Ohio General Assembly members propose sweeping bill to regulate data center development

Last week, a group of legislators in the Ohio House of Representatives introduced a far-reaching bill to regulate data center development across the state of Ohio.

In the face of concerns about the environmental, aesthetic, and public finance ramifications of Ohio’s rapid growth in data center development, Ohio House Bill 983 introduces a range of interventions designed to insert the public into data center development decisions.

The bill will require voter approval for every new construction or expansion of a data center with peak electric load over one megawatt for every municipality and township within five miles of the project.

Permits issued without voter approval would be considered void.

It would also impose new air emission and water discharge standards on a range of substances including PFAS, glycols, metals, and other organic compounds. These standards would apply to existing data centers after eighteen months.

Owners of data centers would also be held financially responsible for water supply and water pressure impacts associated with their centers.

On the fiscal side, the legislation would ban local governments from offering property tax incentives for data centers and power plants associated with them. It would also require public disclosure of all data center agreements associated with development and supply.

This legislation would significantly slow down the development of data centers in Ohio, if not stop it altogether.

The voter approval requirement in particular could lead to dozens of required communitywide votes across the country to authorize data center developments.

This would have a substantial impact on Ohio’s economy.

Developers are set to invest $40 billion in data centers across the state of Ohio over the next four years. While this legislation would prevent some wasteful incentive spending, it would also likely cost the state economy tens of billions of dollars in investment.

One of the major concerns people have with data center development in Ohio is strain on public utilities, particularly electricity and water.

If data center development drives up demand for each of these, it could drive prices for electricity and water up for local residents.

This could fall more heavily on low-income residents because they spend more of their income on utilities than high-income residents. This bill’s proposed interventions could mitigate some of these cost concerns.

There are other spillover effects people worry about with data center developments.

Will incentives leave less money available for schools? Will data centers lead to wastewater, air emissions, and noise pollution?

These are concerns that could be bluntly dealt with through bans on incentives and community votes, which are likely to torpedo most projects.

Overall, though, it seems like this bill is a hammer for an issue that likely needs a scalpel.

Communitywide votes to authorize new developments would likely be tantamount to a total ban of data center development across the state. This could slow Ohio’s economy to the tune of tens of billions of dollars over the next few years.

Making sure that Ohio’s electricity and water systems keep up with new development could probably be achieved with less economic pain than a de facto permanent ban on development would create.

This commentary first appeared in the Ohio Capital Journal.

What assumptions underlie cost-benefit analysis?

Over the past year, I have had some correspondence with Aidan Vining, one of the co-authors of a leading textbook on public policy analysis and the “bible” of cost-benefit analysis. This has been exciting for me because few people have had a larger hand shaping guidance for this generation of public policy analysis than Dr. Vining. His perspectives on policy analysis broadly and cost-benefit analysis specifically have been incredibly influential to both the profession and me.

Last month, Vining emailed me a journal article he just published in the Journal of Benefit-Cost Analysis titled “Ten Principles of Cost-Benefit Analysis and Five Barriers to Its Wider Use.” I thought it would be valuable to share these ten principles on this blog because they are helpful to understanding how cost-benefit analysis works.

1. Cost-benefit analysis measures efficiency, not overall social welfare.

Vining argues that cost-benefit analysis is a tool for analyzing “allocative efficiency,” not “social welfare.” What does this mean?

Basically, what he is saying is that cost-benefit analysis helps us understand whether a policy will make the economic “pie” bigger–whether it will increase the amount of value in society, measured in dollars. This is not the same as measuring whether society is better, because distribution of that value may matter. My graduate school benefit-cost professor Dan Acland thinks that it is appropriate to fold utility measures into cost-benefit analysis. Vining argues against this, saying it is better to present those measures alongside traditional willingness-to-pay results.

This is a matter of controversy in cost-benefit analysis, but my inclination, to the chagrin of my mentor and friend Dr. Acland, is still to say dollars should be presented as dollars and weights should be presented alongside them. This transparency allows the policymaker to get the full analysis rather than obscuring standard willingness to pay beneath weighted willingness to pay.

