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.

What are the steps of cost-benefit analysis?

“Cost-benefit analysis” is a phrase that is used in a lot of different contexts with a variety of meanings. Some people trace cost-benefit analysis in the United States as far back as Benjamin Franklin, who is said to have generated pro-con lists over a number of days to evaluate decision-making.

Cost-benefit analysis is a formalized process in the economic world. Since the New Deal era of large-scale public works, the Army Corps of Engineers has been conducting formal cost-benefit analysis to inform project selection. All major federal regulations have been subject to cost-benefit analysis for nearly half a century. How cost-benefit analysis is conducted at the federal level is the subject of Supreme Court cases. The federal government issues guidance to agencies on how to conduct cost-benefit analysis and the international Society for Benefit-Cost Analysis hosts conferences and workshops and publishes a journal on cost-benefit analysis.

To support this work, Scioto Analysis publishes the State Handbook of Cost-Benefit Analysis, a free resource for state analysts and policymakers interested in interpreting and conducting cost-benefit analysis at the state level.

But what are the steps of formal cost-benefit analysis? While different agencies have different standards for cost-benefit analysis and different contexts call for different specific approaches, the following steps separate a formal cost-benefit analysis from an informal one.

Establishing a baseline for your cost-benefit analysis

At its heart, cost-benefit analysis is a specific form of policy analysis. Policymakers have to make decisions about which policies to adopt and how to implement them. Cost-benefit analysis allows policymakers to understand how a policy works, who is impacted by the policy, and the relative share of costs and benefits of the policy borne by different members of society when it is implemented.

Because of this, having a baseline assessment of conditions is crucial for a cost-benefit analysis. In December, Scioto Analysis released a cost-benefit analysis we conducted on cigarette taxation in Ohio. Because the research we were doing relied on estimates of how much people will reduce their cigarette consumption as prices increased, the baseline number of cigarette sales was a crucial input to our model. Because cigarette consumption is on the decline in Ohio as it is throughout the country, this meant the impacts of the policy would have been smaller than if cigarette consumption was otherwise steady or on the rise.

Determining policy options for cost-benefit analysis

Next, a policy analyst needs to decide which policy options to analyze. If you are working for a policymaker, they will often tell you which options to analyze. But going deeper can be an important undertaking for a policy analyst. In a cost analysis we conducted a few years ago on climate policy in Ohio, we analyzed cap-and-trade, carbon tax, and renewable portfolio standard options for abating climate change in Ohio. In this analysis, we used cap-and-trade policies espoused in other states, renewable portfolio standards adopted by comparable states, and carbon tax levels introduced in Congress as potential policy options for Ohio.

Deciding whose costs and benefits to count

When conducting a cost-benefit analysis, an analyst needs to determine standing early, or whose costs and benefits to count. Should we count people all across the world or just in the jurisdiction the policy applies to? What about residents who are not citizens? What about people who commute into the area? These are all questions that need to be answered by a policy analyst as they conduct a cost-benefit analysis because they can have significant impacts on the outcome of the policy.

Identifying impacts in a cost-benefit analysis

Next, an analyst needs to determine which impacts to analyze in the cost-benefit analysis. This usually involves consuming literature, understanding what economists and researchers have established about what policies like this have done in this and comparable jurisdictions. This is usually a step in the process where analysts can get creative and expand their scope, seeing how many potential impacts there are of a policy as well as the research behind them. In a recent cost-benefit analysis we conducted on wildlife crossings, we analyzed impacts ranging from loss of life from wildlife collisions to the benefits of connecting ecosystems to the cost of pouring concrete. 

Quantification and Monetization in cost-benefit analysis

This is what many would consider the “heart” of cost-benefit analysis: the process of taking impacts and putting numbers on them, then converting them into dollar amounts. The goal here is to use the best available evidence to quantify the impacts of policies on key social outcomes of interest. The analyst will then use this to put dollar figure amounts on each impact based on the social costs and benefits levied by those impacts.

Discounting costs and benefits

Discounting is a key element of cost-benefit analysis. Dollars spent on programs today cannot be spent tomorrow, so there is a future social cost to investing dollars today that must be reconciled with benefits accrued later. This also can work the other way: benefits gained now can lead to costs down the road. This is what we found in our cost-benefit analysis of school closings for COVID-19.

The specific rate that benefits and costs should be discounted at is a subject of debate among scholars of cost-benefit analysis. Analysts usually consult sources of guidance such as the federal government’s Circular A-4 or the textbook by Anthony Boardman et al for answers.

Sensitivity analysis of your results

All analysis includes assumptions. Good analysis tests those assumptions and sees what the results are. Using techniques like partial sensitivity analysis, best-case/worst-case analysis, break-even analysis, and Monte Carlo simulation helps analysts understand how much their results rely on assumptions and which assumptions will impact results most if they are wrong. It can also be a good tool for understanding how likely results are to be directionally correct, like how the Washington State Institute for Public Policy presents their benefit-cost results.

Telling your story

Last, the analyst has to share her results! This could be in a report, a press release, a presentation, or anything else you could think of. But good communication is key in cost-benefit analysis. Even preliminary results or presentation of a list of impacts can be valuable to a policymaker trying to craft better policy. After all, often the process of cost-benefit analysis is more important than the results.

Grants or tax breaks: which fight poverty more efficiently?

