What could state and local governments buy with the cost of the war in Iran?

As of the writing of this article, the United States has spent half a month in a ceasefire with Iran. According to the New York Times, the White House has not released any estimates of the cost of the war, but independent groups estimate it has cost between $28 and $35 billion since it began.

It can be hard to get our heads around what these tens of billions of dollars mean. The Census Bureau did recently give us one yardstick that we can use for some sort of comparison, though: a first look at the 2024 results of the Annual Survey of State and Local Government Finance.

The Annual Survey of State and Local Government Finance is a valuable data source for people like us who spend our time analyzing state and local budgets. This survey collects data from state and local governments across the country which the Census Bureau then uses to estimate what total spending looks like in state and local governments across the country.

The data released this month is just a “first look.” This means that they are releasing aggregate nationwide data–basically what state and local governments spent on a number of different categories taken as a whole. This does give us an interesting point of comparison for spending on the war in Iran. If this money was instead given as a transfer to the nation’s state and local governments, what could they finance with it?

State revenues that could be replaced by Iran War spending

First, let’s look at the revenue side of the equation. The way we could think of this is what revenue streams could be completely replaced if the federal government gave its Iran War money to states instead of using them to wage the war. Below are a few examples.

State property taxes: $23 billion

Coming in below the $28-35 billion estimated cost of the war in Iran are state property tax costs. Now most property tax is collected by local governments–the recent First Look estimate is that local governments collected about $700 billion in property taxes in 2024. But state governments collected $23 billion of their own property taxes. This matters because property taxes are regressive since low-income people spend a larger proportion of their income on housing than upper-income people, and renters pay the brunt of property taxes for their landlords in the form of higher rents. Giving a grant that would allow state governments to do away with their property taxes would put a lot of cash back in the pockets of low-income families.

Motor vehicle licenses: $34 billion

Yep, that’s right: we could make all registrations of cars free with the amount of money the federal government has spent on the war in Iran. This is another fee that often falls more heavily on low-income families since it is usually assessed as a flat fee that falls on each vehicle. Doing away with this fee would be very helpful for low-income households in parts of the country where a car is essential for work.

Local education charges: $25 billion

Local governments charge fees for education services they provide, like community colleges they run, school lunches they charge for, some tuitions they charge, and other charges. All of these could be made free for the cost of the war in Iran. I don’t want to gloss over this portion of it: local governments collected $4.4 billion in lunch fees in 2024. This means that dialing the war spending back by as little as 12% could have freed up enough funds to make school lunches free for the whole country.

State lotteries: $35 billion

State lotteries collected about $35 billion in funds, much of which went to education and funding other state programs. These lotteries could be done away with or just become free with the cost of the war in Iran. This could be a boon for addictive gamblers who lose money on the lottery and also could do away with a regressive institution which costs that fall much more heavily on low-income people than on upper-income people.  

State programs that could be financed by Iran War spending

Now let’s look at expenditures. These are categories of spending in state and local government that could be financed at the cost of the war in Iran.

State elementary and secondary education spending: $24 billion

While the bulk of elementary and secondary school spending comes from local government, states supported elementary and secondary schools to the tune of $24 billion in 2024. That means that a grant that financed all state contributions to elementary and secondary school spending could have been financed with the cost of the war in Iran.

Local library spending: $16 billion

This one is considerably less than the current estimates of the war in Iran, but I had to include it. Local governments across the country spent about $16 billion on libraries in 2024. That means that all the local libraries in the country could have been funded for a year and the federal government could have still had enough money left over to fund a small war in Iran.

State police spending: $24 billion

States have police forces like state patrols that are in charge of keeping highways safe and enforcing state laws. These could be funded across the country for a year for the cost of the war in Iran.

Local jails: $35 billion

Local jails across the country are used to hold people who have been convicted of minor crimes and to hold people before sentencing. The nation’s local jails could be financed for a year with the cost of the war in Iran.

State transit spending: $21 billion

States across the country spent about $21 billion on transit support in 2024. For the cost of the war in Iran, the United States could fund all the state support for transit agencies across the country for a year.

Spending has opportunity costs. The federal government could be making school lunches free, replacing state property taxes, or funding all the local libraries across the country. Instead, it is fighting a war in Iran. I am not an expert in foreign policy, but I can tell you that the opportunity costs for this spending are steep.

Which states have the most no-car households?