2. The individual is the basic unit of analysis.

I find this to be a very interesting element of economic analysis in general. In economic analysis, the whole is always equal to the sum of its parts. Vining argues this is a “minimalist meaning of methodological individualism,” but it is indeed philosophically weighty. What we value about being parts of social units seems to transcend what we gain from them individually, at least when done right. A marriage is not just the benefits it bestows on both parties, it is a good in itself. It is useful to understand, though, that cost-benefit analysis has little value to understanding that sort of good and how social organization interacts with it.

3. People’s choices are usually treated as rational.

Vining says that cost-benefit analysts generally assume that people make their decisions based on their preferences and that they are rational and well-informed. This is an underlying assumption throughout economic analysis as a whole, with some exceptions. In our cost-benefit analysis we conducted on cigarette taxes last year, we used a model developed by cost-benefit researchers to estimate how much of cigarette consumption is “rational,” treating irrational consumption as deadweight loss. Vining’s frequent collaborator David Weimer has also written a book on how to incorporate economic research findings on systematic irrationality into cost-benefit analysis models.

4. Existing property rights usually provide the starting point for analysis.

Cost-benefit analysis is usually conducted to evaluate a policy that could, will, or has been implemented. Since a policy requires a change from a status quo, cost-benefit analysis evaluates a change from a certain status quo. This can have methodological impacts, like whether to use “willingness to pay” or “willingness to accept” measures. It also means cost-benefit analysis is not a good policy for telling you who has the rights to certain goods and services, just whether a policy will increase or decrease the amount of value people get from them given the current resource distribution.

5. Each affected person counts equally.

Traditionally done, cost-benefit analysis treats each individual’s costs and benefits as equally important. Even Dan Acland’s equity weighting scheme is ultimately about trying to bring costs and benefits closer to being in line with utilitarian impact, thus treating each individual equally. Vining says this makes economic impact and “stakeholder-specific” analysis fundamentally incompatible with cost-benefit analysis. I have written in the past about how “personhood” may even be too narrow of a definition for standing in the use of cost-benefit analysis.

6. Value is primarily measured through willingness to pay or accept.

Cost-benefit analysis is ultimately the project of evaluating the satisfaction of preferences throughout an economy. This means that the criteria for a cost-benefit analysis has to be the value people place on goods created by (or destroyed by) a given policy. Using revealed preference, survey, time use proxies, estimation of value of statistical life, or other strategies that focus on how people themselves value outcomes is key for effective cost-benefit analysis.

7. Future impacts must be discounted.

People prefer present benefits to future benefits and future costs to present costs. Vining argues that some of this is irrational, but some is not. Vining lays a stake in the discounting debate, noting a discount of two to three percent, though acknowledging many other governments go much higher. We have written a lot about discount rates in the past.

8. CBA follows a “no-envy” rule.

While there is some evidence that benefits that accrue to some people cause psychological costs to others (think “keeping up with the Joneses”), cost-benefit analysis tends to stay away from factoring these impacts into the analysis of the total value of those benefits. This helps cost-benefit analysis stay in the lane of estimating allocative efficiency and not smuggling equity measures into the analysis, thus making the analysis more obscure.

9. The nation is ordinarily how standing is determined.

This is an interesting claim and a new one for me. Not the result of the claim, but the logic that gets us there. Vining argues that constitutions can be treated as “efficient” since members all gain the benefit of society. This makes constitutional government a legitimate basis for cost-benefit analysis, which could be national, but also can apply to some states. Vining argues that municipal governments do not have constitutions, though does not make it clear why city charters and “home rule” provisions do not qualify in the same way a state constitution does. But this is his argument for why economic impact analysis does not qualify as cost-benefit analysis: people have standing outside of the analysis area unless some sort of constitution draws that line.

10. International agreements can sometimes expand that boundary.

Vining argues that contracts between states can increase standing. This comes up when we do analysis of carbon emissions, which usually have small local costs and large global costs. Vining’s argument implies that an international agreement to curb emissions could lead to full global valuation of carbon emission reductions for national projects.

