends to protect credibility is to prepare for premature exposure. Research and policy analysis can be a slow process. Throughout our analysis, we should be prepared for unexpected deadlines or sudden windows of opportunity. For example, reporters might reach out suddenly looking for a quote on a certain policy. Alternatively, an event might pop up that our analysis would be useful for.

To help prepare for these opportunities without sacrificing credibility, Bardach recommends that we spend time early in our analysis figuring out the answers to the most basic, crude questions that people are bound to ask. That way, if opportunities pop up throughout our analysis, we can share information without overstating our analysis.

Sensitivity Analysis

One of my favorite parts of performing policy analysis is sensitivity analysis. Sensitivity analysis is the process of estimating how assumptions included in an analysis impact the uncertainty around the findings of the analysis. Conducting sensitivity analysis is an exercise in testing the precision of our results. 

Bardach recommends we ask ourselves the question, “how big a mistake can I afford in this assumption before this analysis is in really big trouble?” Sensitivity analysis helps answer that question by forcing us to look at our assumptions and determine where results might be vulnerable. By conducting sensitivity analysis, we can prove that our results aren’t just based on a couple of key assumptions.

What makes policy analysis not credible?

Understanding how to produce credible analysis is important, but so is recognizing the pitfalls that can undermine its credibility. Bardach points out that some of the biggest problems with a project’s credibility come from carelessness and letting our own opinions get in the way of the data.

The quickest way to destroy our credibility is by allowing our preferred solution to interfere with how we define the problem. For instance, if we define a problem as “there is too little funding for this program”, we have already assumed the answer to the problem before doing any research. To ensure credibility, we should leave the door open for all potential solutions.

We can also kill our credibility by letting optimism get in the way of our analysis. Bardach warns us not to count on one of our alternatives working out just because we expect it to. Instead of assuming results, we should rely on data to show us what the truth of a certain policy really is.