Last month, I attended the National Bike Summit, a conference hosted by the League of American Bicyclists. I did this because the executive director of the Iowa Bicycle Coalition was presenting the results of the economic impact analysis we had conducted on their behalf the previous year.

At the conference, I heard a range of different presentations. These ranged from a county commissioner from Houston speaking about the growth of bicycle infrastructure in his city to the editor-in-chief of the Cook Political report speaking on the federal prospect to the midterms to cycling economists from the Netherlands running a simulation on cycling reform at the local level. 

The final talk of the conference, though, was the most fiery. It was given by the hosts of a popular podcast called The War on Cars that is focused on pushing back against car culture in the United States.

Full disclosure: I am a bit more on the “ditch your car” side of the transportation spectrum myself. I never owned a car in high school or college and managed to live without one until an employer in Omaha required me to buy one as a condition of employment. Once I moved to California, I got rid of my car and was able to live mainly walking, bussing, and biking until my current fiancee moved in with me a couple of years ago. I am now the owner of 0.5 cars.

The message of the “War on Cars” is intentionally provocative. One of the hosts even went as far as to say that no one was going to listen to a podcast called “incremental change for win-win solutions.” But there are reasons to worry about reliance on cars.

In a 2019 policy brief I wrote–one of our first as a firm–I wrote about the opportunity for the automation of cars to open the door to vehicle miles traveled fees. In this brief, I went over the many costs of another car on the road.

One is congestion. While road usage can function like a public good under most circumstances, at the time that people want to use cars the most, space on roads becomes rivalrous and car speed slows. This leads to higher commute and travel times for drivers, which is time people do not get back. Fewer cars on the roads means less congestion, which means more time for people.

A second cost of cars is crashes. Tens of thousands of Americans lose their lives to car crashes every year, and many others lose their cars or sustain injuries due to them. Fewer cars on the roads means lower chances of sustaining injury or risking death due to being in a car.

Cars also emit pollution into the atmosphere. Local emissions like particulate matter and nitrous oxide clog lungs and lead to cardiovascular disease and death. Carbon emissions hasten climate change. The fewer cars are on the road, the fewer emissions are impacting human health and the long-term sustainability of the earth.

A final impact of cars is infrastructure degradation. Every time a car drives on the road, it slowly wears down the road and degrades its quality. More degradation of quality can lead to more dangerous roads which can cause damage to cars or even lead to more crashes. If roads degrade too much, they will become inoperable. More cars on the roads means more spending on maintenance of roads. Fewer cars on the road increase their lifespan.

The socially optimal number of cars on the road is a number determined by the marginal social cost of an extra car on the road equalling the marginal social benefit of an extra car on the road. This could be different in different states since different states have different topography and economies. That being said, understanding how many households do not have cars gives us some idea of how reliant a given state is on cars and how much that state has been able to diversify its transportation system. It also could give a snapshot of deprivation. In many parts of the country, it is difficult to participate in the economy without a car. Seeing what percentage of people do not have cars gives us a view of the percentage of people who are not able to participate in the economy in a certain way.

So here it is. In the table below, you can see what percentage of households do not have a car in each of the 50 states.

Households Without a Vehicle

From this list, I have noticed a few things.

There is a large concentration of states in the northeast that have high levels of no-car households. Nearly one in three New York households do not have a car, likely due to its inclusion of the largest city in the United States, which has a strong multimodal transportation network. Massachusetts, New Jersey, Pennsylvania, Rhode Island, Maryland, and Connecticut are all within a state or two of New York and also in the top 10 states for no-car households.

Many states in the Midwest are in the top half of the list of no-car households: Illinois (3), Ohio (12), Minnesota (18), Michigan (19), Missouri (20), North Dakota (21), Wisconsin (22), and Indiana (25). A random Ohio household is less likely to own a car than a random California household.

The states with the highest levels of car ownership are Western states. Idaho, Wyoming, Utah, and Montana are the top 4 states for car ownership, all with over 95% of households owning cars. The only other state to meet that threshold is South Dakota (95.2% car ownership).

There is one strange outlier in this list: Alaska. The state of Alaska has over 9% of households without a car. This could be for a combination of reasons: spread out infrastructure that makes cars less viable as a form of transportation, higher costs of car ownership in Alaska than in the “lower 48,” or high levels of poverty in Anchorage. Whatever reason, Alaska is a strange inclusion in the top 10 least car-dependent states.