Vining’s paper provides a good overview of some key underpinnings of cost-benefit analysis. Good exercise of cost-benefit analysis means understanding what it is doing, understanding how it works, and understanding its limits. If analysts can do these things well, cost-benefit analysis will continue to be a valuable tool for making more efficient public policy.

How do you measure happiness?

I spent last week in Lexington, Kentucky at the annual conference of the International Society for Quality-of-Life Studies, a global interdisciplinary society dedicated to advancing research and knowledge on quality of life (QOL), wellbeing, and happiness studies. For the past decade, I have served on the board for Gross National Happiness USA, a grassroots organization committed to changing measures of progress and success in the United States. The first Scioto Analysis study was released in conjunction with Gross National Happiness USA so we go back a while.

Why should we care about happiness? When I was at the think tank that I worked at before starting Scioto Analysis, I was excited to tell a colleague that I had joined the Gross National Happiness USA board. Her response to me was only to say “fun.” I saw this work as crucial work in the public policy space, but for a lot of people who consider themselves “serious” policy analysts and researchers, the concept of happiness is treated like a little treat rather than an important public policy undertaking.

Two hundred fifty years ago, Jeremy Bentham first argued “It is the greatest happiness of the greatest number that is the measure of right and wrong.” While Bentham widely popularized the idea, he was paraphrasing Irish Philosopher Francis Hutcheson, who argued that the best action produces “the greatest Happiness for the greatest Numbers” fifty years earlier. Suffice to say that people have been explicitly making the argument that morality is based on improving happiness for hundreds of years.

Despite this longstanding charge for morality and public policy, research on happiness has continued to be a fringe subfield in the world of public policy. 

Part of this is due to a lack of data. Gross Domestic Product is calculated quarterly, and employment numbers are released monthly. Due to tax data and survey data, researchers have many places to turn to see what the impacts of public policy are on traditional economic indicators. Environmental data from the Environmental Protection Agency and the Energy Information Administration give researchers data to see how policy impacts the environment. The Centers for Disease Control and Prevention publishes deep research on health, giving researchers opportunities to study health and public policy.

The imbalance between traditional economic indicators and well-being indicators is replete throughout government. The U.S. Federal Government has 36 “principal indicators,” none of which are well-being indicators. The U.S. Federal Reserve releases quarterly projections of GDP growth, unemployment, inflation, and interest rates. The Bureau of Labor Statistics’s monthly jobs report draws on approximately 60,000 households and 121,000 employers representing about 631,000 worksites. Its employer survey covers one-third of all nonfarm payroll jobs. Meanwhile, the CDC asks a life satisfaction question to only two-thirds of states.

While people in the United States do not often focus on statistics around happiness and well-being, progress has been made in other countries, especially in Europe. For over a decade, the United Kingdom’s Office of National Statistics has been asking the question “Overall, how satisfied are you with your life nowadays?” to representative samples of people across the country. The United Kingdom has gone as far as to incorporate well-being measures into their cost-benefit analysis, though there is legitimate question whether this confuses both the practice of benefit-cost analysis and well-being research.

The United Kingdom is not alone in asking this question. Statistics Canada asks its residents “How do you feel about your life as a whole right now?” Germany’s Socio-Economic Panel asks “How satisfied are you with your life, all things considered?”

You can see that many of these formulations follow a similar pattern. The Organisation for Economic Co-operation and Development (OECD) recommends the question “Overall, how satisfied are you with life as a whole these days?” as the core life satisfaction question for its member states. You can see this echoed throughout most of these questions.

Notably, the most common, headline-grabbing happiness report in the world does not use this question.

The World Happiness Report, known for its annual national happiness rankings, uses data from the Gallup World Poll. Respondents are asked to imagine themselves somewhere on a ladder, with the bottom rung being their worst possible life and the top rung being their best possible life. They then answer the question “On which step of the ladder would you say you personally feel you stand at this time?”

In general, this “Cantril Ladder” question is treated as interchangeable with the “how satisfied are you with your life nowadays” question. Some are starting to question this assumption though.

At this year’s conference, I attended a talk where a researcher shared data from the Global Flourishing Study. One of the big findings she shared was that two questions people have about life satisfaction ended up having very different correlations with other questions, even though they are often treated interchangeably in happiness research.