At Scioto Analysis, we frequently analyze poverty and the strategies designed to reduce it. One of the most common tools to combat poverty in the United States is welfare spending, or spending directed at needy families to alleviate poverty. While the efficiency and effectiveness of these programs are long-standing points of debate, research from the United States Census Bureau's 2024 Poverty in the United States report shows that year-over-year, welfare programs continue to reduce the number of people in poverty. The figure below shows the top seven federal programs in 2024 in terms of the total number of fewer people in poverty.

Welfare programs in the United States generally fall into two categories: cash assistance (direct payments) and in-kind benefits (direct goods or services like food, healthcare, or housing). We can also distinguish between how welfare programs are administered. Some programs are administered by state and local governments like Medicaid, Supplemental Nutrition Assistance Program, and Temporary Assistance for Needy Families. Others are tax-administered welfare programs, such as the Child Tax Credit and Earned Income Tax Credit, managed by the Internal Revenue Service.

Many policymakers favor in-kind welfare spending because it ostensibly allows policymakers to exert more control over use of funds by needy families. They hope this means household spending goes toward goods deemed necessities by policymakers and prevents use of funds on entertainment or other goods. A drawback of in-kind spending is that governments does not know the range of different needs felt at the household level and management of household budgets at the level of government leads to inefficiencies throughout the economy.

We can see this play out in how households spend cash when they receive it. In the chart below, U.S. Census Bureau data from 2021 shows that most recipients of the Child Tax Credit, a tax-administered cash assistance program, spend money on a range of goods. If Congress changed the cash transfer child tax credit to ten in-kind spending programs, it is unlikely it would be able to predict the amount of spending needed for these families in the correct quantities to achieve an efficient allocation of resources.

So, if direct cash payments are more efficient tools than in-kind benefits to alleviate poverty, why is a program like Temporary Assistance for Needy Families, which provides direct cash assistance to needy families in the United States, plagued with issues? The main answer is an issue of access. Over the past several years, Temporary Assistance for Needy Families has seen more stringent eligibility criteria, work requirements, and time limits. The goal of these policy changes is to improve participation in the labor force and help reduce reliance on welfare programs, but the actual result is a worsening of deep poverty rates.

A compelling underlying reason I see behind the ineffectiveness of TANF is the administration of the program. More focus is placed on regulating TANF than ensuring access. In addition, discrepancies between state and local government administration of TANF, along with many other welfare programs administered by state and local governments, means that poverty alleviation is starkly different state-to-state.

Another important consideration is scale. Since Temporary Assistance for Needy Families has been block granted and capped, the program is much smaller than other programs so does not help as many families as Supplemental Nutrition Assistance Program, the Earned Income Tax Credit, and the Child Tax Credit.

Using data from the Administration for Children and Families (2024), the U.S. Department of Agriculture (2023), the Supplemental Security Income annual report (2025), the Medicaid and CHIP Payment and Access Commission (2024), and the Congressional Research Service (2018), we can compare administrative costs as a proportion of total spending across different welfare programs. The chart below shows these administrative burdens for five selected welfare programs.

The original key takeaway from these numbers is that welfare programs are generally pretty efficient, with all programs spending 10% or less of their budgets on administration. The more interesting takeaway I see is how much more efficient the Earned Income Tax Credit program is than other forms of welfare. To be specific, the only two direct cash assistance programs shown in the chart are Temporary Assistance for Needy Families and the Earned Income Tax Credit, though Temporary Assistance for Needy Families also includes funds for other programs like job training and childcare. The proportion of funding for the Earned Income Tax Credit spent on administration is one-tenth the size of TANF’s. It could be the case that cash-based welfare programs administered through the tax code, like the Earned Income Tax Credit, avoid the bloat of state and locally administered programs, while also having stronger effects on poverty reduction.

Despite these statistics, the administrative benefits of welfare programs administered through the tax code may not be perfect. For instance, if we take into account the money that households spend on filing taxes to receive these kinds of benefits, the private administrative burden of receiving welfare increases. One study found that if we assume the tax filing fee to be 17.5% instead of 0%, the administrative burden of receiving Earned Income Tax Credit benefits increases to 11%. However, many households who receive the Earned Income Tax Credit are often eligible for free tax filing services, meaning that the real administrative burden to receive these benefits is likely somewhere in the middle. 

In the 2024 tax season, the Internal Revenue Service introduced the IRS Direct File program, which ensured free tax filing services for filers with an adjusted gross income of $89,000 or less. The program has since been repealed, but it may be a worthwhile policy to keep administrative costs for programs like the Earned Income Tax Credit low while increasing accessibility for low- to moderate-income earners across the country.

One significant burden that tax-based welfare programs like the Earned Income Tax Credit can relieve is the benefits cliff. According to researchers at the Urban Institute, many earners who are near welfare cutoffs face higher marginal tax rates than some of the highest earners in the country. When accounting for taxes and reduced income from losing welfare benefits, marginal tax rates can easily reach 60% when households move toward full-time or gain a second earner. Tax-based welfare programs which reward people for working can help reduce these disparities.

Despite these advantages, there is still a significant gap in the efficiency of the welfare system in the United States. While tax-based cash assistance is a great tool for rewarding labor, the most disadvantaged households often fall through the cracks, either by not earning enough to require a tax filing or by not meeting work requirements set by welfare programs administered at the state and local levels.