Prevalence of no-car households is a little bit of a chicken-and-egg problem. Do these places have fewer cars because there are more alternatives or do more alternatives exist because there are fewer cars? It is likely a combination of the two. The benefit they get, though, is fewer costs associated with having too many cars on the road.

Market failure: Tragedy of the commons

In this post, I am going to explain the basics behind another common market failure, the tragedy of the commons. If you would like a more broad overview of what a market failure is, I have a previous blog post on that topic. If you want more deep dives on market failures, check out my posts on externalities and natural monopolies. Additionally, I won’t be going into any of the math behind this concept. Instead, this will be a more intuitive discussion of how the tragedy of the commons works and what its impact on markets is.

What is a “common good?”

Common goods are sometimes confused with public goods because they are both non-excludable and the public sector ends up intervening in these markets. The key difference between these two is that common goods are rivalrous, meaning that one person’s consumption of the good does prevent others from consuming it as well. A common example is a lake that people commonly use for fishing. It is largely impractical to exclude people from fishing on the lake, but if too many people fish they will overfish the lake and there will be no fish left in the future. 

Why the tragedy of the commons happens

The “tragedy” in the tragedy of the commons comes from a mismatch between individual incentives and collective outcomes. Each individual user of a common good has an incentive to use as much of the resource as they reasonably can. If I am fishing in a shared lake, I benefit directly from catching one more fish. However, the cost of that extra fish (slightly reducing the fish population) is spread out across everyone who uses the lake now and in the future.

Because each person only experiences a small fraction of the total cost, it becomes rational for individuals to keep using the resource heavily, even when it harms the group as a whole. If everyone behaves this way, the resource eventually becomes depleted or degraded. In the fishing example, this could mean smaller catches over time, collapsing fish populations, or eventually a lake that can no longer support fishing at all.

Why common goods are market failures

Common goods lead to market failure because private markets alone often struggle to manage them efficiently. Since these goods are non-excludable, it is difficult to assign clear property rights or charge people directly for their usage. A competitive market would establish an equilibrium price where the consumers of the good are directly bearing the costs of consumption.

This leads to overuse, underinvestment in maintenance, and long-term damage to the resource. The total value society gets from the resource declines because there is no built-in mechanism to protect it from overuse.

How policymakers respond to the tragedy of the commons

Because common goods are vulnerable to overuse, public policy often plays an important role in managing them. One common solution is to create rules that limit how much individuals can use the resource. Fishing quotas, hunting seasons, and permits for backcountry campsites are examples of this approach. By restricting total usage, policymakers can help ensure that the resource remains available over time.

Another approach is to introduce forms of exclusion where they were previously difficult. For example, governments may issue licenses or permits that limit the number of users who can access a resource. In some cases, resources are assigned property rights, allowing individuals or groups to take responsibility for managing them. When users have a stake in the long-term health of the resource, they often have stronger incentives to conserve it. This has been a successful strategy for conservation of forest lands used for logging. There are also hybrid approaches that rely on community management. In some areas, local users collectively develop rules for sharing resources in sustainable ways. 

With thoughtful rules, incentives, and oversight, common goods can be managed in ways that balance individual use with long-term sustainability. When done well, these solutions help preserve valuable shared resources while still allowing people to benefit from them.

Original analysis: housing first vs treatment first approaches to reducing homelessness in Hawaii

This morning, Scioto Analysis published a cost-benefit analysis about the impacts of spending on homelessness programs in Hawaii. 

The analysis compares two different approaches to addressing homelessness. The “Housing-First” approach emphasizes the importance of funding permanent supportive housing above other services. An alternative “Treatment-First” approach prioritizes funding for emergency shelters and transitional housing above permanent supportive housing.

Using conservative estimates, analysts estimate that $10 million in housing-first spending would provide $54 million in net benefits. It would deliver housing to 340 residents of Hawaii, save four lives, and increase crime negligibly.

The treatment-first approach would create $15 million in net benefits, housing 114 Hawaii residents and saving one life.