The Global Flourishing Survey asks both the life satisfaction and the Cantril Ladder question, so researchers were able to compare how these questions interacted with other questions using the same respondents and the same survey. What they found was that the life satisfaction question correlated strongly with interpersonal connections and relationships. The Cantril Ladder question, on the other hand, was much more determined by income than the life satisfaction question was.

This intuitively makes sense to me. The Cantril ladder is more of a comparative question, where you are explicitly challenged to imagine your life as better or worse. If I think about my life in those comparative terms, I often think about having more or less money. On the other hand, if I think about how well my life is going nowadays, I think much more about how I spend my time, including who I get to see on a day-to-day basis.

It seems like both of these questions are illuminating and we probably should do some more research to find out how these two compare to one another. But whichever direction we go, well-being deserves a place at the table. Policymakers should have information on how policy impacts well-being. That is, after all, one of their most important jobs: to promote the general welfare and to help their citizens pursue happiness.

What’s the difference between a carbon tax and cap-and-trade programs?

In environmental policy, reducing carbon emissions generally means putting a price on pollution or limiting the amount of pollution that can occur. Two of the most common approaches are carbon taxes and cap-and-trade. While both use market-based mechanisms to reduce emissions, they create different incentives and have different tradeoffs for policymakers and businesses. 

Carbon Tax

A carbon tax works by setting a price on carbon emissions, and allowing firms to emit as much carbon as they can afford. This functions as a Pigouvian tax which if set would lead to an efficient decrease in carbon emissions. 

In order to effectively set a carbon tax, policymakers need to understand what the social cost of carbon is, in other words what the global externality of carbon emissions costs. If policymakers arrive at a solid estimate for this number, then economic theory tells us that markets with any amount of carbon emissions should respond accordingly and shift their production/consumption to a socially optimal level. 

Cap-and-trade

Sometimes branded as “cap-and-invest,” this policy works by setting a limit on the total amount of carbon emissions that occur in a given jurisdiction, then setting up a market that enables firms to buy and sell the legal ability to emit carbon. For example, every factory in a state might be given a certain allotment of carbon credits that determine how much total carbon they are allowed to emit. Factories that emit more than that will have to buy credits from factories that emit less than their allotment, acting as a de facto subsidy for firms that reduce emissions. 

This provides a market framework that essentially allows firms to determine among themselves where the most cost-effective emission reduction opportunities are. Because firms can buy and sell these carbon allowances, firms that have opportunities to reduce their emissions can finance these changes by selling their allowances to firms that can’t change as easily. 

Which is better: carbon taxes or cap-and-trade?

In terms of reducing emissions, both can be effective if implemented correctly. The tradeoffs are more about what signals firms are receiving and how the policies react to sudden changes in economic conditions. 

A carbon tax offers price certainty to firms. This means that they can more easily plan their response to the policy, and it is clear based on the price of carbon whether or not some pollution reducing investment would be worthwhile financially. 

The main drawback of a carbon tax is that a sudden change in economic conditions might make the policy less effective at reducing emissions. An economic boom in a heavily polluting industry might lead to producers absorbing the carbon tax and polluting through it, or burdensome regulations on carbon-free electricity sources might make fossil fuels relatively more competitive. 

In contrast, the cap-and-trade framework does away with price certainty and instead limits overall emissions in a given region. There can be more price volatility for firms, but there is more certainty surrounding total emissions. 

Cap-and-trade can also adjust more naturally to changes in the cost of reducing emissions. If reducing emissions becomes unexpectedly cheap, firms have an incentive to reduce more pollution and sell their excess allowances. If emissions reductions become more expensive, firms can instead purchase allowances from other firms, preventing the overall cost of meeting the emissions target from becoming unnecessarily high. 

Ultimately, there is no single policy that is always better. Both carbon taxes and cap-and-trade can be effective tools for reducing emissions. As long as both are designed properly, policymakers can focus on which areas of certainty/uncertainty they feel more comfortable with.

Ohio economists split on the impacts of permanent daylight saving time

In a survey released this morning by Scioto Analysis, 7 of 15 economists agreed that the twice yearly clock change harms Ohio’s economy.