Scioto Analysis estimates several differences in outcomes between these two policy programs:

• For every dollar in costs, housing-first spending delivers five dollars in benefits, while treatment-first spending delivers two dollars in benefits

• Housing-first spending will house 340 residents of Hawaii and treatment-first spending will house 114 residents

• Housing-first spending lifts 170 children out of homelessness while treatment-first spending lifts 57 children out of homelessness

• The average child lifted out of homelessness will gain $27,000 in income over their lifetime under both programs; the difference in the number of children supported by each program will create a gap of $1.1 million in benefits between the two programs

Both programs increase the social cost of crime in surrounding areas negligibly, and both programs may lead to modest reductions in property values surrounding homeless shelters.

Analysts conducted 10,000 simulations of each program with different variables and costs to test the model. They find that the benefits outweigh the costs in 99% of instances for housing-first spending and 97% of instances for treatment-first spending. For the housing-first program, 90% of simulations had net benefits between $10 million and $59 million. 90% of treatment-first simulated outcomes produced net benefits between $800,000 and $17.5 million.

This study is the latest in a series of cost-benefit analyses conducted by Scioto Analysis to demonstrate the use of cost-benefit analysis to analyze state policies. Past cost-benefit analyses and other analyses can be found here.

Market failure: Public goods

In this post, I am going to explain the basics behind another common market failure, public goods. If you would like a more broad overview of what a market failure is, I have a previous blog post on that topic. If you want more deep dives on market failures, check out my posts on externalities and natural monopolies. Additionally, I won’t be going into any of the math behind this concept. Instead, this will be a more intuitive discussion of how public goods work and what their impact on markets is.

What is a “public good?”

Public goods are defined by two key characteristics: they are non-exclusive and non-rival. In other words, it is difficult or impossible to restrict people from consuming the good and one person’s consumption of the good doesn’t impact the ability of another to consume it.
Nice weather is an example of a public good. It isn’t realistic for some company to come around and try to get me to pay before I’m allowed to go on a walk when it’s 70 degrees and sunny, nor does my walk on that day prevent anyone else from enjoying that nice weather. Even though I have a very high willingness to pay for days that are 70 degrees and sunny, it isn’t realistic for a private company to participate in the nice sunny day market.

As a more practical example, consider some public infrastructure like roads and sidewalks. These things are largely non-rivalrous,* and they are almost entirely non-excludable. Toll roads enable a small amount of exclusion, but they can almost always be avoided if you are willing to drive on backroads around them.

Why public goods are market failures

The fact that public goods are non-exclusive and non-rival means that private firms are largely unable to sell them in a traditional market. If a private firm decided they were going to try to build roads and charge people for using them, they would inevitably fall victim to the free rider problem. 

These are public goods we collectively have a willingness to pay for but no real structure to pay for. The solution to this problem is to have the public sector levy taxes to fund them. Taxes create some drag on the economy, but they are great for reducing the severity of the free rider problem since the public sector can create a wide tax base. 

Ideally, the public sector will try to provide public goods at a level that reflects the community’s overall willingness to pay. Roads, sidewalks, street lighting, and emergency services are all examples of goods that are difficult to sell individually but make communities function much more smoothly. When done well, public provision of these goods can improve productivity, safety, and overall quality of life in ways that far exceed their upfront cost.

Of course, not every good fits perfectly into the public good category. Some goods are partially rival or partially excludable, and policymakers can use hybrid approaches like user fees, special assessments, or public-private partnerships. But the underlying idea stays the same: when markets struggle to provide something valuable on their own, carefully designed public action can help fill the gap.

* Traffic and congestion make these goods slightly rivalrous, but after those conditions clear up the roads still remain for others to use.

Market failure: Natural Monopoly

In this post, I am going to explain the basics behind one of the most common market failures in economics, the natural monopoly. If you would like a more broad overview of what a market failure is, I have a previous blog post on that topic. Additionally, I won’t be going into any of the math behind this concept. Instead, this will be a more intuitive discussion of how natural monopolies work. If you would like a mathematical explanation of this topic, this is a great resource.

Regular Monopoly vs. Natural Monopoly

A natural first question when learning about natural monopolies is what makes them natural? There are two main factors that lead to this distinction.

First, with regular monopolies there is a single producer operating in a market because some factors outside the market have made it so no new suppliers can enter the market. The cost to enter the market for a new firm is too high, so only one remains. These barriers may come from outside the market, such as legal protections, control of resources, or anti-competitive behavior. A natural monopoly is also characterized by a high cost to enter the market that makes it so only one producer remains, but the difference is that in this case the high cost to enter is a factor of conditions within the market. 