In July, the Sunshine Protection Act was passed in the United States House of Representatives and is currently awaiting approval from the United States Senate. The Sunshine Protection Act would make Daylight Saving Time, which is traditionally observed from the second Sunday in March to the first Sunday in November, permanent across the United States. Proponents of the bill argue that more sunlight in the evening would provide more usable time for shopping and recreation during the winter. Opponents argue that less daylight in the morning would create safety concerns for schoolchildren and productivity losses for agriculture and construction workers.

7 of 15 economists agreed that the twice yearly clock change harms Ohio’s economy. According to Bill LaFayette of Regionomics, “The big impact comes from throwing off people’s circadian rhythm when the time changes. There are more traffic crashes, more strokes and heart attacks, and errors at work that could have financial implications.” Of the remaining economists, 6 disagreed and 2 were uncertain.

7 of 15 economists were uncertain if permanent Daylight Saving Time in Ohio will increase consumer spending on leisure, dining, and retail. Of the remaining economists, 4 agreed that consumer spending would increase and 4 disagreed. Jonathan Andreas of Bluffton University agreed, explaining, “The morning hasn’t been as much of a time for spending as the evening, so retailers like this proposal!” However, according to Kevin Egan of the University of Toledo, “If there is any small increase in spending in the extra hour at night during the winter, it will be offset by less spending somewhere else.”

8 of 15 economists were uncertain if permanent Daylight Saving Time will improve the health and quality of life for Ohio residents. According to Kathryn Wilson of Kent State University, “There are conflicting results that I would expect. Research shows that the time change disrupts sleep patterns. [...] However, when permanent daylight savings was implemented in the 1970s, there were a number of children killed on their way to school in the morning.” Of the remaining economists, 2 agreed that health and quality of life would improve for Ohio residents, and 5 disagreed. 

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.

The New World Screwworm’s Return and the Paradox of Prevention

Lately, I’ve been reading the book Spillover by David Quammen. It’s a fascinating look at “zoonoses,” the term for contagions from animals that spill over into human populations. Illnesses such as HIV, ebola, and coronaviruses (like the one that caused the COVID pandemic) fall into this category.

As the human population has exploded over the past few centuries and we have encroached ever further into areas formerly uninhabited by humans, the crossover of sicknesses from animals to people has become more common. Disease-causing organisms which have long circulated in animal populations can make a foray into people and cause human suffering and economic losses whenever humans encroach onto formerly wild spaces. No one knows how many yet-undiscovered germs lurk in animal populations which could cause new human diseases, and as such, it’s hard to be prepared for these threats.

The idea of these novel bugs from far-flung corners of the world at the interface between human settlements and wilderness may capture your imagination (as it has mine throughout Quammen’s book), but biological threats do not have to be novel or exotic to catch us unprepared. Frequently, the biological threats governments struggle to contain are not actually mysterious or new at all. Some are familiar, well understood, and quite preventable. The comeback story of the New World screwworm offers a clear example.

New World Screwworm’s Eradication and Resurgence

In June, the US Department of Agriculture confirmed that the New World screwworm had returned to the United States. It was first found in a calf in Texas 50 miles from the US-Mexico border, then reported in more cattle and a dog in the weeks that followed. 

Screwworms are parasites which can take humans, livestock, pets, and even birds as their host. Screwworm flies can lay eggs in skin scrapes as small as tick bites or in the mucous membranes of warm-blooded animals or people. Once hatched, the larvae burrow into the skin and feed on living tissue before leaving their host to transform into adult flies. Screwworm infection can prove deadly if left untreated.

Screwworms also impose substantial costs on agriculture. Infested livestock may die or require veterinary treatment, while ranchers must spend more time and money inspecting animals, treating wounds, and controlling outbreaks. Infestations can also reduce meat and milk production. The US Department of Agriculture estimates that a widespread outbreak in Texas alone could cost $1.8 billion annually in livestock deaths and treatment costs. 

The screwworm detections in Texas were noteworthy because the US successfully eradicated the New World screwworm from its soil in the 1960s by releasing millions of sterile male flies over the course of years. These sterile flies mated with females and crowded out fertile males in such large numbers that future generations became successively smaller until eventually, local populations were eradicated.

The US continued the effort after local eradication, releasing sterile flies south of our border in partnership with central American countries. By the early 2000s, the campaign had pushed screwworm south to Panama, and then even Panama was formally declared screwworm-free in 2006. Governments working together had rid the entire North American continent of the parasite.

The program was costly. The production and release of sterile flies cost about $1.3 billion across North America over roughly 55 years. But the economic benefits of eradication were much larger. An estimate updated in 2021 places annual benefits to livestock producers in the United States at $896 million, with an additional benefit of $329 million in Mexico and $88 million in Central America. That study identifies livestock producers as the primary direct beneficiaries and describes additional benefits including greater availability of livestock and dairy products, fewer meat and milk shortages, and reduced reliance on food imports. It notes that public health benefits may also exist, but those are separate from the producer benefit estimates.  The US Department of Agriculture estimates that screwworm eradication also resulted in $2.8 billion in annual benefits to the wider economy, although the agency does not detail how that broader estimate is calculated or divided among different types of benefits.

Even so, as the eradication effort moved south, sterile screwworm production and release facilities in the US consolidated. Domestic facilities stopped producing sterile male flies and production of the flies moved to a Mexican facility. Given that the problem had retreated, this made sense; Mexico was a better location to invest the funds and efforts. Eventually, though, the last fly production plant in Chiapas, Mexico, closed in 2012, after the US transferred its ownership to the Mexican government. This closure left a facility in Pacora, Panama as North America's only producer of sterile flies.

For a decade afterward, all seemed well. Then, in 2023, a screwworm outbreak in Panama  prompted a local state of emergency. The screwworm began to march north again, and by the following year, it was spotted in Mexico. The US Department of Agriculture formulated a response plan in 2025, then announced plans for a new facility in Texas to produce sterile flies in March 2026

 By June 2026, the screwworm had infected over 170,000 animals and 2,000 people in Central America and Mexico, causing 10 human deaths. That same month, the screwworm was detected in Texas.

The Price of Rebuilding Defenses

The old adage is that an ounce of prevention is worth a pound of cure. Although the US continued funding containment efforts in Panama long after screwworms were eradicated on US territory, regional sterile fly production became concentrated in a single facility without enough capacity to respond to the large outbreak which emerged. Now that the barrier has been breached, eradicating the screwworm in the US again will be costly.

The remaining facility in Panama can currently produce approximately 100 million sterile flies per week. This is nowhere near enough capacity for the present problem, so the US Department of Agriculture is building a $750 million facility to produce up to 300 million sterile flies per week in Texas. They are also spending $25 million to release sterile flies in Arizona, and spending $21 million on sterile fly production at a facility in Mexico which should churn out 100 million flies per week once active. These represent initial investments; once operational, the Texas facility will cost additional operating dollars each year, and fly releases will likely be needed again in Arizona in future years, too. 

In addition to these efforts, the US Department of Agriculture awarded $105 million to 40 other projects meant to strengthen the US defense against screwworms. The US will also fund projects to provide Mexico traps and lures, enhance live animal inspections at ports of entry, and to audit Mexico’s animal health controls. 

Preventing screwworm’s resurgence will certainly be expensive, but the potential for agricultural losses if we do not stop it extends well beyond Texas. Before eradication, the screwworm was endemic to Florida, California, Arizona, New Mexico, Oklahoma, Louisiana, Arkansas, Missouri, and Kansas.

There’s substantial crossover between that list and the list of the largest cattle inventories among US states today. Texas had 12.5 million heads of beef or dairy cattle in 2022, by far the largest inventory. Kansas ranked #3, with 6.1 million, Oklahoma #5, with 4.5 million, and Missouri #6, with 4 million. 

If the U.S. federal government allows screwworm to continue claiming ground, ranchers owning a sizable share of the US cattle supply would need to spend money surveilling their cattle. The infection is generally treatable so long as it is caught early, so ranchers would be incentivized to keep a close eye on their herds. Once spotted, veterinary care and quarantining are needed, all of which raise operations costs for farms and would be likely to raise the final price of beef and dairy for consumers.