For example, a bad actor might create a regular monopoly by throwing bricks through the windows of their competitors. Now, any new firm that might want to enter the market knows that the cost to enter for them also includes daily window repairs, and they will choose to not bother. The end result is that only the bad actor remains and a monopoly forms. 

Now consider the market for electricity distribution. If you wanted to independently enter the market for electricity distribution, you would need to rebuild the entire electric grid including substations and transmission lines, and you would need to connect individual buildings to your own wires just to begin selling to people. The upfront costs are so high that competing against an established provider doesn’t make sense for prospective businesses. 

The second main characteristic of a natural monopoly is that they have economies of scale. That means that after a firm manages to get established and pay the high upfront costs, it costs them very little to expand their operation and serve a wide range of customers. Consider again electricity distribution. Once a company builds power poles all across a city, it takes relatively very little to hook up one additional building. 

The end result is that unlike regular monopolies, natural monopolies result organically from market conditions. The upfront costs and the economies of scale make it so that adding a new provider would actually be less efficient overall, since firms would have to charge extremely high prices in order to cover the high fixed costs. 

Policy implications

If left to its own devices, a natural monopoly will create the same economic problems that regular monopolies do: higher than optimal prices and lower than optimal supply of the good. Unfortunately unlike regular monopolies it doesn’t just work for the government to force a natural monopoly to split up. Instead, policymakers allow natural monopolies to exist, but impose regulations on them that try to bring the market conditions more into balance with what we might hope for from a competitive market. 

In practice, this usually means regulating the prices that natural monopolies can charge and the level of service they must provide. For example, many utilities are required to justify their prices to regulators, who review the company’s costs and allow prices that cover those costs plus a reasonable profit. This helps ensure that firms can maintain infrastructure and continue operating, while also protecting customers from excessive pricing.

Another approach policymakers sometimes consider is public ownership. In some communities, essential services like water or electricity distribution are operated by government-owned utilities rather than private firms. Both regulation and public ownership aim to solve the same core problem: how to capture the efficiency of a single provider without allowing monopoly power to harm consumers.

What are the benefits of active transportation?

Recently, I was made aware of a super cool tool the Ohio Department of Transportation put together for performing cost-benefit analysis at the state and local level. I am extremely happy to see a state organization put together such a helpful reference that local governments can easily access to help inform infrastructure projects. Today, I wanted to go through some of the benefits they highlight in their tool and talk about how active forms of transportation can create broader economic value.

When we’re talking about “active transportation,” we are typically referring to modes of transportation that rely on human power rather than engines like walking and biking. In addition to being desirable amenities that people enjoy, these projects can generate meaningful economic benefits that show up across many different parts of the transportation system.

Safety benefits of active transportation

One of the main assumptions underpinning this cost-benefit analysis is that by providing active transportation infrastructure, cities can reduce the number and severity of traffic crashes.

When roads include dedicated space for cyclists and safer crossings for pedestrians, crashes with motor vehicles decline. Drivers also tend to slow down when streets are designed to accommodate multiple modes, which reduces both the likelihood and severity of crashes.

These safety improvements generate economic value because crashes are expensive. They impose costs through medical bills, lost productivity, emergency response, insurance claims, and property damage. Even relatively small reductions in crash rates can produce large economic savings when aggregated across an entire community. From a cost-benefit perspective, investments that prevent crashes often pay for themselves over time through avoided losses.

Vehicle operating costs

Another major category of benefits comes from reduced vehicle operating costs. When people substitute even a small number of car trips with walking or biking, they reduce the amount they spend on fuel, maintenance, tires, and vehicle depreciation.

These costs are often overlooked because they are spread out over time. A single avoided trip might only save a small amount of money, but over the course of a year those savings can add up. For households operating on tight budgets, reducing transportation costs can free up resources for housing, food, or other necessities.

At a broader level, fewer vehicle miles traveled also reduces wear and tear on roads. That means governments can delay expensive maintenance and reconstruction projects. While active transportation infrastructure has its own maintenance needs, those costs are generally much lower than maintaining infrastructure designed to support heavy motor vehicles.

Pollution reductions

Active transportation also reduces pollution by lowering the number of vehicle trips made using gasoline or diesel engines. This includes both traditional air pollutants such as particulate matter and nitrogen oxides, as well as greenhouse gas emissions that contribute to climate change.