Why Governments Underinvest in Prevention

After the COVID pandemic, it might seem obvious that governments should spend more to detect and contain biological threats before they become emergencies. Screwworm shows us why that remains difficult, though. The US did not wholly abandon its prevention efforts once the problem disappeared from its soil; it maintained a sterile fly barrier south of our border for decades. But as the parasite disappeared from public view and faded from recent memory, production facilities eventually closed, leaving the system dependent on one Panamanian plant with limited capacity. This approach left the US with little margin for error and no slack capacity in the case of a reappearance of the flies. 

The choice to consolidate sterile fly production to one facility may have seemed appropriate at the time. Maintaining multiple facilities would have cost more during normal years and may have been hard to justify. But as soon as the problem reemerged, we saw why capacity was needed: biological production cannot be turned on overnight. The facility the US Department of Agriculture is funding in Texas won’t open until the end of 2027

The lesson is perhaps that successful prevention often paradoxically makes itself appear unnecessary. When prevention programs work as intended, their benefits manifest as bad outcomes that people never have to experience. In the case of screwworm, it’s livestock that don’t die, veterinary treatments not needed, cattle trade that isn’t interrupted, and beef prices that don’t rise. It’s all hypothetical and invisible. The facilities, expertise, surveillance, and institutional capacity that prevent those outcomes can start to look like wasteful public spending when their benefits are so intangible. These kinds of programs could be an easy cut for politicians looking to scale back budgets.

While as a policy analyst, I understand that prevention programs frequently have benefits which outweigh their costs, I also understand the impulse to undervalue prevention. If you recall, my introduction to this topic was the tangentially related book, Spillover. That book is filled with unfamiliar pathogens emerging from strange animals in exotic forests and caves. These frightening biological threats captured my attention readily; they appeal to a sense of discovery and danger. The book inspired me to set out looking for the next potential strange disease emerging from some distant corner of the world. Instead, I found this story about an old agricultural parasite that governments had already spent decades beating back. Though I hope this post is interesting, I know it won’t be in the same way that Spillover is. Managing old problems is not as inherently interesting as the mystery and intrigue of fighting a new one.

Because of this, a novel threat can easily generate headlines, emergency declarations, and new funding. Screwworm was an old problem we believed we’d already solved. Its long absence made continued preparedness harder to justify investment in, and now that screwworms have returned, the US must rebuild its defenses while also responding to the incursion the screwworms have already made. 

The New World screwworm’s resurgence is a reminder that discovering and defeating biological threats are only one aspect of the public health challenges we face. Governments must also stay vigilant at the task of sustaining the institutions that keep old threats from becoming new emergencies, long after their original danger has faded from public memory.

Which U.S. cities will implement congestion fees next?

Earlier this week, Scioto Analysis released a cost-benefit analysis of New York City’s Congestion Relief Zone during its first full year of operation. We found that congestion pricing in New York City produced $2 billion in net benefits in 2025. Some of these benefits are reduced air pollution, driver time savings, and avoided vehicle crashes. 

Cities across the globe have implemented congestion relief zones prior to New York City, including Singapore, London, Stockholm, and Milan. All four of these cities saw reductions in traffic between 11% and 45% during the first year of congestion relief zone operation. This got me thinking: if congestion relief zones are so effective at reducing traffic, then which cities in the United States should be next for congestion relief zones?

Which cities across the United States are most congested?

The easy answer would be cities in the United States that suffer the most from congestion. The table below shows the top ten cities based on 2024 annual hours of delay according to the Texas A&M Transportation Institute’s Mobility Division.

Most Congested Cities in the United States
City 2024 Annual Hours of Delay
Los Angeles, CA 1.1 million hours
New York, NY 890,000 hours
Chicago, IL 420,000 hours
Miami, FL 340,000 hours
Houston, TX 270,000 hours
San Francisco, CA 270,000 hours
Atlanta, GA 260,000 hours
Washington, DC 230,000 hours
Dallas, TX 230,000 hours
Philadelphia, PA 220,000 hours

These annual hours of delay are for the year before the congestion relief zone in New York City began. Using congestion data alone, Los Angeles might be a better candidate for a congestion relief zone than New York City. If anything, it seems like Los Angeles is the obvious next choice for a congestion relief zone: New York City and Los Angeles stand alone in terms of congestion. The third most congested city is Chicago, which has less than 40% the congestion of Los Angeles.