The economic value of pollution reductions shows up in several ways. Improved air quality leads to fewer respiratory illnesses, fewer missed workdays, and lower healthcare costs. These benefits are particularly important in urban areas where traffic-related pollution can concentrate near major corridors.

Highway externalities

Economists use the term externalities to describe costs that drivers impose on others but do not directly pay for themselves. Highway congestion is one of the most familiar examples. When one additional car enters a crowded road, it slows down everyone else.

By shifting some trips to walking or biking, active transportation can help reduce congestion, especially for short-distance trips where alternatives to driving are most feasible. Even small reductions in traffic volumes can meaningfully improve travel times during rush hour. Other types of externalities include vehicle noise and roadway wear. Reducing vehicle dependence helps lower these indirect costs, which benefits both drivers and non-drivers.

Consumer surplus

In the context of active transportation, consumer surplus captures the value people receive from having more transportation options. Some people genuinely prefer walking or biking for short trips, but they may not currently do so because safe infrastructure is unavailable. When new infrastructure makes those trips possible, people experience benefits that go beyond direct cost savings.

For example, someone might prefer biking to work because it is faster than driving in congested traffic or because it avoids parking hassles. Even if the monetary savings are small, the convenience and time savings create real value. Cost-benefit analysis attempts to measure these gains so they can be compared to project costs.

Health benefits

Regular physical activity is associated with lower risks of chronic diseases such as heart disease, diabetes, and obesity. By integrating physical activity into daily routines, walking and biking infrastructure can help people stay active without requiring dedicated exercise time.

From an economic perspective, these health improvements translate into reduced healthcare costs, fewer missed workdays, and longer, more productive lives. These benefits can be especially large when projects make it easier for people to incorporate small amounts of physical activity into everyday trips, such as walking to transit stops or biking to nearby stores.

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Taken together, these categories illustrate how active transportation projects generate value across many different dimensions. As more communities consider investments in active transportation, tools like this one provide a useful reminder that the benefits extend far beyond recreation. When evaluated through a cost-benefit lens, active transportation is not just about mobility, it is about improving economic efficiency and quality of life across an entire community.

Ohio lawmakers move to put warning labels on social media

Earlier this month, a bipartisan group of representatives introduced a bill in the Ohio House of Representatives that would require “addictive” social media platforms to come with warning labels when they are opened.

This comes off the heels of a high-profile ruling that found Meta, the parent company of Facebook, and Google liable for creating social media sites that were intentionally addictive and that led to mental health problems for a child.

A large body of research asserts that social media can have addictive properties and has caused problem addiction among large swaths of the general population.

In a 2021 meta-analysis of 63 studies on social media addiction, an international team of researchers found as much as a quarter of people in studies across 32 countries qualified as at least moderately addicted to social media.

Warning labels are a tried and true practice in public health prevention.

As early as 1906, the federal government was holding companies accountable for not disclosing product information to consumers.

The first required warning labels were applied to regulation of poison adopted federally in the late 1920s. 

From an economic standpoint, warning labels help promote more efficient markets.

Consumers cannot make informed consumption decisions if they are not aware of the potential harms of goods.

In theory, clear labels about potential harms can protect consumers from purchasing or misusing goods and prevent both misallocation of resources and dangerous accidents.

A meta-analysis by Canadian marketing researchers of 66 studies on warning labels finds warning labels are at least somewhat effective at shifting consumer behavior.

Labels could be a tool for helping consumers control their own well-being.

Many studies have made a connection between mental health issues and social media exposure. Some researchers, however, have questioned this body of research.

A group of Norwegian researchers reviewed 79 studies on the association between social media use and mental health for adolescents.

They found that three-quarters of the studies they reviewed focused on some sort of connection between social media use and some sort of pathology.

Researchers were much more likely to ask if social media hurt than to ask if it helped.

Their takeaway: researchers are finding what they are looking for, and the literature may be more reflective of a “moral panic” perpetuated by the media than a true public health crisis.

On the other hand, some researchers are finding evidence social media use could be eating away at our economy.

A study last year by an Argentinian economist estimated that smartphone checking could be eating up as much as 13% of the Argentinian economy by cutting into productivity, with the heaviest losses coming from high-distriction jobs like office work, finance, government, and service jobs.

Personally, do I want to get a warning message shown to me every time I open TikTok on my phone? Not really.

But I understand the concern people have with social media and what it could be doing to public health and the state economy.

There may be reckoning coming soon where the public sector figures out where its place is in the regulation of social media.

I figure we still have a long way to go before we get there.

This commentary first appeared in the Ohio Capital Journal.

Market failure: Externality

In this post, I am going to explain the basics behind perhaps the single most common market failure, externalities. If you would like a more broad overview of what a market failure is, I have a previous blog post on that topic. Additionally, I won’t be going into any of the math behind this concept. Instead, this will be a more intuitive discussion of how externalities work and what their impact on markets is.

What Is an Externality?

In a typical market exchange, there are two parties: the buyer and the seller. The buyer pays a price and receives a good or service, and the seller receives payment in exchange for providing it. In theory, both parties benefit, and the costs and benefits of the transaction are fully accounted for in the price.

Externalities occur when some of the costs or benefits of this transaction spill over onto other people who were not part of the original decision. These outside effects are not reflected in the price of the good, which means the market does not fully account for the true costs or benefits of producing or consuming it.

Negative Externalities

One of the most common types of externalities is a negative externality. This happens when an activity imposes costs on others who are not part of the transaction.

Consider a steel factory. The owners of the factory sell steel to customers and earn revenue from those sales but in doing so releases pollution into a nearby river as part of its production process. The company may not pay directly for the environmental damage caused by the pollution, but people downstream may face contaminated water, harm to wildlife, or increased costs to clean their drinking water.

In this situation, the factory and its customers are part of the market transaction, but the people downstream are not. Even though they are not buying or selling steel, they still bear part of the cost. Because the factory does not fully pay for the harm it causes, the price of steel ends up being lower than the true social cost of producing it. As a result, more steel may be produced than is economically efficient.

Negative externalities are common in areas like pollution, noise, traffic congestion, and public health impacts.

Positive Externalities

Externalities are not always harmful. Sometimes an activity creates benefits for people who are not directly involved in the transaction. These are called positive externalities.

Consider education. When a person attends school or job training, they benefit by gaining skills and improving their future earning potential. However, society also benefits in ways that go beyond the individual. A more educated population can lead to higher productivity, lower crime rates, better civic participation, and faster technological progress.

In this case, the student pays tuition and invests time in education, but the broader community also gains benefits that are not reflected in the price of education. Because individuals may not consider all of these broader benefits when making decisions, fewer people may pursue education than would be socially optimal.

Other common examples of goods that produce positive externalities include vaccinations, research and development, and maintaining well-kept property in a neighborhood.

Policy Implications

The core problem with externalities is that market prices do not reflect the full costs or benefits of an activity. When negative externalities exist, goods may be overproduced because producers are not paying the full cost of their actions. When positive externalities exist, goods may be underproduced because producers or consumers do not receive the full benefit of their actions.

In both cases, the market outcome differs from what would be considered economically efficient. Resources are not allocated in a way that maximizes total benefits to society. They create a gap between private costs and social costs, but fortunately policymakers have some tools available to them in order to correct the imbalance. 

One common solution is taxes (called pigouvian taxes) on activities that create negative externalities. For example, governments may impose taxes on pollution or carbon emissions. These taxes increase the market cost of harmful activities, bringing those costs in line with the true social costs that include the externalities. Policymakers could also regulate the negative externality and set some external cap on how much is allowed to take place. Governments may set limits on pollution levels, require safety standards, or mandate certain behaviors to reduce harmful spillover effects.

When there is a positive externality, policymakers can do the inverse and subsidise those markets, artificially lowering the price in the market so that more people participate. Governments often subsidize education, vaccinations, and research because these activities generate benefits that extend beyond the individual making the decision.

Externalities are one of the most important reasons why markets sometimes fail to produce socially desirable outcomes. They highlight the fact that individual decisions can have broader impacts on society that are not captured in market prices. Understanding externalities helps explain why governments regulate pollution, subsidize education, and invest in public health.

What is the difference between stated preference and revealed preference?

Last month, I attended the Society for Benefit-Cost Analysis' annual research conference. It is the yearly gathering of the top minds in cost-benefit research from across the world, many of whom were instrumental in the development of the field over the past half century. 