While high congestion is the prerequisite for implementing a congestion relief zone, it certainly isn’t the only factor at play. At its core, a congestion fee charges drivers to enter designated zones during peak hours. The logic is simple: we are taxing traffic because we want less of it. 

However, people are entering these zones for a reason: work, recreation, shopping, or something else entirely. A congestion fee won’t be high enough to deter everyone from entering the zone. Some drivers whose destinations lie outside the zone may instead reroute around it, potentially shifting traffic into surrounding areas. But for anyone who still needs to get into the zone and won't be deterred, there has to be a viable alternative form of transportation. Otherwise, congestion pricing places a disproportionate burden on drivers who can’t afford the fee.

Which cities have the best public transit?

One answer to this problem is public transit. If a city has high-quality public transit, then people should still be able to get where they need to go without driving. According to the AllTransit Rankings from the Center for Neighborhood Technology, we rank the top ten cities for public transit in the United States in the table below.

Best Public Transit Systems in the United States
City Transit Performance Score
New York, NY 9.6
San Francisco, CA 9.3
Jersey City, NJ 9.2
Chicago, IL 9.2
Washington, DC 9.1
Boston, MA 9.1
Philadelphia, PA 9.1
Newark, NJ 8.8
Miami, FL 8.5
Seattle, WA 8.3

New York City ranks at the top of public transit rankings in the United States. New York residents who choose not to drive in designated zones due to congestion pricing can instead take the subway or bus to get where they need to go. Some other cities that have both high congestion rates and high-quality public transit are Chicago, San Francisco, Miami, Philadelphia, and Washington, DC.

One city that is missing from these rankings is Los Angeles, the most congested city in the country. Public transit in Los Angeles ranks 18th in the United States. This means that Los Angeles has a similar quality public transit system to my home city, Cleveland (which has a tenth of the population of Los Angeles). 

Which cities have the busiest central business districts?

To create a congestion relief zone, there needs to be an identifiable zone to implement congestion fee pricing in. This means that congestion shouldn’t be widely dispersed across the city; it should be concentrated in a particular area. 

One pattern we noticed during our analysis of New York City’s congestion relief zone is that congestion relief zones have generally been created around a city's central business district. If we assume that the next United States city would take the same approach, we can estimate which central business districts suffer from the heaviest traffic.

While neighborhood-level traffic data is difficult to collect, we can use the share of jobs located in the central business district as a proxy. If a large chunk of a city’s jobs are in the central business district, then lots of people have to travel there on a daily basis. Using a 2020 report from Demographia, the table below shows the top 10 cities in the country ranked by the proportion of jobs located in their central business districts.

United States Cities with Highest Proportion of Jobs in Central Business District
City Jobs in Central Business District Percentage of Total
New York, NY 1,900,000 20.2%
San Francisco, CA 370,000 16.3%
Washington, DC 430,000 13.0%
Chicago, IL 570,000 12.5%
Honolulu, HI 56,000 11.4%
Seattle, WA 210,000 11.2%
Boston, MA 260,000 10.1%
Hartford, CT 63,000 10.1%
New Orlean, LA 59,000 9.9%
Richmond, VA 61,000 9.8%

Once again, Los Angeles is missing from this list. Despite being the most congested city in the country, it ranks 49th in the proportion of jobs located in its central business district, with just 2.6% of Los Angeles jobs in the central business district.

Which cities are next for congestion fees?

Based on these rankings, it's easy to see why New York City was the city most fit for a congestion relief zone in the United States. The only remaining cities that have high congestion, high-quality public transit, and a large share of jobs in their central business districts are Chicago, San Francisco, and Washington, DC. Based on the success that New York City has seen so far with its congestion relief zone, one of these cities might be next.

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.