This year’s conference opened with a presentation by Amitabh Chandra of the Harvard Kennedy School of Government and Harvard Business School about Medicare recipients, how they respond to gaps in their coverage, and how we can try to elicit better estimates for people's willingness to pay for additional years of life. The points the speaker brought up made me want to explain a little more how we get the estimates we use throughout cost-benefit analysis.

Stated preference vs. revealed preference

The two main ways policy analysts estimate willingness to pay for goods are stated preference studies and revealed preference studies. As their names imply, in stated preference studies participants are directly asked what they’d be willing to pay for something, while revealed preference studies try to find out how people actually react to changing prices to determine their willingness to pay for goods. 

Of the two, many researchers prefer results from revealed preference studies. This is because stated preference studies can be subject to a number of biases that are hard to control. For example, people may be influenced by social pressures if asked about their willingness to pay for some goods with stigmas attached.  For example, they might overstate their willingness to pay for cancer research or understate their willingness to pay for illegal drugs. 

Revealed preference studies are more difficult to set up, but if done well they can circumvent many of these biases that stated preference studies need to control for. Someone may say they’d only buy junk food if it was cheaper than a healthy option, but if we observe them buying it at a higher price then we can better understand their true willingness to pay. 

Challenges with estimation of willingness to pay

Neither stated preference nor revealed preference studies work when people don’t really understand the value of the thing we are interested in. One area that people tend to do a bad job understanding values is in healthcare. 

Healthcare is full of decisions about low probability, high cost events and humans are notoriously bad about thinking probabilistically. It is really hard to measure how much someone is willing to pay for something like a new drug that reduces the risk of some very rare but serious disease. 

We tend to solve this problem by relying on our estimates for the value of statistical life. People can show their willingness to trade off earnings for changes in the riskiness of their job. We might discover that for example, mortality rates are 1% higher for welders working on active construction sites vs. welders working in shops. If welders working on active sites get paid more, we can take that as an estimate for how much a welder is willing to trade the risk of death for income, from which we can determine the value of statistical life.

People don’t do their own cost-benefit analysis

The main point Chandra made was that his research found that Medicare recipients acted in ways that were not consistent with our understanding of how much people value risk of death reductions when faced with budget constraints caused by gaps in their coverage. In particular, people’s noncompliance with drug regimens implied people valuing mortality risk reduction much lower than we see in, for instance, job market wage risk premiums.

This may lead us to believe that our estimates of the value of statistical life are not always accurate or useful. Indeed, there are lots of different estimates for the value of statistical life. One argument against our current values is that because they are based on revealed preferences of people making decisions about where to work and for what salary, that they might not apply to people not in the labor force (say retired recipients of Medicaid).

The vignette approach to estimating willingness to pay for mortality risk reduction

The main method our keynote speaker used to get around this problem of our revealed preference studies not lining up with the behaviors we see people take was to go back to the drawing board and pilot a new way to calculate how much people value reductions in their risk of death. 

To do this, he used a dichotomous choice stated preference approach, which is a wordy way of saying survey respondents were given two options and had to choose the one they prefer. So instead of being asked “how much would you pay for a hot dog?” people were asked “would you prefer option A that costs $5.00 or option B that costs $9.00?” If you ask enough people to make these choices and you randomly vary the prices people see when they are asked, you can accurately determine how much people are willing to pay for certain goods. 

The big innovation our speaker made was that instead of asking very specific questions, he gave his survey respondents longer vignettes about peoples’ lives to choose between. Each vignette told the story of a person’s life, where they lived, whether they married and had children or not, and importantly what their income was and how old they were when they died. His goal was to use those two facts to determine how much people were willing to trade off money for extra years of life.

This approach has two main advantages. First, it allows the researcher to control for a large set of preferences between people. With a large enough sample size, you can extract the importance of income and years separate from the other characteristics revealed in the vignettes. Second, it makes the question easier to understand for people. There isn’t some esoteric question about how much you’d pay for an extra year of life, you just see annual incomes and the age at which someone dies. Those are much easier for people to wrap their heads around. 

In the end, this week’s keynote highlighted how difficult and how important it is to understand how people value changes in their health and longevity. Traditional revealed preference methods remain useful, but they can fall short when people face complex risks they do not fully grasp. The vignette approach offers a promising alternative by grounding abstract tradeoffs in clear and relatable life stories. As the field continues refining how we estimate willingness to pay for added years of life, innovations like this show how cost-benefit analysis evolves as we learn more about real human decision making.